From 61070a629513e05793e515cb6e56fec4ea4fd9f4 Mon Sep 17 00:00:00 2001 From: "localizeflow[bot]" Date: Fri, 30 Jan 2026 01:02:25 +0000 Subject: [PATCH] chore(i18n): sync translations with latest source changes (chunk 1/1, 334 changes) --- README.md | 2 +- .../01-defining-data-science/README.md | 4 +- .../04-stats-and-probability/README.md | 14 +- translations/br/1-Introduction/README.md | 2 +- .../2-Working-With-Data/07-python/README.md | 12 +- translations/br/2-Working-With-Data/README.md | 2 +- .../12-visualization-relationships/README.md | 14 +- .../R/09-visualization-quantities/README.md | 16 +- .../10-visualization-distributions/README.md | 20 +- .../R/11-visualization-proportions/README.md | 6 +- .../12-visualization-relationships/README.md | 14 +- .../R/13-meaningful-vizualizations/README.md | 16 +- .../br/3-Data-Visualization/README.md | 2 +- .../14-Introduction/README.md | 4 +- .../br/4-Data-Science-Lifecycle/README.md | 2 +- .../br/5-Data-Science-In-Cloud/README.md | 4 +- 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translations/es/.co-op-translator.json create mode 100644 translations/fr/.co-op-translator.json diff --git a/README.md b/README.md index bd7ad395..dc04661b 100644 --- a/README.md +++ b/README.md @@ -33,7 +33,7 @@ Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson cur #### Supported via GitHub Action (Automated & Always Up-to-Date) -[Arabic](./translations/ar/README.md) | [Bengali](./translations/bn/README.md) | [Bulgarian](./translations/bg/README.md) | [Burmese (Myanmar)](./translations/my/README.md) | [Chinese (Simplified)](./translations/zh/README.md) | [Chinese (Traditional, Hong Kong)](./translations/hk/README.md) | [Chinese (Traditional, Macau)](./translations/mo/README.md) | [Chinese (Traditional, Taiwan)](./translations/tw/README.md) | [Croatian](./translations/hr/README.md) | [Czech](./translations/cs/README.md) | [Danish](./translations/da/README.md) | [Dutch](./translations/nl/README.md) | [Estonian](./translations/et/README.md) | [Finnish](./translations/fi/README.md) | [French](./translations/fr/README.md) | [German](./translations/de/README.md) | [Greek](./translations/el/README.md) | [Hebrew](./translations/he/README.md) | [Hindi](./translations/hi/README.md) | [Hungarian](./translations/hu/README.md) | [Indonesian](./translations/id/README.md) | [Italian](./translations/it/README.md) | [Japanese](./translations/ja/README.md) | [Kannada](./translations/kn/README.md) | [Korean](./translations/ko/README.md) | [Lithuanian](./translations/lt/README.md) | [Malay](./translations/ms/README.md) | [Malayalam](./translations/ml/README.md) | [Marathi](./translations/mr/README.md) | [Nepali](./translations/ne/README.md) | [Nigerian Pidgin](./translations/pcm/README.md) | [Norwegian](./translations/no/README.md) | [Persian (Farsi)](./translations/fa/README.md) | [Polish](./translations/pl/README.md) | [Portuguese (Brazil)](./translations/br/README.md) | [Portuguese (Portugal)](./translations/pt/README.md) | [Punjabi (Gurmukhi)](./translations/pa/README.md) | [Romanian](./translations/ro/README.md) | [Russian](./translations/ru/README.md) | [Serbian (Cyrillic)](./translations/sr/README.md) | [Slovak](./translations/sk/README.md) | [Slovenian](./translations/sl/README.md) | [Spanish](./translations/es/README.md) | [Swahili](./translations/sw/README.md) | [Swedish](./translations/sv/README.md) | [Tagalog (Filipino)](./translations/tl/README.md) | [Tamil](./translations/ta/README.md) | [Telugu](./translations/te/README.md) | [Thai](./translations/th/README.md) | [Turkish](./translations/tr/README.md) | [Ukrainian](./translations/uk/README.md) | [Urdu](./translations/ur/README.md) | [Vietnamese](./translations/vi/README.md) +[Arabic](./translations/ar/README.md) | [Bengali](./translations/bn/README.md) | [Bulgarian](./translations/bg/README.md) | [Burmese (Myanmar)](./translations/my/README.md) | [Chinese (Simplified)](./translations/zh-CN/README.md) | [Chinese (Traditional, Hong Kong)](./translations/zh-HK/README.md) | [Chinese (Traditional, Macau)](./translations/zh-MO/README.md) | [Chinese (Traditional, Taiwan)](./translations/zh-TW/README.md) | [Croatian](./translations/hr/README.md) | [Czech](./translations/cs/README.md) | [Danish](./translations/da/README.md) | [Dutch](./translations/nl/README.md) | [Estonian](./translations/et/README.md) | [Finnish](./translations/fi/README.md) | [French](./translations/fr/README.md) | [German](./translations/de/README.md) | [Greek](./translations/el/README.md) | [Hebrew](./translations/he/README.md) | [Hindi](./translations/hi/README.md) | [Hungarian](./translations/hu/README.md) | [Indonesian](./translations/id/README.md) | [Italian](./translations/it/README.md) | [Japanese](./translations/ja/README.md) | [Kannada](./translations/kn/README.md) | [Korean](./translations/ko/README.md) | [Lithuanian](./translations/lt/README.md) | [Malay](./translations/ms/README.md) | [Malayalam](./translations/ml/README.md) | [Marathi](./translations/mr/README.md) | [Nepali](./translations/ne/README.md) | [Nigerian Pidgin](./translations/pcm/README.md) | [Norwegian](./translations/no/README.md) | [Persian (Farsi)](./translations/fa/README.md) | [Polish](./translations/pl/README.md) | [Portuguese (Brazil)](./translations/pt-BR/README.md) | [Portuguese (Portugal)](./translations/pt-PT/README.md) | [Punjabi (Gurmukhi)](./translations/pa/README.md) | [Romanian](./translations/ro/README.md) | [Russian](./translations/ru/README.md) | [Serbian (Cyrillic)](./translations/sr/README.md) | [Slovak](./translations/sk/README.md) | [Slovenian](./translations/sl/README.md) | [Spanish](./translations/es/README.md) | [Swahili](./translations/sw/README.md) | [Swedish](./translations/sv/README.md) | [Tagalog (Filipino)](./translations/tl/README.md) | [Tamil](./translations/ta/README.md) | [Telugu](./translations/te/README.md) | [Thai](./translations/th/README.md) | [Turkish](./translations/tr/README.md) | [Ukrainian](./translations/uk/README.md) | [Urdu](./translations/ur/README.md) | [Vietnamese](./translations/vi/README.md) > **Prefer to Clone Locally?** diff --git a/translations/br/1-Introduction/01-defining-data-science/README.md b/translations/br/1-Introduction/01-defining-data-science/README.md index 8a56ceb2..189bef0d 100644 --- a/translations/br/1-Introduction/01-defining-data-science/README.md +++ b/translations/br/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Vídeo Definindo Ciência de Dados](../../../../translated_images/br/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) +[![Vídeo Definindo Ciência de Dados](../../../../translated_images/pt-BR/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Quiz pré-aula](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ Se quisermos ser ainda mais detalhados, podemos traçar o tempo gasto em cada m Neste desafio, tentaremos encontrar conceitos relevantes para o campo de Ciência de Dados analisando textos. Vamos pegar um artigo da Wikipedia sobre Ciência de Dados, baixar e processar o texto e, em seguida, construir uma nuvem de palavras como esta: -![Nuvem de Palavras para Ciência de Dados](../../../../translated_images/br/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) +![Nuvem de Palavras para Ciência de Dados](../../../../translated_images/pt-BR/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) Visite [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') para ler o código. Você também pode executar o código e ver como ele realiza todas as transformações de dados em tempo real. diff --git a/translations/br/1-Introduction/04-stats-and-probability/README.md b/translations/br/1-Introduction/04-stats-and-probability/README.md index cfc22ce7..2f5386b4 100644 --- a/translations/br/1-Introduction/04-stats-and-probability/README.md +++ b/translations/br/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: A Teoria da Estatística e Probabilidade são duas áreas altamente relacionadas da Matemática que são extremamente relevantes para a Ciência de Dados. É possível trabalhar com dados sem um conhecimento profundo de matemática, mas ainda assim é melhor conhecer pelo menos alguns conceitos básicos. Aqui apresentaremos uma breve introdução que ajudará você a começar. -[![Vídeo de Introdução](../../../../translated_images/br/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Vídeo de Introdução](../../../../translated_images/pt-BR/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Quiz pré-aula](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ A distribuição discreta mais conhecida é a **distribuição uniforme**, na qu Só podemos falar sobre a probabilidade de uma variável estar em um determinado intervalo de valores, por exemplo, P(t1≤X2). Nesse caso, a distribuição de probabilidade é descrita por uma **função densidade de probabilidade** p(x), tal que -![P(t_1\le X Mais exemplos de correlação e covariância podem ser encontrados no [notebook complementar](notebook.ipynb). diff --git a/translations/br/1-Introduction/README.md b/translations/br/1-Introduction/README.md index 49851040..50f1f747 100644 --- a/translations/br/1-Introduction/README.md +++ b/translations/br/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introdução à Ciência de Dados -![dados em ação](../../../translated_images/br/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) +![dados em ação](../../../translated_images/pt-BR/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > Foto por Stephen Dawson no Unsplash Nestes módulos, você descobrirá como a Ciência de Dados é definida e aprenderá sobre as considerações éticas que devem ser levadas em conta por um cientista de dados. Você também aprenderá como os dados são definidos e terá uma introdução a estatística e probabilidade, os principais domínios acadêmicos da Ciência de Dados. diff --git a/translations/br/2-Working-With-Data/07-python/README.md b/translations/br/2-Working-With-Data/07-python/README.md index 6eab0009..e584e99f 100644 --- a/translations/br/2-Working-With-Data/07-python/README.md +++ b/translations/br/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Trabalhando com Python - _Sketchnote por [@nitya](https://twitter.com/nitya)_ | -[![Vídeo de Introdução](../../../../translated_images/br/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) +[![Vídeo de Introdução](../../../../translated_images/pt-BR/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) Embora bancos de dados ofereçam maneiras muito eficientes de armazenar dados e consultá-los usando linguagens de consulta, a forma mais flexível de processar dados é escrever seu próprio programa para manipulá-los. Em muitos casos, realizar uma consulta em um banco de dados seria uma maneira mais eficaz. No entanto, em alguns casos, quando é necessário um processamento de dados mais complexo, isso não pode ser feito facilmente usando SQL. O processamento de dados pode ser programado em qualquer linguagem de programação, mas existem certas linguagens que são mais adequadas para trabalhar com dados. Cientistas de dados geralmente preferem uma das seguintes linguagens: @@ -73,7 +73,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Gráfico de Série Temporal](../../../../translated_images/br/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) +![Gráfico de Série Temporal](../../../../translated_images/pt-BR/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) Agora suponha que, a cada semana, organizamos uma festa para amigos e levamos 10 pacotes adicionais de sorvete para a festa. Podemos criar outra série, indexada por semana, para demonstrar isso: ```python @@ -84,7 +84,7 @@ Quando somamos duas séries, obtemos o número total: total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Gráfico de Série Temporal](../../../../translated_images/br/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) +![Gráfico de Série Temporal](../../../../translated_images/pt-BR/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Nota** que não estamos usando a sintaxe simples `total_items+additional_items`. Se fizéssemos isso, receberíamos muitos valores `NaN` (*Not a Number*) na série resultante. Isso ocorre porque há valores ausentes para alguns pontos do índice na série `additional_items`, e somar `NaN` a qualquer coisa resulta em `NaN`. Assim, precisamos especificar o parâmetro `fill_value` durante a soma. @@ -93,7 +93,7 @@ Com séries temporais, também podemos **re-amostrar** a série com diferentes i monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Médias Mensais de Série Temporal](../../../../translated_images/br/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) +![Médias Mensais de Série Temporal](../../../../translated_images/pt-BR/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -219,7 +219,7 @@ O primeiro problema em que vamos focar é o modelamento da propagação epidêmi Como queremos demonstrar como lidar com dados, convidamos você a abrir [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) e lê-lo de cima a baixo. Você também pode executar as células e realizar alguns desafios que deixamos para você no final. -![Propagação da COVID](../../../../translated_images/br/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) +![Propagação da COVID](../../../../translated_images/pt-BR/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Se você não sabe como executar código no Jupyter Notebook, confira [este artigo](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). @@ -241,7 +241,7 @@ Um exemplo completo de análise deste conjunto de dados usando o serviço cognit Abra [`notebook-papers.ipynb`](notebook-papers.ipynb) e leia-o de cima a baixo. Você também pode executar as células e realizar alguns desafios que deixamos para você no final. -![Tratamento Médico para COVID](../../../../translated_images/br/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) +![Tratamento Médico para COVID](../../../../translated_images/pt-BR/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Processando Dados de Imagem diff --git a/translations/br/2-Working-With-Data/README.md b/translations/br/2-Working-With-Data/README.md index e1dff327..8f09c95f 100644 --- a/translations/br/2-Working-With-Data/README.md +++ b/translations/br/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Trabalhando com Dados -![amor por dados](../../../translated_images/br/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) +![amor por dados](../../../translated_images/pt-BR/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > Foto por Alexander Sinn no Unsplash Nestas lições, você aprenderá algumas das maneiras de gerenciar, manipular e usar dados em aplicações. Você aprenderá sobre bancos de dados relacionais e não relacionais e como os dados podem ser armazenados neles. Aprenderá os fundamentos de trabalhar com Python para gerenciar dados e descobrirá algumas das muitas formas de usar Python para gerenciar e explorar dados. diff --git a/translations/br/3-Data-Visualization/12-visualization-relationships/README.md b/translations/br/3-Data-Visualization/12-visualization-relationships/README.md index 4df4abd3..2ea5616c 100644 --- a/translations/br/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/br/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ Crie um gráfico de dispersão básico para mostrar a relação entre o preço p ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/br/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) +![scatterplot 1](../../../../translated_images/pt-BR/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) Agora, mostre os mesmos dados com um esquema de cores de mel para mostrar como o preço evolui ao longo dos anos. Você pode fazer isso adicionando um parâmetro 'hue' para mostrar a mudança ano após ano: @@ -60,7 +60,7 @@ Agora, mostre os mesmos dados com um esquema de cores de mel para mostrar como o ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/br/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) +![scatterplot 2](../../../../translated_images/pt-BR/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) Com essa mudança de esquema de cores, você pode ver claramente uma forte progressão ao longo dos anos em termos de preço do mel por libra. De fato, se você observar um conjunto de amostra nos dados para verificar (escolha um estado, como o Arizona, por exemplo), pode ver um padrão de aumento de preço ano após ano, com poucas exceções: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` Você pode ver o tamanho dos pontos aumentando gradualmente. -![scatterplot 3](../../../../translated_images/br/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) +![scatterplot 3](../../../../translated_images/pt-BR/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) Isso é um caso simples de oferta e demanda? Devido a fatores como mudanças climáticas e colapso das colônias, há menos mel disponível para compra ano após ano, e, portanto, o preço aumenta? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` Resposta: Sim, com algumas exceções em torno do ano de 2003: -![line chart 1](../../../../translated_images/br/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) +![line chart 1](../../../../translated_images/pt-BR/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Como o Seaborn está agregando dados em torno de uma linha, ele exibe "as múltiplas medições em cada valor de x, plotando a média e o intervalo de confiança de 95% em torno da média". [Fonte](https://seaborn.pydata.org/tutorial/relational.html). Esse comportamento demorado pode ser desativado adicionando `ci=None`. @@ -114,7 +114,7 @@ Pergunta: Bem, em 2003 também podemos ver um pico na oferta de mel? E se você sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/br/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) +![line chart 2](../../../../translated_images/pt-BR/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) Resposta: Não exatamente. Se você observar a produção total, parece que ela realmente aumentou naquele ano específico, embora, de forma geral, a quantidade de mel sendo produzida esteja em declínio durante esses anos. @@ -139,7 +139,7 @@ sns.relplot( ``` Nesta visualização, você pode comparar o rendimento por colônia e o número de colônias ano após ano, lado a lado, com um wrap definido em 3 para as colunas: -![facet grid](../../../../translated_images/br/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) +![facet grid](../../../../translated_images/pt-BR/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) Para este conjunto de dados, nada particularmente se destaca em relação ao número de colônias e seu rendimento, ano após ano e estado por estado. Existe uma maneira diferente de encontrar uma correlação entre essas duas variáveis? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/br/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) +![superimposed plots](../../../../translated_images/pt-BR/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) Embora nada salte aos olhos em torno do ano de 2003, isso nos permite terminar esta lição com uma nota um pouco mais feliz: embora o número de colônias esteja em declínio geral, ele está se estabilizando, mesmo que o rendimento por colônia esteja diminuindo. diff --git a/translations/br/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/br/3-Data-Visualization/R/09-visualization-quantities/README.md index b2a97f17..5357f710 100644 --- a/translations/br/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/br/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Aqui, você instala o pacote `ggplot2` e o importa para o ambiente de trabalho usando o comando `library("ggplot2")`. Para plotar qualquer gráfico no ggplot, a função `ggplot()` é usada, e você especifica o conjunto de dados, as variáveis x e y como atributos. Neste caso, usamos a função `geom_line()` porque queremos plotar um gráfico de linha. -![MaxWingspan-lineplot](../../../../../translated_images/br/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) +![MaxWingspan-lineplot](../../../../../translated_images/pt-BR/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) O que você percebe imediatamente? Parece haver pelo menos um outlier - que envergadura impressionante! Uma envergadura de mais de 2000 centímetros equivale a mais de 20 metros - será que há Pterodáctilos em Minnesota? Vamos investigar. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Especificamos o ângulo no `theme` e definimos os rótulos dos eixos x e y em `xlab()` e `ylab()`, respectivamente. O `ggtitle()` dá um nome ao gráfico. -![MaxWingspan-lineplot-improved](../../../../../translated_images/br/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/pt-BR/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) Mesmo com a rotação dos rótulos ajustada para 45 graus, ainda há muitos para ler. Vamos tentar uma estratégia diferente: rotular apenas os outliers e definir os rótulos dentro do gráfico. Você pode usar um gráfico de dispersão para criar mais espaço para os rótulos: @@ -100,7 +100,7 @@ O que está acontecendo aqui? Você usou a função `geom_point()` para plotar p O que você descobre? -![MaxWingspan-scatterplot](../../../../../translated_images/br/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) +![MaxWingspan-scatterplot](../../../../../translated_images/pt-BR/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## Filtre seus dados @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` Criamos um novo dataframe `birds_filtered` e, em seguida, plotamos um gráfico de dispersão. Ao filtrar os outliers, seus dados agora estão mais coesos e compreensíveis. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/br/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/pt-BR/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) Agora que temos um conjunto de dados mais limpo, pelo menos em termos de envergadura, vamos descobrir mais sobre esses pássaros. @@ -161,7 +161,7 @@ birds_filtered %>% group_by(Category) %>% ``` No trecho a seguir, instalamos os pacotes [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) e [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) para ajudar a manipular e agrupar dados a fim de plotar um gráfico de barras empilhadas. Primeiro, agrupamos os dados pela `Categoria` do pássaro e, em seguida, resumimos as colunas `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan`. Depois, plotamos o gráfico de barras usando o pacote `ggplot2`, especificando as cores para as diferentes categorias e os rótulos. -![Stacked bar chart](../../../../../translated_images/br/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) +![Stacked bar chart](../../../../../translated_images/pt-BR/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) Este gráfico de barras, no entanto, é ilegível porque há muitos dados não agrupados. Você precisa selecionar apenas os dados que deseja plotar, então vamos observar o comprimento dos pássaros com base em sua categoria. @@ -176,7 +176,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` Primeiro, contamos os valores únicos na coluna `Categoria` e, em seguida, os classificamos em um novo dataframe `birds_count`. Esses dados classificados são então organizados no mesmo nível para que sejam plotados de forma ordenada. Usando o `ggplot2`, você então plota os dados em um gráfico de barras. O `coord_flip()` plota barras horizontais. -![category-length](../../../../../translated_images/br/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) +![category-length](../../../../../translated_images/pt-BR/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) Este gráfico de barras mostra uma boa visão do número de pássaros em cada categoria. Em um piscar de olhos, você vê que o maior número de pássaros nesta região está na categoria Patos/Gansos/AvesAquáticas. Minnesota é a "terra dos 10.000 lagos", então isso não é surpreendente! @@ -199,7 +199,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` Agrupamos os dados `birds_filtered` por `Categoria` e, em seguida, plotamos um gráfico de barras. -![comparing data](../../../../../translated_images/br/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) +![comparing data](../../../../../translated_images/pt-BR/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) Nada surpreendente aqui: beija-flores têm o menor ComprimentoMáximo em comparação com Pelicanos ou Gansos. É bom quando os dados fazem sentido lógico! @@ -211,7 +211,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/br/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) +![super-imposed values](../../../../../translated_images/pt-BR/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 Desafio diff --git a/translations/br/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/br/3-Data-Visualization/R/10-visualization-distributions/README.md index 1cfddf12..e4e3292d 100644 --- a/translations/br/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/br/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![comprimento máximo por ordem](../../../../../translated_images/br/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) +![comprimento máximo por ordem](../../../../../translated_images/pt-BR/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) Isso fornece uma visão geral da distribuição do comprimento corporal por ordem de pássaros, mas não é a maneira ideal de exibir distribuições reais. Essa tarefa geralmente é realizada criando um histograma. @@ -57,7 +57,7 @@ O `ggplot2` oferece ótimas maneiras de visualizar a distribuição de dados usa ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![distribuição em todo o conjunto de dados](../../../../../translated_images/br/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) +![distribuição em todo o conjunto de dados](../../../../../translated_images/pt-BR/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) Como você pode ver, a maioria dos 400+ pássaros neste conjunto de dados está na faixa de menos de 2000 para sua massa corporal máxima. Obtenha mais informações sobre os dados alterando o parâmetro `bins` para um número maior, algo como 30: @@ -65,7 +65,7 @@ Como você pode ver, a maioria dos 400+ pássaros neste conjunto de dados está ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![distribuição-30bins](../../../../../translated_images/br/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) +![distribuição-30bins](../../../../../translated_images/pt-BR/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) Este gráfico mostra a distribuição de forma um pouco mais detalhada. Um gráfico menos inclinado para a esquerda poderia ser criado garantindo que você selecione apenas dados dentro de um determinado intervalo: @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![histograma filtrado](../../../../../translated_images/br/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) +![histograma filtrado](../../../../../translated_images/pt-BR/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ Experimente outros filtros e pontos de dados. Para ver a distribuição completa dos dados, remova o filtro `['MaxBodyMass']` para mostrar distribuições rotuladas. @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` Parece haver uma correlação esperada entre esses dois elementos ao longo de um eixo esperado, com um ponto de convergência particularmente forte: -![gráfico 2d](../../../../../translated_images/br/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) +![gráfico 2d](../../../../../translated_images/pt-BR/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Os histogramas funcionam bem por padrão para dados numéricos. E se você precisar ver distribuições de acordo com dados textuais? ## Explore o conjunto de dados para distribuições usando dados textuais @@ -122,7 +122,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![envergadura e status de conservação](../../../../../translated_images/br/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) +![envergadura e status de conservação](../../../../../translated_images/pt-BR/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) Não parece haver uma boa correlação entre envergadura mínima e status de conservação. Teste outros elementos do conjunto de dados usando este método. Você encontra alguma correlação? @@ -136,7 +136,7 @@ Vamos trabalhar com gráficos de densidade agora! ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![gráfico de densidade](../../../../../translated_images/br/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) +![gráfico de densidade](../../../../../translated_images/pt-BR/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) Você pode ver como o gráfico reflete o anterior para os dados de envergadura mínima; é apenas um pouco mais suave. Se você quisesse revisitar aquela linha irregular de MaxBodyMass no segundo gráfico que construiu, poderia suavizá-la muito bem recriando-a usando este método: @@ -144,7 +144,7 @@ Você pode ver como o gráfico reflete o anterior para os dados de envergadura m ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![densidade de massa corporal](../../../../../translated_images/br/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) +![densidade de massa corporal](../../../../../translated_images/pt-BR/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) Se você quisesse uma linha suave, mas não muito suave, edite o parâmetro `adjust`: @@ -152,7 +152,7 @@ Se você quisesse uma linha suave, mas não muito suave, edite o parâmetro `adj ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![massa corporal menos suave](../../../../../translated_images/br/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) +![massa corporal menos suave](../../../../../translated_images/pt-BR/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ Leia sobre os parâmetros disponíveis para este tipo de gráfico e experimente! @@ -162,7 +162,7 @@ Este tipo de gráfico oferece visualizações explicativas muito bonitas. Com al ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![massa corporal por ordem](../../../../../translated_images/br/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) +![massa corporal por ordem](../../../../../translated_images/pt-BR/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 Desafio diff --git a/translations/br/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/br/3-Data-Visualization/R/11-visualization-proportions/README.md index 6ca7b72e..a805b048 100644 --- a/translations/br/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/br/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -92,7 +92,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` Voilá, um gráfico de pizza mostrando as proporções desses dados de acordo com essas duas classes de cogumelos. É muito importante obter a ordem correta dos rótulos, especialmente aqui, então certifique-se de verificar a ordem com a qual o array de rótulos foi construído! -![gráfico de pizza](../../../../../translated_images/br/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) +![gráfico de pizza](../../../../../translated_images/pt-BR/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## Roscas! @@ -126,7 +126,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![gráfico de rosca](../../../../../translated_images/br/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) +![gráfico de rosca](../../../../../translated_images/pt-BR/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) Este código usa duas bibliotecas - ggplot2 e webr. Usando a função PieDonut da biblioteca webr, podemos criar um gráfico de rosca facilmente! @@ -164,7 +164,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual Usando um gráfico de waffle, você pode ver claramente as proporções das cores dos chapéus neste conjunto de dados de cogumelos. Curiosamente, há muitos cogumelos com chapéus verdes! -![gráfico de waffle](../../../../../translated_images/br/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) +![gráfico de waffle](../../../../../translated_images/pt-BR/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) Nesta lição, você aprendeu três maneiras de visualizar proporções. Primeiro, você precisa agrupar seus dados em categorias e, em seguida, decidir qual é a melhor maneira de exibir os dados - pizza, rosca ou waffle. Todas são deliciosas e proporcionam ao usuário uma visão instantânea de um conjunto de dados. diff --git a/translations/br/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/br/3-Data-Visualization/R/12-visualization-relationships/README.md index 3da07c7c..abcc8c93 100644 --- a/translations/br/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/br/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/br/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) +![scatterplot 1](../../../../../translated_images/pt-BR/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) Agora, mostre os mesmos dados com um esquema de cores de mel para ilustrar como o preço evolui ao longo dos anos. Você pode fazer isso adicionando um parâmetro 'scale_color_gradientn' para mostrar a mudança, ano após ano: @@ -61,7 +61,7 @@ Agora, mostre os mesmos dados com um esquema de cores de mel para ilustrar como ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/br/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) +![scatterplot 2](../../../../../translated_images/pt-BR/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) Com essa mudança no esquema de cores, você pode ver claramente uma forte progressão ao longo dos anos no preço do mel por libra. De fato, se você observar um conjunto de amostra nos dados para verificar (escolha um estado, como o Arizona, por exemplo), verá um padrão de aumento de preços ano após ano, com poucas exceções: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` Você pode ver o tamanho dos pontos aumentando gradualmente. -![scatterplot 3](../../../../../translated_images/br/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) +![scatterplot 3](../../../../../translated_images/pt-BR/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) Isso é um caso simples de oferta e demanda? Devido a fatores como mudanças climáticas e colapso das colônias, há menos mel disponível para compra ano após ano, e, portanto, o preço aumenta? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` Resposta: Sim, com algumas exceções por volta do ano de 2003: -![line chart 1](../../../../../translated_images/br/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) +![line chart 1](../../../../../translated_images/pt-BR/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) Pergunta: Bem, em 2003 também podemos ver um aumento na oferta de mel? E se você observar a produção total ano após ano? @@ -115,7 +115,7 @@ Pergunta: Bem, em 2003 também podemos ver um aumento na oferta de mel? E se voc qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/br/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) +![line chart 2](../../../../../translated_images/pt-BR/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) Resposta: Não exatamente. Se você observar a produção total, parece que ela realmente aumentou naquele ano específico, embora, de forma geral, a quantidade de mel produzida esteja em declínio durante esses anos. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` Nesta visualização, você pode comparar o rendimento por colônia e o número de colônias ano após ano, lado a lado, com um wrap configurado para 3 colunas: -![facet grid](../../../../../translated_images/br/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) +![facet grid](../../../../../translated_images/pt-BR/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) Para este conjunto de dados, nada particularmente se destaca em relação ao número de colônias e seu rendimento, ano após ano e estado por estado. Existe uma maneira diferente de encontrar uma correlação entre essas duas variáveis? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/br/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) +![superimposed plots](../../../../../translated_images/pt-BR/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) Embora nada salte aos olhos em torno do ano de 2003, isso nos permite terminar esta lição com uma nota um pouco mais feliz: embora o número de colônias esteja em declínio geral, ele está se estabilizando, mesmo que o rendimento por colônia esteja diminuindo. diff --git a/translations/br/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/br/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 938b3c9d..b920da8d 100644 --- a/translations/br/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/br/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ Em lições anteriores, você experimentou criar vários tipos interessantes de Mesmo que um cientista de dados seja cuidadoso ao escolher o gráfico certo para os dados certos, há muitas maneiras de exibir dados de forma a provar um ponto, muitas vezes às custas de comprometer os próprios dados. Existem muitos exemplos de gráficos e infográficos enganosos! -[![Como os Gráficos Mentem por Alberto Cairo](../../../../../translated_images/br/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Como os gráficos mentem") +[![Como os Gráficos Mentem por Alberto Cairo](../../../../../translated_images/pt-BR/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Como os gráficos mentem") > 🎥 Clique na imagem acima para assistir a uma palestra sobre gráficos enganosos Este gráfico inverte o eixo X para mostrar o oposto da verdade, com base na data: -![gráfico ruim 1](../../../../../translated_images/br/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) +![gráfico ruim 1](../../../../../translated_images/pt-BR/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [Este gráfico](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) é ainda mais enganoso, pois o olhar é atraído para a direita, levando à conclusão de que, ao longo do tempo, os casos de COVID diminuíram nos vários condados. Na verdade, se você olhar atentamente para as datas, verá que elas foram reorganizadas para criar essa tendência enganosa de queda. -![gráfico ruim 2](../../../../../translated_images/br/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) +![gráfico ruim 2](../../../../../translated_images/pt-BR/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) Este exemplo notório usa cor E um eixo Y invertido para enganar: em vez de concluir que as mortes por armas aumentaram após a aprovação de uma legislação favorável às armas, o olhar é enganado para pensar que o oposto é verdadeiro: -![gráfico ruim 3](../../../../../translated_images/br/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) +![gráfico ruim 3](../../../../../translated_images/pt-BR/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) Este gráfico estranho mostra como a proporção pode ser manipulada, de forma hilária: -![gráfico ruim 4](../../../../../translated_images/br/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) +![gráfico ruim 4](../../../../../translated_images/pt-BR/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) Comparar o incomparável é mais um truque duvidoso. Existe um [site maravilhoso](https://tylervigen.com/spurious-correlations) dedicado a 'correlações espúrias', exibindo 'fatos' que correlacionam coisas como a taxa de divórcio no Maine e o consumo de margarina. Um grupo no Reddit também coleta os [usos feios](https://www.reddit.com/r/dataisugly/top/?t=all) de dados. @@ -100,13 +100,13 @@ Rotule seus eixos, forneça uma legenda, se necessário, e ofereça tooltips par Se seus dados forem textuais e extensos no eixo X, você pode inclinar o texto para melhorar a legibilidade. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) oferece gráficos em 3D, se seus dados suportarem. Visualizações de dados sofisticadas podem ser produzidas usando essa ferramenta. -![gráficos 3D](../../../../../translated_images/br/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) +![gráficos 3D](../../../../../translated_images/pt-BR/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## Exibição de gráficos animados e em 3D Algumas das melhores visualizações de dados hoje em dia são animadas. Shirley Wu tem exemplos incríveis feitos com D3, como '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', onde cada flor é uma visualização de um filme. Outro exemplo para o Guardian é 'bussed out', uma experiência interativa que combina visualizações com Greensock e D3, além de um formato de artigo com narrativa para mostrar como NYC lida com seu problema de moradores de rua, enviando pessoas para fora da cidade. -![busing](../../../../../translated_images/br/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) +![busing](../../../../../translated_images/pt-BR/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: Como os EUA Movem seus Moradores de Rua" do [Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Visualizações por Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ Embora esta lição não seja suficiente para ensinar essas poderosas biblioteca Você completará um aplicativo web que exibirá uma visão animada dessa rede social. Ele usa uma biblioteca criada para gerar uma [visualização de uma rede](https://github.com/emiliorizzo/vue-d3-network) usando Vue.js e D3. Quando o aplicativo estiver em execução, você poderá mover os nós na tela para reorganizar os dados. -![liaisons](../../../../../translated_images/br/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) +![liaisons](../../../../../translated_images/pt-BR/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Projeto: Crie um gráfico para mostrar uma rede usando D3.js diff --git a/translations/br/3-Data-Visualization/README.md b/translations/br/3-Data-Visualization/README.md index 1e9e2dbd..33a9c231 100644 --- a/translations/br/3-Data-Visualization/README.md +++ b/translations/br/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Visualizações -![uma abelha em uma flor de lavanda](../../../translated_images/br/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) +![uma abelha em uma flor de lavanda](../../../translated_images/pt-BR/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > Foto por Jenna Lee no Unsplash Visualizar dados é uma das tarefas mais importantes de um cientista de dados. Imagens valem mais que mil palavras, e uma visualização pode ajudar você a identificar diversos aspectos interessantes dos seus dados, como picos, valores atípicos, agrupamentos, tendências e muito mais, que podem ajudar a entender a história que seus dados estão tentando contar. diff --git a/translations/br/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/br/4-Data-Science-Lifecycle/14-Introduction/README.md index f0a36625..6adfa469 100644 --- a/translations/br/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/br/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ Neste ponto, você provavelmente já percebeu que a ciência de dados é um proc Esta lição foca em 3 partes do ciclo de vida: captura, processamento e manutenção. -![Diagrama do ciclo de vida da ciência de dados](../../../../translated_images/br/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) +![Diagrama do ciclo de vida da ciência de dados](../../../../translated_images/pt-BR/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > Foto por [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## Captura @@ -101,7 +101,7 @@ Explore o [Ciclo de Vida do Processo de Ciência de Dados em Equipe](https://doc |Processo de Ciência de Dados em Equipe (TDSP)|Processo padrão da indústria para mineração de dados (CRISP-DM)| |--|--| -|![Ciclo de Vida do Processo de Ciência de Dados em Equipe](../../../../translated_images/br/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Imagem do Processo de Ciência de Dados](../../../../translated_images/br/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | +|![Ciclo de Vida do Processo de Ciência de Dados em Equipe](../../../../translated_images/pt-BR/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Imagem do Processo de Ciência de Dados](../../../../translated_images/pt-BR/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | Imagem por [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Imagem por [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [Quiz Pós-Aula](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/br/4-Data-Science-Lifecycle/README.md b/translations/br/4-Data-Science-Lifecycle/README.md index 38994183..1ec32cf1 100644 --- a/translations/br/4-Data-Science-Lifecycle/README.md +++ b/translations/br/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # O Ciclo de Vida da Ciência de Dados -![communication](../../../translated_images/br/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) +![communication](../../../translated_images/pt-BR/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > Foto por Headway no Unsplash Nestes módulos, você explorará alguns aspectos do ciclo de vida da Ciência de Dados, incluindo análise e comunicação de dados. diff --git a/translations/br/5-Data-Science-In-Cloud/README.md b/translations/br/5-Data-Science-In-Cloud/README.md index 8596371e..3c2ecc55 100644 --- a/translations/br/5-Data-Science-In-Cloud/README.md +++ b/translations/br/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Ciência de Dados na Nuvem -![cloud-picture](../../../translated_images/br/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) +![cloud-picture](../../../translated_images/pt-BR/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > Foto de [Jelleke Vanooteghem](https://unsplash.com/@ilumire) no [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) Quando se trata de fazer ciência de dados com big data, a nuvem pode ser um divisor de águas. Nas próximas três lições, vamos entender o que é a nuvem e por que ela pode ser tão útil. Também vamos explorar um conjunto de dados sobre insuficiência cardíaca e construir um modelo para ajudar a avaliar a probabilidade de alguém sofrer uma insuficiência cardíaca. Usaremos o poder da nuvem para treinar, implantar e consumir um modelo de duas maneiras diferentes. Uma delas utilizando apenas a interface do usuário em um formato de Baixo Código/Sem Código, e a outra utilizando o Azure Machine Learning Software Developer Kit (Azure ML SDK). -![project-schema](../../../translated_images/br/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) +![project-schema](../../../translated_images/pt-BR/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### Tópicos diff --git a/translations/br/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/br/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 3875f853..d37d2bad 100644 --- a/translations/br/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/br/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ Graças à democratização da IA, os desenvolvedores estão encontrando mais fa * [Ciência de Dados na Saúde](https://data-flair.training/blogs/data-science-in-healthcare/) - destaca aplicações como imagem médica (e.g., ressonância magnética, raio-X, tomografia), genômica (sequenciamento de DNA), desenvolvimento de medicamentos (avaliação de risco, previsão de sucesso), análise preditiva (cuidados com pacientes e logística de suprimentos), rastreamento e prevenção de doenças etc. -![Aplicações de Ciência de Dados no Mundo Real](../../../../translated_images/br/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Crédito da Imagem: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Aplicações de Ciência de Dados no Mundo Real](../../../../translated_images/pt-BR/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Crédito da Imagem: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) A figura mostra outros domínios e exemplos de aplicação de técnicas de ciência de dados. Quer explorar outras aplicações? Confira a seção [Revisão e Autoestudo](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) abaixo. diff --git a/translations/br/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/br/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 4961b59d..087017ff 100644 --- a/translations/br/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/br/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ A interface do Explorer (mostrada na captura de tela abaixo) permite que você s 2. Explorar o [Catálogo de conjuntos de dados](https://planetarycomputer.microsoft.com/catalog) - aprender o propósito de cada conjunto de dados. 3. Usar o Explorer - escolher um conjunto de dados de interesse, selecionar uma consulta relevante e uma opção de renderização. -![O Explorer do Planetary Computer](../../../../translated_images/br/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) +![O Explorer do Planetary Computer](../../../../translated_images/pt-BR/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `Sua Tarefa:` Agora, estude a visualização que foi gerada no navegador e responda às seguintes perguntas: diff --git a/translations/br/CONTRIBUTING.md b/translations/br/CONTRIBUTING.md index 2c00e1c5..bec4f849 100644 --- a/translations/br/CONTRIBUTING.md +++ b/translations/br/CONTRIBUTING.md @@ -316,7 +316,7 @@ Inclua na descrição do seu PR: ``` ```` -- Adicione texto alternativo às imagens: `![Texto alternativo](../../translated_images/br/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` +- Adicione texto alternativo às imagens: `![Texto alternativo](../../translated_images/pt-BR/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Mantenha os comprimentos das linhas razoáveis (cerca de 80-100 caracteres) ### Python diff --git a/translations/br/README.md b/translations/br/README.md index 49c22d10..f869e8d2 100644 --- a/translations/br/README.md +++ b/translations/br/README.md @@ -33,7 +33,7 @@ Os Azure Cloud Advocates da Microsoft têm o prazer de oferecer um currículo de **🙏 Agradecimentos especiais 🙏 aos nossos autores, revisores e colaboradores de conteúdo do [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** notavelmente Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Anotações visuais por @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/br/00-Title.8af36cd35da1ac55.webp)| +|![Anotações visuais por @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/pt-BR/00-Title.8af36cd35da1ac55.webp)| |:---:| | Ciência de Dados para Iniciantes - _Anotação visual por [@nitya](https://twitter.com/nitya)_ | @@ -62,7 +62,7 @@ Os Azure Cloud Advocates da Microsoft têm o prazer de oferecer um currículo de Estamos com uma série de aprendizado no Discord com IA, saiba mais e junte-se a nós em [Learn with AI Series](https://aka.ms/learnwithai/discord) de 18 a 30 de setembro de 2025. Você receberá dicas e truques para usar o GitHub Copilot para Ciência de Dados. -![Série Learn with AI](../../../../translated_images/br/1.2b28cdc6205e26fe.webp) +![Série Learn with AI](../../../../translated_images/pt-BR/1.2b28cdc6205e26fe.webp) # Você é estudante? @@ -142,7 +142,7 @@ Cada exemplo inclui comentários detalhados explicando cada passo, tornando-o pe ## Aulas -|![ Sketchnote por @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/br/00-Roadmap.4905d6567dff4753.webp)| +|![ Sketchnote por @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/pt-BR/00-Roadmap.4905d6567dff4753.webp)| |:---:| | Ciência de Dados para Iniciantes: Roteiro - _Sketchnote por [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/br/sketchnotes/README.md b/translations/br/sketchnotes/README.md index 9cb5e895..13809f0e 100644 --- a/translations/br/sketchnotes/README.md +++ b/translations/br/sketchnotes/README.md @@ -13,7 +13,7 @@ Encontre todos os sketchnotes aqui! Nitya Narasimhan, artista -![sketchnote do roadmap](../../../translated_images/br/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) +![sketchnote do roadmap](../../../translated_images/pt-BR/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/en/.co-op-translator.json b/translations/en/.co-op-translator.json new file mode 100644 index 00000000..38f6b82b --- /dev/null +++ b/translations/en/.co-op-translator.json @@ -0,0 +1,422 @@ +{ + "1-Introduction/01-defining-data-science/README.md": { + "original_hash": "43212cc1ac137b7bb1dcfb37ca06b0f4", + "translation_date": "2025-10-25T18:32:23+00:00", + "source_file": "1-Introduction/01-defining-data-science/README.md", + "language_code": "en" + }, + "1-Introduction/01-defining-data-science/assignment.md": { + "original_hash": "4e0f1773b9bee1be3b28f9fe2c71b3de", + "translation_date": "2025-08-31T11:09:48+00:00", + "source_file": 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b/translations/en/1-Introduction/01-defining-data-science/assignment.md index 56a870da..3760cbc9 100644 --- a/translations/en/1-Introduction/01-defining-data-science/assignment.md +++ b/translations/en/1-Introduction/01-defining-data-science/assignment.md @@ -1,12 +1,3 @@ - # Assignment: Data Science Scenarios In this first assignment, we ask you to think about some real-life processes or problems in different domains, and how you can improve them using the Data Science process. Consider the following: diff --git a/translations/en/1-Introduction/01-defining-data-science/solution/assignment.md b/translations/en/1-Introduction/01-defining-data-science/solution/assignment.md index b7ce465f..fa7e77c8 100644 --- a/translations/en/1-Introduction/01-defining-data-science/solution/assignment.md +++ b/translations/en/1-Introduction/01-defining-data-science/solution/assignment.md @@ -1,12 +1,3 @@ - # Assignment: Data Science Scenarios In this first assignment, we ask you to think about some real-life processes or problems in different domains, and how you can improve them using the Data Science process. Consider the following: diff --git a/translations/en/1-Introduction/02-ethics/README.md b/translations/en/1-Introduction/02-ethics/README.md index bbfbb3f9..1c1d4230 100644 --- a/translations/en/1-Introduction/02-ethics/README.md +++ b/translations/en/1-Introduction/02-ethics/README.md @@ -1,12 +1,3 @@ - # Introduction to Data Ethics |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/02-Ethics.png)| diff --git a/translations/en/1-Introduction/02-ethics/assignment.md b/translations/en/1-Introduction/02-ethics/assignment.md index 11e5d408..e70062da 100644 --- a/translations/en/1-Introduction/02-ethics/assignment.md +++ b/translations/en/1-Introduction/02-ethics/assignment.md @@ -1,12 +1,3 @@ - ## Write A Data Ethics Case Study ## Instructions diff --git a/translations/en/1-Introduction/03-defining-data/README.md b/translations/en/1-Introduction/03-defining-data/README.md index e616655d..e0cbaf3c 100644 --- a/translations/en/1-Introduction/03-defining-data/README.md +++ b/translations/en/1-Introduction/03-defining-data/README.md @@ -1,12 +1,3 @@ - # Defining Data |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/03-DefiningData.png)| diff --git a/translations/en/1-Introduction/03-defining-data/assignment.md b/translations/en/1-Introduction/03-defining-data/assignment.md index 10c0e4ba..8161c99d 100644 --- a/translations/en/1-Introduction/03-defining-data/assignment.md +++ b/translations/en/1-Introduction/03-defining-data/assignment.md @@ -1,12 +1,3 @@ - # Classifying Datasets ## Instructions diff --git a/translations/en/1-Introduction/04-stats-and-probability/README.md b/translations/en/1-Introduction/04-stats-and-probability/README.md index 73d4801a..02fce28c 100644 --- a/translations/en/1-Introduction/04-stats-and-probability/README.md +++ b/translations/en/1-Introduction/04-stats-and-probability/README.md @@ -1,12 +1,3 @@ - # A Brief Introduction to Statistics and Probability |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/04-Statistics-Probability.png)| @@ -64,7 +55,7 @@ To better understand the distribution of data, we use **quartiles**: The relationship between the median and quartiles can be visualized using a **box plot**: -Box Plot Explanation +Box Plot Explanation We also calculate the **interquartile range** (IQR=Q3-Q1) and identify **outliers**—values outside the range [Q1-1.5*IQR, Q3+1.5*IQR]. diff --git a/translations/en/1-Introduction/04-stats-and-probability/assignment.md b/translations/en/1-Introduction/04-stats-and-probability/assignment.md index 9819213e..d1150e04 100644 --- a/translations/en/1-Introduction/04-stats-and-probability/assignment.md +++ b/translations/en/1-Introduction/04-stats-and-probability/assignment.md @@ -1,12 +1,3 @@ - # Small Diabetes Study In this assignment, we will work with a small dataset of diabetes patients taken from [here](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html). diff --git a/translations/en/1-Introduction/README.md b/translations/en/1-Introduction/README.md index 00d42ab5..f25151e7 100644 --- a/translations/en/1-Introduction/README.md +++ b/translations/en/1-Introduction/README.md @@ -1,12 +1,3 @@ - # Introduction to Data Science ![data in action](../../../1-Introduction/images/data.jpg) diff --git a/translations/en/2-Working-With-Data/05-relational-databases/README.md b/translations/en/2-Working-With-Data/05-relational-databases/README.md index 2750761a..a2e03888 100644 --- a/translations/en/2-Working-With-Data/05-relational-databases/README.md +++ b/translations/en/2-Working-With-Data/05-relational-databases/README.md @@ -1,12 +1,3 @@ - # Working with Data: Relational Databases |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/05-RelationalData.png)| diff --git a/translations/en/2-Working-With-Data/05-relational-databases/assignment.md b/translations/en/2-Working-With-Data/05-relational-databases/assignment.md index 1521efe4..362fdf49 100644 --- a/translations/en/2-Working-With-Data/05-relational-databases/assignment.md +++ b/translations/en/2-Working-With-Data/05-relational-databases/assignment.md @@ -1,12 +1,3 @@ - # Displaying airport data You have been provided a [database](https://raw.githubusercontent.com/Microsoft/Data-Science-For-Beginners/main/2-Working-With-Data/05-relational-databases/airports.db) built on [SQLite](https://sqlite.org/index.html) which contains information about airports. The schema is displayed below. You will use the [SQLite extension](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum) in [Visual Studio Code](https://code.visualstudio.com?WT.mc_id=academic-77958-bethanycheum) to display information about different cities' airports. diff --git a/translations/en/2-Working-With-Data/06-non-relational/README.md b/translations/en/2-Working-With-Data/06-non-relational/README.md index db2ae5bc..1997326b 100644 --- a/translations/en/2-Working-With-Data/06-non-relational/README.md +++ b/translations/en/2-Working-With-Data/06-non-relational/README.md @@ -1,12 +1,3 @@ - # Working with Data: Non-Relational Data |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/06-NoSQL.png)| diff --git a/translations/en/2-Working-With-Data/06-non-relational/assignment.md b/translations/en/2-Working-With-Data/06-non-relational/assignment.md index c8735c9a..7bfa92f0 100644 --- a/translations/en/2-Working-With-Data/06-non-relational/assignment.md +++ b/translations/en/2-Working-With-Data/06-non-relational/assignment.md @@ -1,12 +1,3 @@ - # Soda Profits ## Instructions diff --git a/translations/en/2-Working-With-Data/07-python/README.md b/translations/en/2-Working-With-Data/07-python/README.md index 53ca825a..66c7e303 100644 --- a/translations/en/2-Working-With-Data/07-python/README.md +++ b/translations/en/2-Working-With-Data/07-python/README.md @@ -1,12 +1,3 @@ - # Working with Data: Python and the Pandas Library | ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/07-WorkWithPython.png) | diff --git a/translations/en/2-Working-With-Data/07-python/assignment.md b/translations/en/2-Working-With-Data/07-python/assignment.md index 009150ef..d20afa00 100644 --- a/translations/en/2-Working-With-Data/07-python/assignment.md +++ b/translations/en/2-Working-With-Data/07-python/assignment.md @@ -1,12 +1,3 @@ - # Assignment for Data Processing in Python In this assignment, we will ask you to expand upon the code we started developing in our challenges. The assignment consists of two parts: diff --git a/translations/en/2-Working-With-Data/08-data-preparation/README.md b/translations/en/2-Working-With-Data/08-data-preparation/README.md index a7a0f730..6d5f54f1 100644 --- a/translations/en/2-Working-With-Data/08-data-preparation/README.md +++ b/translations/en/2-Working-With-Data/08-data-preparation/README.md @@ -1,12 +1,3 @@ - # Working with Data: Data Preparation |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/08-DataPreparation.png)| diff --git a/translations/en/2-Working-With-Data/08-data-preparation/assignment.md b/translations/en/2-Working-With-Data/08-data-preparation/assignment.md index c7ec9dd8..8c24dc46 100644 --- a/translations/en/2-Working-With-Data/08-data-preparation/assignment.md +++ b/translations/en/2-Working-With-Data/08-data-preparation/assignment.md @@ -1,12 +1,3 @@ - # Evaluating Data from a Form A client has been testing a [small form](../../../../2-Working-With-Data/08-data-preparation/index.html) to collect some basic information about their customer base. They have shared their findings with you to validate the data they have gathered. You can open the `index.html` page in your browser to review the form. diff --git a/translations/en/2-Working-With-Data/README.md b/translations/en/2-Working-With-Data/README.md index 08ce9ae1..e2f0ae19 100644 --- a/translations/en/2-Working-With-Data/README.md +++ b/translations/en/2-Working-With-Data/README.md @@ -1,12 +1,3 @@ - # Working with Data ![data love](../../../2-Working-With-Data/images/data-love.jpg) diff --git a/translations/en/3-Data-Visualization/09-visualization-quantities/README.md b/translations/en/3-Data-Visualization/09-visualization-quantities/README.md index abef206c..3d98bdae 100644 --- a/translations/en/3-Data-Visualization/09-visualization-quantities/README.md +++ b/translations/en/3-Data-Visualization/09-visualization-quantities/README.md @@ -1,12 +1,3 @@ - # Visualizing Quantities |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/09-Visualizing-Quantities.png)| diff --git a/translations/en/3-Data-Visualization/09-visualization-quantities/assignment.md b/translations/en/3-Data-Visualization/09-visualization-quantities/assignment.md index 93d7d090..f8c57dae 100644 --- a/translations/en/3-Data-Visualization/09-visualization-quantities/assignment.md +++ b/translations/en/3-Data-Visualization/09-visualization-quantities/assignment.md @@ -1,12 +1,3 @@ - # Lines, Scatters and Bars ## Instructions diff --git a/translations/en/3-Data-Visualization/10-visualization-distributions/README.md b/translations/en/3-Data-Visualization/10-visualization-distributions/README.md index aa84f560..e938beb8 100644 --- a/translations/en/3-Data-Visualization/10-visualization-distributions/README.md +++ b/translations/en/3-Data-Visualization/10-visualization-distributions/README.md @@ -1,12 +1,3 @@ - # Visualizing Distributions |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/10-Visualizing-Distributions.png)| diff --git a/translations/en/3-Data-Visualization/10-visualization-distributions/assignment.md b/translations/en/3-Data-Visualization/10-visualization-distributions/assignment.md index 4712cf7d..3fd7b8e1 100644 --- a/translations/en/3-Data-Visualization/10-visualization-distributions/assignment.md +++ b/translations/en/3-Data-Visualization/10-visualization-distributions/assignment.md @@ -1,12 +1,3 @@ - # Apply your skills ## Instructions diff --git a/translations/en/3-Data-Visualization/11-visualization-proportions/README.md b/translations/en/3-Data-Visualization/11-visualization-proportions/README.md index 17cb8357..21953eea 100644 --- a/translations/en/3-Data-Visualization/11-visualization-proportions/README.md +++ b/translations/en/3-Data-Visualization/11-visualization-proportions/README.md @@ -1,12 +1,3 @@ - # Visualizing Proportions |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/11-Visualizing-Proportions.png)| diff --git a/translations/en/3-Data-Visualization/11-visualization-proportions/assignment.md b/translations/en/3-Data-Visualization/11-visualization-proportions/assignment.md index ad391564..64c3d40e 100644 --- a/translations/en/3-Data-Visualization/11-visualization-proportions/assignment.md +++ b/translations/en/3-Data-Visualization/11-visualization-proportions/assignment.md @@ -1,12 +1,3 @@ - # Try it in Excel ## Instructions diff --git a/translations/en/3-Data-Visualization/12-visualization-relationships/README.md b/translations/en/3-Data-Visualization/12-visualization-relationships/README.md index 6e0ad823..c3781f3a 100644 --- a/translations/en/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/en/3-Data-Visualization/12-visualization-relationships/README.md @@ -1,12 +1,3 @@ - # Visualizing Relationships: All About Honey 🍯 |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/12-Visualizing-Relationships.png)| diff --git a/translations/en/3-Data-Visualization/12-visualization-relationships/assignment.md b/translations/en/3-Data-Visualization/12-visualization-relationships/assignment.md index 6cdef82c..d4035a55 100644 --- a/translations/en/3-Data-Visualization/12-visualization-relationships/assignment.md +++ b/translations/en/3-Data-Visualization/12-visualization-relationships/assignment.md @@ -1,12 +1,3 @@ - # Explore the Beehive ## Instructions diff --git a/translations/en/3-Data-Visualization/13-meaningful-visualizations/README.md b/translations/en/3-Data-Visualization/13-meaningful-visualizations/README.md index 6d87b81b..f1e08581 100644 --- a/translations/en/3-Data-Visualization/13-meaningful-visualizations/README.md +++ b/translations/en/3-Data-Visualization/13-meaningful-visualizations/README.md @@ -1,12 +1,3 @@ - # Making Meaningful Visualizations |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/13-MeaningfulViz.png)| diff --git a/translations/en/3-Data-Visualization/13-meaningful-visualizations/assignment.md b/translations/en/3-Data-Visualization/13-meaningful-visualizations/assignment.md index f2196e3c..bba0d928 100644 --- a/translations/en/3-Data-Visualization/13-meaningful-visualizations/assignment.md +++ b/translations/en/3-Data-Visualization/13-meaningful-visualizations/assignment.md @@ -1,12 +1,3 @@ - # Build your own custom vis ## Instructions diff --git a/translations/en/3-Data-Visualization/13-meaningful-visualizations/solution/README.md b/translations/en/3-Data-Visualization/13-meaningful-visualizations/solution/README.md index 711b93fe..3738003c 100644 --- a/translations/en/3-Data-Visualization/13-meaningful-visualizations/solution/README.md +++ b/translations/en/3-Data-Visualization/13-meaningful-visualizations/solution/README.md @@ -1,12 +1,3 @@ - # Dangerous Liaisons data visualization project To begin, make sure NPM and Node are installed and running on your computer. Install the dependencies (npm install) and then launch the project locally (npm run serve): diff --git a/translations/en/3-Data-Visualization/13-meaningful-visualizations/starter/README.md b/translations/en/3-Data-Visualization/13-meaningful-visualizations/starter/README.md index 2bc63761..3738003c 100644 --- a/translations/en/3-Data-Visualization/13-meaningful-visualizations/starter/README.md +++ b/translations/en/3-Data-Visualization/13-meaningful-visualizations/starter/README.md @@ -1,12 +1,3 @@ - # Dangerous Liaisons data visualization project To begin, make sure NPM and Node are installed and running on your computer. Install the dependencies (npm install) and then launch the project locally (npm run serve): diff --git a/translations/en/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/en/3-Data-Visualization/R/09-visualization-quantities/README.md index 942e307b..9174f454 100644 --- a/translations/en/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/en/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -1,12 +1,3 @@ - # Visualizing Quantities |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/sketchnotes/09-Visualizing-Quantities.png)| |:---:| diff --git a/translations/en/3-Data-Visualization/R/09-visualization-quantities/assignment.md b/translations/en/3-Data-Visualization/R/09-visualization-quantities/assignment.md index b8a7a354..5a16fb36 100644 --- a/translations/en/3-Data-Visualization/R/09-visualization-quantities/assignment.md +++ b/translations/en/3-Data-Visualization/R/09-visualization-quantities/assignment.md @@ -1,12 +1,3 @@ - # Lines, Scatters and Bars ## Instructions diff --git a/translations/en/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/en/3-Data-Visualization/R/10-visualization-distributions/README.md index c000af32..486b9ebd 100644 --- a/translations/en/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/en/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -1,12 +1,3 @@ - # Visualizing Distributions |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/sketchnotes/10-Visualizing-Distributions.png)| diff --git a/translations/en/3-Data-Visualization/R/10-visualization-distributions/assignment.md b/translations/en/3-Data-Visualization/R/10-visualization-distributions/assignment.md index 37be6bdb..c210b5ca 100644 --- a/translations/en/3-Data-Visualization/R/10-visualization-distributions/assignment.md +++ b/translations/en/3-Data-Visualization/R/10-visualization-distributions/assignment.md @@ -1,12 +1,3 @@ - # Apply your skills ## Instructions diff --git a/translations/en/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/en/3-Data-Visualization/R/11-visualization-proportions/README.md index ad242727..26284c21 100644 --- a/translations/en/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/en/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -1,12 +1,3 @@ - # Visualizing Proportions |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/11-Visualizing-Proportions.png)| diff --git a/translations/en/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/en/3-Data-Visualization/R/12-visualization-relationships/README.md index d0a509b3..09acb037 100644 --- a/translations/en/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/en/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -1,12 +1,3 @@ - # Visualizing Relationships: All About Honey 🍯 |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/12-Visualizing-Relationships.png)| diff --git a/translations/en/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/en/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index f1a19bf6..ca1064fe 100644 --- a/translations/en/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/en/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -1,12 +1,3 @@ - # Creating Meaningful Visualizations |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/13-MeaningfulViz.png)| diff --git a/translations/en/3-Data-Visualization/README.md b/translations/en/3-Data-Visualization/README.md index de7604c8..2fb9614b 100644 --- a/translations/en/3-Data-Visualization/README.md +++ b/translations/en/3-Data-Visualization/README.md @@ -1,12 +1,3 @@ - # Visualizations ![a bee on a lavender flower](../../../3-Data-Visualization/images/bee.jpg) diff --git a/translations/en/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/en/4-Data-Science-Lifecycle/14-Introduction/README.md index 046185fa..24ac926e 100644 --- a/translations/en/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/en/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -1,12 +1,3 @@ - # Introduction to the Data Science Lifecycle |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/14-DataScience-Lifecycle.png)| diff --git a/translations/en/4-Data-Science-Lifecycle/14-Introduction/assignment.md b/translations/en/4-Data-Science-Lifecycle/14-Introduction/assignment.md index 0f87469f..809fbd43 100644 --- a/translations/en/4-Data-Science-Lifecycle/14-Introduction/assignment.md +++ b/translations/en/4-Data-Science-Lifecycle/14-Introduction/assignment.md @@ -1,12 +1,3 @@ - # Assessing a Dataset A client has reached out to your team for assistance in analyzing the seasonal spending habits of taxi customers in New York City. diff --git a/translations/en/4-Data-Science-Lifecycle/15-analyzing/README.md b/translations/en/4-Data-Science-Lifecycle/15-analyzing/README.md index 2db19888..3d5c1139 100644 --- a/translations/en/4-Data-Science-Lifecycle/15-analyzing/README.md +++ b/translations/en/4-Data-Science-Lifecycle/15-analyzing/README.md @@ -1,12 +1,3 @@ - # The Data Science Lifecycle: Analyzing |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/15-Analyzing.png)| diff --git a/translations/en/4-Data-Science-Lifecycle/15-analyzing/assignment.md b/translations/en/4-Data-Science-Lifecycle/15-analyzing/assignment.md index 740490d5..1860146a 100644 --- a/translations/en/4-Data-Science-Lifecycle/15-analyzing/assignment.md +++ b/translations/en/4-Data-Science-Lifecycle/15-analyzing/assignment.md @@ -1,12 +1,3 @@ - # Exploring for answers This is a continuation of the previous lesson's [assignment](../14-Introduction/assignment.md), where we briefly examined the dataset. Now, we will dive deeper into the data. diff --git a/translations/en/4-Data-Science-Lifecycle/16-communication/README.md b/translations/en/4-Data-Science-Lifecycle/16-communication/README.md index dba49183..5d50392f 100644 --- a/translations/en/4-Data-Science-Lifecycle/16-communication/README.md +++ b/translations/en/4-Data-Science-Lifecycle/16-communication/README.md @@ -1,12 +1,3 @@ - # The Data Science Lifecycle: Communication |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/16-Communicating.png)| diff --git a/translations/en/4-Data-Science-Lifecycle/16-communication/assignment.md b/translations/en/4-Data-Science-Lifecycle/16-communication/assignment.md index 369b68ee..bd546115 100644 --- a/translations/en/4-Data-Science-Lifecycle/16-communication/assignment.md +++ b/translations/en/4-Data-Science-Lifecycle/16-communication/assignment.md @@ -1,12 +1,3 @@ - # Tell a story ## Instructions diff --git a/translations/en/4-Data-Science-Lifecycle/README.md b/translations/en/4-Data-Science-Lifecycle/README.md index 90eb5165..a194e5f8 100644 --- a/translations/en/4-Data-Science-Lifecycle/README.md +++ b/translations/en/4-Data-Science-Lifecycle/README.md @@ -1,12 +1,3 @@ - # The Data Science Lifecycle ![communication](../../../4-Data-Science-Lifecycle/images/communication.jpg) diff --git a/translations/en/5-Data-Science-In-Cloud/17-Introduction/README.md b/translations/en/5-Data-Science-In-Cloud/17-Introduction/README.md index 87749274..359bac4f 100644 --- a/translations/en/5-Data-Science-In-Cloud/17-Introduction/README.md +++ b/translations/en/5-Data-Science-In-Cloud/17-Introduction/README.md @@ -1,12 +1,3 @@ - # Introduction to Data Science in the Cloud |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/17-DataScience-Cloud.png)| diff --git a/translations/en/5-Data-Science-In-Cloud/17-Introduction/assignment.md b/translations/en/5-Data-Science-In-Cloud/17-Introduction/assignment.md index 635c5498..eba20cec 100644 --- a/translations/en/5-Data-Science-In-Cloud/17-Introduction/assignment.md +++ b/translations/en/5-Data-Science-In-Cloud/17-Introduction/assignment.md @@ -1,12 +1,3 @@ - # Market Research ## Instructions diff --git a/translations/en/5-Data-Science-In-Cloud/18-Low-Code/README.md b/translations/en/5-Data-Science-In-Cloud/18-Low-Code/README.md index dd09fc30..88891901 100644 --- a/translations/en/5-Data-Science-In-Cloud/18-Low-Code/README.md +++ b/translations/en/5-Data-Science-In-Cloud/18-Low-Code/README.md @@ -1,12 +1,3 @@ - # Data Science in the Cloud: The "Low code/No code" way |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/18-DataScience-Cloud.png)| diff --git a/translations/en/5-Data-Science-In-Cloud/18-Low-Code/assignment.md b/translations/en/5-Data-Science-In-Cloud/18-Low-Code/assignment.md index 0236390e..c90db30b 100644 --- a/translations/en/5-Data-Science-In-Cloud/18-Low-Code/assignment.md +++ b/translations/en/5-Data-Science-In-Cloud/18-Low-Code/assignment.md @@ -1,12 +1,3 @@ - # Low code/No code Data Science project on Azure ML ## Instructions diff --git a/translations/en/5-Data-Science-In-Cloud/19-Azure/README.md b/translations/en/5-Data-Science-In-Cloud/19-Azure/README.md index bb589f43..40aa2fb5 100644 --- a/translations/en/5-Data-Science-In-Cloud/19-Azure/README.md +++ b/translations/en/5-Data-Science-In-Cloud/19-Azure/README.md @@ -1,12 +1,3 @@ - # Data Science in the Cloud: The "Azure ML SDK" way |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/19-DataScience-Cloud.png)| diff --git a/translations/en/5-Data-Science-In-Cloud/19-Azure/assignment.md b/translations/en/5-Data-Science-In-Cloud/19-Azure/assignment.md index 85e151a8..4d5a0c70 100644 --- a/translations/en/5-Data-Science-In-Cloud/19-Azure/assignment.md +++ b/translations/en/5-Data-Science-In-Cloud/19-Azure/assignment.md @@ -1,12 +1,3 @@ - # Data Science project using Azure ML SDK ## Instructions diff --git a/translations/en/5-Data-Science-In-Cloud/README.md b/translations/en/5-Data-Science-In-Cloud/README.md index 0db59ba0..628013f8 100644 --- a/translations/en/5-Data-Science-In-Cloud/README.md +++ b/translations/en/5-Data-Science-In-Cloud/README.md @@ -1,12 +1,3 @@ - # Data Science in the Cloud ![cloud-picture](../../../5-Data-Science-In-Cloud/images/cloud-picture.jpg) diff --git a/translations/en/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/en/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 4f5d9fef..2feee804 100644 --- a/translations/en/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/en/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -1,12 +1,3 @@ - # Data Science in the Real World | ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-RealWorld.png) | diff --git a/translations/en/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/en/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 5c2bd6ab..89fc48e2 100644 --- a/translations/en/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/en/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -1,12 +1,3 @@ - # Explore a Planetary Computer Dataset ## Instructions diff --git a/translations/en/6-Data-Science-In-Wild/README.md b/translations/en/6-Data-Science-In-Wild/README.md index 534555bb..032f9f8d 100644 --- a/translations/en/6-Data-Science-In-Wild/README.md +++ b/translations/en/6-Data-Science-In-Wild/README.md @@ -1,12 +1,3 @@ - # Data Science in the Wild Practical applications of data science across various industries. diff --git a/translations/en/AGENTS.md b/translations/en/AGENTS.md index d605a579..5e497c95 100644 --- a/translations/en/AGENTS.md +++ b/translations/en/AGENTS.md @@ -1,12 +1,3 @@ - # AGENTS.md ## Project Overview diff --git a/translations/en/CODE_OF_CONDUCT.md b/translations/en/CODE_OF_CONDUCT.md index 54d00c58..45dfbe83 100644 --- a/translations/en/CODE_OF_CONDUCT.md +++ b/translations/en/CODE_OF_CONDUCT.md @@ -1,12 +1,3 @@ - # Microsoft Open Source Code of Conduct This project follows the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). diff --git a/translations/en/CONTRIBUTING.md b/translations/en/CONTRIBUTING.md index a632793f..8d707e93 100644 --- a/translations/en/CONTRIBUTING.md +++ b/translations/en/CONTRIBUTING.md @@ -1,12 +1,3 @@ - # Contributing to Data Science for Beginners Thank you for your interest in contributing to the Data Science for Beginners curriculum! We welcome contributions from the community. diff --git a/translations/en/INSTALLATION.md b/translations/en/INSTALLATION.md index 7b734d1a..2c5ac5a1 100644 --- a/translations/en/INSTALLATION.md +++ b/translations/en/INSTALLATION.md @@ -1,12 +1,3 @@ - # Installation Guide This guide will help you set up your environment to work with the Data Science for Beginners curriculum. diff --git a/translations/en/README.md b/translations/en/README.md index 437b3146..e4d3b405 100644 --- a/translations/en/README.md +++ b/translations/en/README.md @@ -1,12 +1,3 @@ - # Data Science for Beginners - A Curriculum [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198) @@ -33,7 +24,7 @@ Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson cur **🙏 Special thanks 🙏 to our [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) authors, reviewers and content contributors,** notably Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/en/00-Title.8af36cd35da1ac55.webp)| +|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../translated_images/en/00-Title.8af36cd35da1ac55.webp)| |:---:| | Data Science For Beginners - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | @@ -42,7 +33,7 @@ Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson cur #### Supported via GitHub Action (Automated & Always Up-to-Date) -[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) +[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh-CN/README.md) | [Chinese (Traditional, Hong Kong)](../zh-HK/README.md) | [Chinese (Traditional, Macau)](../zh-MO/README.md) | [Chinese (Traditional, Taiwan)](../zh-TW/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../pt-BR/README.md) | [Portuguese (Portugal)](../pt-PT/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) > **Prefer to Clone Locally?** @@ -62,13 +53,13 @@ Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson cur We have a Discord learn with AI series ongoing, learn more and join us at [Learn with AI Series](https://aka.ms/learnwithai/discord) from 18 - 30 September, 2025. You will get tips and tricks of using GitHub Copilot for Data Science. -![Learn with AI series](../../../../translated_images/en/1.2b28cdc6205e26fe.webp) +![Learn with AI series](../../translated_images/en/1.2b28cdc6205e26fe.webp) # Are you a student? Get started with the following resources: -- [Student Hub page](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) In this page, you will find beginner resources, Student packs and even ways to get a free cert voucher. This is one page you want to bookmark and check from time to time as we switch out content at least monthly. +- [Student Hub page](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) On this page, you will find beginner resources, Student packs and even ways to get a free cert voucher. This is one page you want to bookmark and check from time to time as we switch out content at least monthly. - [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Join a global community of student ambassadors, this could be your way into Microsoft. # Getting Started @@ -94,8 +85,8 @@ Get started with the following resources: ## 👩‍🏫 For Teachers > **Teachers**: we have [included some suggestions](for-teachers.md) on how to use this curriculum. We'd love your feedback [in our discussion forum](https://github.com/microsoft/Data-Science-For-Beginners/discussions)! - ## Meet the Team + [![Promo video](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "Promo video") **Gif by** [Mohit Jaisal](https://www.linkedin.com/in/mohitjaisal) @@ -142,7 +133,7 @@ Each example includes detailed comments explaining every step, making it perfect ## Lessons -|![ Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/en/00-Roadmap.4905d6567dff4753.webp)| +|![ Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../translated_images/en/00-Roadmap.4905d6567dff4753.webp)| |:---:| | Data Science For Beginners: Roadmap - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/en/SECURITY.md b/translations/en/SECURITY.md index a0cfa614..4edb9d8b 100644 --- a/translations/en/SECURITY.md +++ b/translations/en/SECURITY.md @@ -1,12 +1,3 @@ - ## Security Microsoft prioritizes the security of its software products and services, including all source code repositories managed through our GitHub organizations, such as [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/). diff --git a/translations/en/SUPPORT.md b/translations/en/SUPPORT.md index 221af0fe..ca74206b 100644 --- a/translations/en/SUPPORT.md +++ b/translations/en/SUPPORT.md @@ -1,12 +1,3 @@ - # Support ## How to report issues and seek assistance diff --git a/translations/en/TROUBLESHOOTING.md b/translations/en/TROUBLESHOOTING.md index 550d8e9e..aa5f5ccd 100644 --- a/translations/en/TROUBLESHOOTING.md +++ b/translations/en/TROUBLESHOOTING.md @@ -1,12 +1,3 @@ - # Troubleshooting Guide This guide provides solutions to common issues you might encounter while working with the Data Science for Beginners curriculum. diff --git a/translations/en/USAGE.md b/translations/en/USAGE.md index 3b62a0e2..c2b4ee81 100644 --- a/translations/en/USAGE.md +++ b/translations/en/USAGE.md @@ -1,12 +1,3 @@ - # Usage Guide This guide provides examples and common workflows for using the Data Science for Beginners curriculum. diff --git a/translations/en/docs/_sidebar.md b/translations/en/docs/_sidebar.md index d4b5d12f..e541f374 100644 --- a/translations/en/docs/_sidebar.md +++ b/translations/en/docs/_sidebar.md @@ -1,12 +1,3 @@ - - Introduction - [Defining Data Science](../1-Introduction/01-defining-data-science/README.md) - [Ethics of Data Science](../1-Introduction/02-ethics/README.md) diff --git a/translations/en/examples/README.md b/translations/en/examples/README.md index f77d5ffe..02169ae1 100644 --- a/translations/en/examples/README.md +++ b/translations/en/examples/README.md @@ -1,12 +1,3 @@ - # Beginner-Friendly Data Science Examples Welcome to the examples directory! This collection of simple, well-commented examples is designed to help you get started with data science, even if you're a complete beginner. diff --git a/translations/en/for-teachers.md b/translations/en/for-teachers.md index 97553872..092542d3 100644 --- a/translations/en/for-teachers.md +++ b/translations/en/for-teachers.md @@ -1,12 +1,3 @@ - ## For Educators Would you like to use this curriculum in your classroom? Go ahead! diff --git a/translations/en/quiz-app/README.md b/translations/en/quiz-app/README.md index df7b9d76..8422c5ce 100644 --- a/translations/en/quiz-app/README.md +++ b/translations/en/quiz-app/README.md @@ -1,12 +1,3 @@ - # Quizzes These quizzes are the pre- and post-lecture quizzes for the data science curriculum at https://aka.ms/datascience-beginners diff --git a/translations/en/sketchnotes/README.md b/translations/en/sketchnotes/README.md index 46b160fb..b70b85b5 100644 --- a/translations/en/sketchnotes/README.md +++ b/translations/en/sketchnotes/README.md @@ -1,12 +1,3 @@ - Find all sketchnotes here! ## Credits diff --git a/translations/es/.co-op-translator.json b/translations/es/.co-op-translator.json new file mode 100644 index 00000000..2f6c46c3 --- /dev/null +++ b/translations/es/.co-op-translator.json @@ -0,0 +1,422 @@ +{ + "1-Introduction/01-defining-data-science/README.md": { + "original_hash": "43212cc1ac137b7bb1dcfb37ca06b0f4", + "translation_date": 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a/translations/es/1-Introduction/01-defining-data-science/README.md b/translations/es/1-Introduction/01-defining-data-science/README.md index b8ace35f..06b6ce0b 100644 --- a/translations/es/1-Introduction/01-defining-data-science/README.md +++ b/translations/es/1-Introduction/01-defining-data-science/README.md @@ -1,12 +1,3 @@ - # Definiendo Ciencia de Datos | ![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/01-Definitions.png) | diff --git a/translations/es/1-Introduction/01-defining-data-science/assignment.md b/translations/es/1-Introduction/01-defining-data-science/assignment.md index eba42809..955138f0 100644 --- a/translations/es/1-Introduction/01-defining-data-science/assignment.md +++ b/translations/es/1-Introduction/01-defining-data-science/assignment.md @@ -1,12 +1,3 @@ - # Asignación: Escenarios de Ciencia de Datos En esta primera asignación, te pedimos que pienses en algún proceso o problema de la vida real en diferentes dominios de problemas, y cómo podrías mejorarlo utilizando el proceso de Ciencia de Datos. Reflexiona sobre lo siguiente: diff --git a/translations/es/1-Introduction/01-defining-data-science/solution/assignment.md b/translations/es/1-Introduction/01-defining-data-science/solution/assignment.md index 5a94b98a..fd177857 100644 --- a/translations/es/1-Introduction/01-defining-data-science/solution/assignment.md +++ b/translations/es/1-Introduction/01-defining-data-science/solution/assignment.md @@ -1,12 +1,3 @@ - # Asignación: Escenarios de Ciencia de Datos En esta primera asignación, te pedimos que pienses en algún proceso o problema de la vida real en diferentes dominios de problemas, y cómo podrías mejorarlo utilizando el proceso de Ciencia de Datos. Reflexiona sobre lo siguiente: diff --git a/translations/es/1-Introduction/02-ethics/README.md b/translations/es/1-Introduction/02-ethics/README.md index 808d074a..9f0d8825 100644 --- a/translations/es/1-Introduction/02-ethics/README.md +++ b/translations/es/1-Introduction/02-ethics/README.md @@ -1,12 +1,3 @@ - # Introducción a la Ética de los Datos |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/02-Ethics.png)| diff --git a/translations/es/1-Introduction/02-ethics/assignment.md b/translations/es/1-Introduction/02-ethics/assignment.md index 18a4cd17..a2a44e4d 100644 --- a/translations/es/1-Introduction/02-ethics/assignment.md +++ b/translations/es/1-Introduction/02-ethics/assignment.md @@ -1,12 +1,3 @@ - ## Escribe un Estudio de Caso sobre Ética de Datos ## Instrucciones diff --git a/translations/es/1-Introduction/03-defining-data/README.md b/translations/es/1-Introduction/03-defining-data/README.md index 394dc5b6..fb0323ff 100644 --- a/translations/es/1-Introduction/03-defining-data/README.md +++ b/translations/es/1-Introduction/03-defining-data/README.md @@ -1,12 +1,3 @@ - # Definiendo Datos |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/03-DefiningData.png)| diff --git a/translations/es/1-Introduction/03-defining-data/assignment.md b/translations/es/1-Introduction/03-defining-data/assignment.md index 638fb32c..bd28d3b8 100644 --- a/translations/es/1-Introduction/03-defining-data/assignment.md +++ b/translations/es/1-Introduction/03-defining-data/assignment.md @@ -1,12 +1,3 @@ - # Clasificación de Conjuntos de Datos ## Instrucciones diff --git a/translations/es/1-Introduction/04-stats-and-probability/README.md b/translations/es/1-Introduction/04-stats-and-probability/README.md index 455c45f8..9004df62 100644 --- a/translations/es/1-Introduction/04-stats-and-probability/README.md +++ b/translations/es/1-Introduction/04-stats-and-probability/README.md @@ -1,12 +1,3 @@ - # Una Breve Introducción a Estadística y Probabilidad |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/04-Statistics-Probability.png)| @@ -64,7 +55,7 @@ Para ayudarnos a entender la distribución de los datos, es útil hablar de **cu Gráficamente, podemos representar la relación entre la mediana y los cuartiles en un diagrama llamado **diagrama de caja**: -Explicación del Diagrama de Caja +Explicación del Diagrama de Caja Aquí también calculamos el **rango intercuartílico** IQR=Q3-Q1, y los llamados **valores atípicos** - valores que están fuera de los límites [Q1-1.5*IQR, Q3+1.5*IQR]. diff --git a/translations/es/1-Introduction/04-stats-and-probability/assignment.md b/translations/es/1-Introduction/04-stats-and-probability/assignment.md index ebd1d3f0..fae8e561 100644 --- a/translations/es/1-Introduction/04-stats-and-probability/assignment.md +++ b/translations/es/1-Introduction/04-stats-and-probability/assignment.md @@ -1,12 +1,3 @@ - # Pequeño Estudio sobre Diabetes En esta tarea, trabajaremos con un pequeño conjunto de datos de pacientes con diabetes tomado de [aquí](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html). diff --git a/translations/es/1-Introduction/README.md b/translations/es/1-Introduction/README.md index 91af5af9..bb02b60c 100644 --- a/translations/es/1-Introduction/README.md +++ b/translations/es/1-Introduction/README.md @@ -1,12 +1,3 @@ - # Introducción a la Ciencia de Datos ![datos en acción](../../../translated_images/es/data.48e22bb7617d8d92.webp) diff --git a/translations/es/2-Working-With-Data/05-relational-databases/README.md b/translations/es/2-Working-With-Data/05-relational-databases/README.md index 2aa90595..0b7868d8 100644 --- a/translations/es/2-Working-With-Data/05-relational-databases/README.md +++ b/translations/es/2-Working-With-Data/05-relational-databases/README.md @@ -1,12 +1,3 @@ - # Trabajando con Datos: Bases de Datos Relacionales |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/05-RelationalData.png)| diff --git a/translations/es/2-Working-With-Data/05-relational-databases/assignment.md b/translations/es/2-Working-With-Data/05-relational-databases/assignment.md index c9fd600c..1cb26353 100644 --- a/translations/es/2-Working-With-Data/05-relational-databases/assignment.md +++ b/translations/es/2-Working-With-Data/05-relational-databases/assignment.md @@ -1,12 +1,3 @@ - # Mostrando datos de aeropuertos Se te ha proporcionado una [base de datos](https://raw.githubusercontent.com/Microsoft/Data-Science-For-Beginners/main/2-Working-With-Data/05-relational-databases/airports.db) basada en [SQLite](https://sqlite.org/index.html) que contiene información sobre aeropuertos. El esquema se muestra a continuación. Utilizarás la [extensión SQLite](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum) en [Visual Studio Code](https://code.visualstudio.com?WT.mc_id=academic-77958-bethanycheum) para mostrar información sobre los aeropuertos de diferentes ciudades. diff --git a/translations/es/2-Working-With-Data/06-non-relational/README.md b/translations/es/2-Working-With-Data/06-non-relational/README.md index a9de2f5e..1e3672a7 100644 --- a/translations/es/2-Working-With-Data/06-non-relational/README.md +++ b/translations/es/2-Working-With-Data/06-non-relational/README.md @@ -1,12 +1,3 @@ - # Trabajando con Datos: Datos No Relacionales |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/06-NoSQL.png)| diff --git a/translations/es/2-Working-With-Data/06-non-relational/assignment.md b/translations/es/2-Working-With-Data/06-non-relational/assignment.md index fc73a212..44c0e746 100644 --- a/translations/es/2-Working-With-Data/06-non-relational/assignment.md +++ b/translations/es/2-Working-With-Data/06-non-relational/assignment.md @@ -1,12 +1,3 @@ - # Beneficios de Soda ## Instrucciones diff --git a/translations/es/2-Working-With-Data/07-python/README.md b/translations/es/2-Working-With-Data/07-python/README.md index 888315d5..d088de9a 100644 --- a/translations/es/2-Working-With-Data/07-python/README.md +++ b/translations/es/2-Working-With-Data/07-python/README.md @@ -1,12 +1,3 @@ - # Trabajando con Datos: Python y la Biblioteca Pandas | ![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/07-WorkWithPython.png) | diff --git a/translations/es/2-Working-With-Data/07-python/assignment.md b/translations/es/2-Working-With-Data/07-python/assignment.md index fdac9fc8..ce935de2 100644 --- a/translations/es/2-Working-With-Data/07-python/assignment.md +++ b/translations/es/2-Working-With-Data/07-python/assignment.md @@ -1,12 +1,3 @@ - # Asignación para Procesamiento de Datos en Python En esta asignación, te pediremos que desarrolles el código que hemos comenzado a crear en nuestros desafíos. La asignación consta de dos partes: diff --git a/translations/es/2-Working-With-Data/08-data-preparation/README.md b/translations/es/2-Working-With-Data/08-data-preparation/README.md index bb135142..73d80c33 100644 --- a/translations/es/2-Working-With-Data/08-data-preparation/README.md +++ b/translations/es/2-Working-With-Data/08-data-preparation/README.md @@ -1,12 +1,3 @@ - # Trabajando con Datos: Preparación de Datos |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/08-DataPreparation.png)| diff --git a/translations/es/2-Working-With-Data/08-data-preparation/assignment.md b/translations/es/2-Working-With-Data/08-data-preparation/assignment.md index d24803a6..d5251a6a 100644 --- a/translations/es/2-Working-With-Data/08-data-preparation/assignment.md +++ b/translations/es/2-Working-With-Data/08-data-preparation/assignment.md @@ -1,12 +1,3 @@ - # Evaluando Datos de un Formulario Un cliente ha estado probando un [formulario pequeño](../../../../2-Working-With-Data/08-data-preparation/index.html) para recopilar algunos datos básicos sobre su base de clientes. Han traído sus hallazgos para que valides los datos que han recopilado. Puedes abrir la página `index.html` en el navegador para echar un vistazo al formulario. diff --git a/translations/es/2-Working-With-Data/README.md b/translations/es/2-Working-With-Data/README.md index 346f41dc..15aa3d41 100644 --- a/translations/es/2-Working-With-Data/README.md +++ b/translations/es/2-Working-With-Data/README.md @@ -1,12 +1,3 @@ - # Trabajando con Datos ![amor por los datos](../../../translated_images/es/data-love.a22ef29e6742c852.webp) diff --git a/translations/es/3-Data-Visualization/09-visualization-quantities/README.md b/translations/es/3-Data-Visualization/09-visualization-quantities/README.md index 48455a37..8abcadd2 100644 --- a/translations/es/3-Data-Visualization/09-visualization-quantities/README.md +++ b/translations/es/3-Data-Visualization/09-visualization-quantities/README.md @@ -1,12 +1,3 @@ - # Visualizando Cantidades |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/09-Visualizing-Quantities.png)| diff --git a/translations/es/3-Data-Visualization/09-visualization-quantities/assignment.md b/translations/es/3-Data-Visualization/09-visualization-quantities/assignment.md index d70d4a2c..4f9bebe5 100644 --- a/translations/es/3-Data-Visualization/09-visualization-quantities/assignment.md +++ b/translations/es/3-Data-Visualization/09-visualization-quantities/assignment.md @@ -1,12 +1,3 @@ - # Líneas, Dispersión y Barras ## Instrucciones diff --git a/translations/es/3-Data-Visualization/10-visualization-distributions/README.md b/translations/es/3-Data-Visualization/10-visualization-distributions/README.md index 6e1c1e63..0ff090fc 100644 --- a/translations/es/3-Data-Visualization/10-visualization-distributions/README.md +++ b/translations/es/3-Data-Visualization/10-visualization-distributions/README.md @@ -1,12 +1,3 @@ - # Visualizando Distribuciones |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/10-Visualizing-Distributions.png)| diff --git a/translations/es/3-Data-Visualization/10-visualization-distributions/assignment.md b/translations/es/3-Data-Visualization/10-visualization-distributions/assignment.md index 39393be8..2e8231a1 100644 --- a/translations/es/3-Data-Visualization/10-visualization-distributions/assignment.md +++ b/translations/es/3-Data-Visualization/10-visualization-distributions/assignment.md @@ -1,12 +1,3 @@ - # Aplica tus habilidades ## Instrucciones diff --git a/translations/es/3-Data-Visualization/11-visualization-proportions/README.md b/translations/es/3-Data-Visualization/11-visualization-proportions/README.md index e9016a04..3ec083f2 100644 --- a/translations/es/3-Data-Visualization/11-visualization-proportions/README.md +++ b/translations/es/3-Data-Visualization/11-visualization-proportions/README.md @@ -1,12 +1,3 @@ - # Visualizando Proporciones |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/11-Visualizing-Proportions.png)| diff --git a/translations/es/3-Data-Visualization/11-visualization-proportions/assignment.md b/translations/es/3-Data-Visualization/11-visualization-proportions/assignment.md index c528d081..5c607e24 100644 --- a/translations/es/3-Data-Visualization/11-visualization-proportions/assignment.md +++ b/translations/es/3-Data-Visualization/11-visualization-proportions/assignment.md @@ -1,12 +1,3 @@ - # Pruébalo en Excel ## Instrucciones diff --git a/translations/es/3-Data-Visualization/12-visualization-relationships/README.md b/translations/es/3-Data-Visualization/12-visualization-relationships/README.md index be7cd08f..14f9cd2a 100644 --- a/translations/es/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/es/3-Data-Visualization/12-visualization-relationships/README.md @@ -1,12 +1,3 @@ - # Visualizando Relaciones: Todo Sobre la Miel 🍯 |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/12-Visualizing-Relationships.png)| diff --git a/translations/es/3-Data-Visualization/12-visualization-relationships/assignment.md b/translations/es/3-Data-Visualization/12-visualization-relationships/assignment.md index 90ed117c..ea13c135 100644 --- a/translations/es/3-Data-Visualization/12-visualization-relationships/assignment.md +++ b/translations/es/3-Data-Visualization/12-visualization-relationships/assignment.md @@ -1,12 +1,3 @@ - # Sumérgete en la colmena ## Instrucciones diff --git a/translations/es/3-Data-Visualization/13-meaningful-visualizations/README.md b/translations/es/3-Data-Visualization/13-meaningful-visualizations/README.md index d1ce042e..c2683c39 100644 --- a/translations/es/3-Data-Visualization/13-meaningful-visualizations/README.md +++ b/translations/es/3-Data-Visualization/13-meaningful-visualizations/README.md @@ -1,12 +1,3 @@ - # Creando Visualizaciones Significativas |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/13-MeaningfulViz.png)| diff --git a/translations/es/3-Data-Visualization/13-meaningful-visualizations/assignment.md b/translations/es/3-Data-Visualization/13-meaningful-visualizations/assignment.md index 055888cc..329d21dd 100644 --- a/translations/es/3-Data-Visualization/13-meaningful-visualizations/assignment.md +++ b/translations/es/3-Data-Visualization/13-meaningful-visualizations/assignment.md @@ -1,12 +1,3 @@ - # Crea tu propia visualización personalizada ## Instrucciones diff --git a/translations/es/3-Data-Visualization/13-meaningful-visualizations/solution/README.md b/translations/es/3-Data-Visualization/13-meaningful-visualizations/solution/README.md index d8c6f2f6..2fb6133c 100644 --- a/translations/es/3-Data-Visualization/13-meaningful-visualizations/solution/README.md +++ b/translations/es/3-Data-Visualization/13-meaningful-visualizations/solution/README.md @@ -1,12 +1,3 @@ - # Proyecto de visualización de datos Dangerous Liaisons Para comenzar, asegúrate de tener NPM y Node funcionando en tu máquina. Instala las dependencias (npm install) y luego ejecuta el proyecto localmente (npm run serve): diff --git a/translations/es/3-Data-Visualization/13-meaningful-visualizations/starter/README.md b/translations/es/3-Data-Visualization/13-meaningful-visualizations/starter/README.md index 2dcd3923..2fb6133c 100644 --- a/translations/es/3-Data-Visualization/13-meaningful-visualizations/starter/README.md +++ b/translations/es/3-Data-Visualization/13-meaningful-visualizations/starter/README.md @@ -1,12 +1,3 @@ - # Proyecto de visualización de datos Dangerous Liaisons Para comenzar, asegúrate de tener NPM y Node funcionando en tu máquina. Instala las dependencias (npm install) y luego ejecuta el proyecto localmente (npm run serve): diff --git a/translations/es/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/es/3-Data-Visualization/R/09-visualization-quantities/README.md index f2586dbc..46678dea 100644 --- a/translations/es/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/es/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -1,12 +1,3 @@ - # Visualizando Cantidades |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/sketchnotes/09-Visualizing-Quantities.png)| |:---:| diff --git a/translations/es/3-Data-Visualization/R/09-visualization-quantities/assignment.md b/translations/es/3-Data-Visualization/R/09-visualization-quantities/assignment.md index 60c1c707..adc56bb7 100644 --- a/translations/es/3-Data-Visualization/R/09-visualization-quantities/assignment.md +++ b/translations/es/3-Data-Visualization/R/09-visualization-quantities/assignment.md @@ -1,12 +1,3 @@ - # Líneas, Dispersión y Barras ## Instrucciones diff --git a/translations/es/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/es/3-Data-Visualization/R/10-visualization-distributions/README.md index e9d36bee..1f48f2ee 100644 --- a/translations/es/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/es/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -1,12 +1,3 @@ - # Visualizando Distribuciones |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/sketchnotes/10-Visualizing-Distributions.png)| diff --git a/translations/es/3-Data-Visualization/R/10-visualization-distributions/assignment.md b/translations/es/3-Data-Visualization/R/10-visualization-distributions/assignment.md index 4eff1d4c..f2ee5f2d 100644 --- a/translations/es/3-Data-Visualization/R/10-visualization-distributions/assignment.md +++ b/translations/es/3-Data-Visualization/R/10-visualization-distributions/assignment.md @@ -1,12 +1,3 @@ - # Aplica tus habilidades ## Instrucciones diff --git a/translations/es/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/es/3-Data-Visualization/R/11-visualization-proportions/README.md index 32fce313..f328c884 100644 --- a/translations/es/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/es/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -1,12 +1,3 @@ - # Visualizando Proporciones |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/11-Visualizing-Proportions.png)| diff --git a/translations/es/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/es/3-Data-Visualization/R/12-visualization-relationships/README.md index e457adec..91773264 100644 --- a/translations/es/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/es/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -1,12 +1,3 @@ - # Visualizando Relaciones: Todo Sobre la Miel 🍯 |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/12-Visualizing-Relationships.png)| diff --git a/translations/es/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/es/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 962f947a..d94a8e58 100644 --- a/translations/es/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/es/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -1,12 +1,3 @@ - # Creando Visualizaciones Significativas |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/13-MeaningfulViz.png)| diff --git a/translations/es/3-Data-Visualization/README.md b/translations/es/3-Data-Visualization/README.md index 2691e02a..4ada5a8f 100644 --- a/translations/es/3-Data-Visualization/README.md +++ b/translations/es/3-Data-Visualization/README.md @@ -1,12 +1,3 @@ - # Visualizaciones ![una abeja en una flor de lavanda](../../../translated_images/es/bee.0aa1d91132b12e3a.webp) diff --git a/translations/es/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/es/4-Data-Science-Lifecycle/14-Introduction/README.md index 1771dde7..105019a5 100644 --- a/translations/es/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/es/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -1,12 +1,3 @@ - # Introducción al Ciclo de Vida de la Ciencia de Datos |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/14-DataScience-Lifecycle.png)| diff --git a/translations/es/4-Data-Science-Lifecycle/14-Introduction/assignment.md b/translations/es/4-Data-Science-Lifecycle/14-Introduction/assignment.md index e4e989c9..21a10aaa 100644 --- a/translations/es/4-Data-Science-Lifecycle/14-Introduction/assignment.md +++ b/translations/es/4-Data-Science-Lifecycle/14-Introduction/assignment.md @@ -1,12 +1,3 @@ - # Evaluando un Conjunto de Datos Un cliente se ha acercado a tu equipo para solicitar ayuda en la investigación de los hábitos de gasto estacionales de los clientes de taxis en la ciudad de Nueva York. diff --git a/translations/es/4-Data-Science-Lifecycle/15-analyzing/README.md b/translations/es/4-Data-Science-Lifecycle/15-analyzing/README.md index 528e8dad..a0220fb8 100644 --- a/translations/es/4-Data-Science-Lifecycle/15-analyzing/README.md +++ b/translations/es/4-Data-Science-Lifecycle/15-analyzing/README.md @@ -1,12 +1,3 @@ - # El Ciclo de Vida de la Ciencia de Datos: Analizando |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/15-Analyzing.png)| diff --git a/translations/es/4-Data-Science-Lifecycle/15-analyzing/assignment.md b/translations/es/4-Data-Science-Lifecycle/15-analyzing/assignment.md index b56953d8..d044229c 100644 --- a/translations/es/4-Data-Science-Lifecycle/15-analyzing/assignment.md +++ b/translations/es/4-Data-Science-Lifecycle/15-analyzing/assignment.md @@ -1,12 +1,3 @@ - # Explorando respuestas Esta es una continuación de la [tarea](../14-Introduction/assignment.md) de la lección anterior, donde examinamos brevemente el conjunto de datos. Ahora profundizaremos más en los datos. diff --git a/translations/es/4-Data-Science-Lifecycle/16-communication/README.md b/translations/es/4-Data-Science-Lifecycle/16-communication/README.md index 14523354..6c377803 100644 --- a/translations/es/4-Data-Science-Lifecycle/16-communication/README.md +++ b/translations/es/4-Data-Science-Lifecycle/16-communication/README.md @@ -1,12 +1,3 @@ - # El Ciclo de Vida de la Ciencia de Datos: Comunicación |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/16-Communicating.png)| diff --git a/translations/es/4-Data-Science-Lifecycle/16-communication/assignment.md b/translations/es/4-Data-Science-Lifecycle/16-communication/assignment.md index 1ef9b0fe..27c376ff 100644 --- a/translations/es/4-Data-Science-Lifecycle/16-communication/assignment.md +++ b/translations/es/4-Data-Science-Lifecycle/16-communication/assignment.md @@ -1,12 +1,3 @@ - # Cuenta una historia ## Instrucciones diff --git a/translations/es/4-Data-Science-Lifecycle/README.md b/translations/es/4-Data-Science-Lifecycle/README.md index afca247f..1e7a43b6 100644 --- a/translations/es/4-Data-Science-Lifecycle/README.md +++ b/translations/es/4-Data-Science-Lifecycle/README.md @@ -1,12 +1,3 @@ - # El Ciclo de Vida de la Ciencia de Datos ![communication](../../../translated_images/es/communication.06d8e2a88d30d168.webp) diff --git a/translations/es/5-Data-Science-In-Cloud/17-Introduction/README.md b/translations/es/5-Data-Science-In-Cloud/17-Introduction/README.md index 76cc8dc6..c5fdb895 100644 --- a/translations/es/5-Data-Science-In-Cloud/17-Introduction/README.md +++ b/translations/es/5-Data-Science-In-Cloud/17-Introduction/README.md @@ -1,12 +1,3 @@ - # Introducción a la Ciencia de Datos en la Nube |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/17-DataScience-Cloud.png)| diff --git a/translations/es/5-Data-Science-In-Cloud/17-Introduction/assignment.md b/translations/es/5-Data-Science-In-Cloud/17-Introduction/assignment.md index 9dead6f2..accffe5f 100644 --- a/translations/es/5-Data-Science-In-Cloud/17-Introduction/assignment.md +++ b/translations/es/5-Data-Science-In-Cloud/17-Introduction/assignment.md @@ -1,12 +1,3 @@ - # Investigación de Mercado ## Instrucciones diff --git a/translations/es/5-Data-Science-In-Cloud/18-Low-Code/README.md b/translations/es/5-Data-Science-In-Cloud/18-Low-Code/README.md index 6df3d17b..bf5a8e1b 100644 --- a/translations/es/5-Data-Science-In-Cloud/18-Low-Code/README.md +++ b/translations/es/5-Data-Science-In-Cloud/18-Low-Code/README.md @@ -1,12 +1,3 @@ - # Ciencia de Datos en la Nube: El enfoque "Low code/No code" |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/18-DataScience-Cloud.png)| diff --git a/translations/es/5-Data-Science-In-Cloud/18-Low-Code/assignment.md b/translations/es/5-Data-Science-In-Cloud/18-Low-Code/assignment.md index e5702dfe..b1ba4b61 100644 --- a/translations/es/5-Data-Science-In-Cloud/18-Low-Code/assignment.md +++ b/translations/es/5-Data-Science-In-Cloud/18-Low-Code/assignment.md @@ -1,12 +1,3 @@ - # Proyecto de Ciencia de Datos Low code/No code en Azure ML ## Instrucciones diff --git a/translations/es/5-Data-Science-In-Cloud/19-Azure/README.md b/translations/es/5-Data-Science-In-Cloud/19-Azure/README.md index de7f99ee..a4761e39 100644 --- a/translations/es/5-Data-Science-In-Cloud/19-Azure/README.md +++ b/translations/es/5-Data-Science-In-Cloud/19-Azure/README.md @@ -1,12 +1,3 @@ - # Ciencia de Datos en la Nube: El camino del "Azure ML SDK" |![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/19-DataScience-Cloud.png)| diff --git a/translations/es/5-Data-Science-In-Cloud/19-Azure/assignment.md b/translations/es/5-Data-Science-In-Cloud/19-Azure/assignment.md index 08ffaff7..d3a51ad1 100644 --- a/translations/es/5-Data-Science-In-Cloud/19-Azure/assignment.md +++ b/translations/es/5-Data-Science-In-Cloud/19-Azure/assignment.md @@ -1,12 +1,3 @@ - # Proyecto de Ciencia de Datos usando Azure ML SDK ## Instrucciones diff --git a/translations/es/5-Data-Science-In-Cloud/README.md b/translations/es/5-Data-Science-In-Cloud/README.md index bfea3b51..e90b6dfe 100644 --- a/translations/es/5-Data-Science-In-Cloud/README.md +++ b/translations/es/5-Data-Science-In-Cloud/README.md @@ -1,12 +1,3 @@ - # Ciencia de Datos en la Nube ![cloud-picture](../../../translated_images/es/cloud-picture.f5526de3c6c6387b.webp) diff --git a/translations/es/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/es/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index ddb4d8db..3932a2ea 100644 --- a/translations/es/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/es/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -1,12 +1,3 @@ - # Ciencia de Datos en el Mundo Real | ![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-RealWorld.png) | diff --git a/translations/es/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/es/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index d91d6d6b..cb9060ed 100644 --- a/translations/es/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/es/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -1,12 +1,3 @@ - # Explora un Conjunto de Datos del Planetary Computer ## Instrucciones diff --git a/translations/es/6-Data-Science-In-Wild/README.md b/translations/es/6-Data-Science-In-Wild/README.md index 9c50f7de..5d8af8d3 100644 --- a/translations/es/6-Data-Science-In-Wild/README.md +++ b/translations/es/6-Data-Science-In-Wild/README.md @@ -1,12 +1,3 @@ - # Ciencia de Datos en el Mundo Real Aplicaciones prácticas de la ciencia de datos en diversas industrias. diff --git a/translations/es/AGENTS.md b/translations/es/AGENTS.md index 88e2be36..bd96b007 100644 --- a/translations/es/AGENTS.md +++ b/translations/es/AGENTS.md @@ -1,12 +1,3 @@ - # AGENTS.md ## Resumen del Proyecto diff --git a/translations/es/CODE_OF_CONDUCT.md b/translations/es/CODE_OF_CONDUCT.md index 3cb6ee2e..a5945897 100644 --- a/translations/es/CODE_OF_CONDUCT.md +++ b/translations/es/CODE_OF_CONDUCT.md @@ -1,12 +1,3 @@ - # Código de Conducta de Código Abierto de Microsoft Este proyecto ha adoptado el [Código de Conducta de Código Abierto de Microsoft](https://opensource.microsoft.com/codeofconduct/). diff --git a/translations/es/CONTRIBUTING.md b/translations/es/CONTRIBUTING.md index 0b89553a..2e415f5b 100644 --- a/translations/es/CONTRIBUTING.md +++ b/translations/es/CONTRIBUTING.md @@ -1,12 +1,3 @@ - # Contribuir a Ciencia de Datos para Principiantes ¡Gracias por tu interés en contribuir al currículo de Ciencia de Datos para Principiantes! Apreciamos las contribuciones de la comunidad. diff --git a/translations/es/INSTALLATION.md b/translations/es/INSTALLATION.md index c0d2614b..3c91c3ea 100644 --- a/translations/es/INSTALLATION.md +++ b/translations/es/INSTALLATION.md @@ -1,12 +1,3 @@ - # Guía de Instalación Esta guía te ayudará a configurar tu entorno para trabajar con el plan de estudios de Ciencia de Datos para Principiantes. diff --git a/translations/es/README.md b/translations/es/README.md index c3f96503..3f50d126 100644 --- a/translations/es/README.md +++ b/translations/es/README.md @@ -1,52 +1,43 @@ - -# Ciencia de Datos para Principiantes - Un Currículo - -[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198) - -[![GitHub license](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE) -[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/) -[![GitHub issues](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/) -[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/pulls/) -[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) - -[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/Data-Science-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/Data-Science-For-Beginners/watchers/) -[![GitHub forks](https://img.shields.io/github/forks/microsoft/Data-Science-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/Data-Science-For-Beginners/network/) -[![GitHub stars](https://img.shields.io/github/stars/microsoft/Data-Science-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/Data-Science-For-Beginners/stargazers/) +# Ciencia de Datos para Principiantes - Un Plan de Estudios + +[![Abrir en GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198) + +[![Licencia de GitHub](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE) +[![Colaboradores en GitHub](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/) +[![Issues en GitHub](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/) +[![Pull requests en GitHub](https://img.shields.io/github/issues-pr/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/pulls/) +[![Se aceptan PRs](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) + +[![Seguidores en GitHub](https://img.shields.io/github/watchers/microsoft/Data-Science-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/Data-Science-For-Beginners/watchers/) +[![Bifurcaciones en GitHub](https://img.shields.io/github/forks/microsoft/Data-Science-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/Data-Science-For-Beginners/network/) +[![Estrellas en GitHub](https://img.shields.io/github/stars/microsoft/Data-Science-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/Data-Science-For-Beginners/stargazers/) [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) +[![Foro de desarrolladores Microsoft Foundry](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) -Los Azure Cloud Advocates de Microsoft tienen el placer de ofrecer un currículo de 10 semanas, 20 lecciones, todo sobre Ciencia de Datos. Cada lección incluye cuestionarios previos y posteriores a la lección, instrucciones escritas para completar la lección, una solución y una tarea. Nuestra pedagogía basada en proyectos te permite aprender mientras construyes, una forma comprobada de que las nuevas habilidades "se queden". +Los Defensores de la Nube de Azure en Microsoft se complacen en ofrecer un plan de estudios de 10 semanas y 20 lecciones totalmente dedicado a la Ciencia de Datos. Cada lección incluye cuestionarios antes y después de la lección, instrucciones escritas para completar la lección, una solución y una tarea. Nuestra pedagogía basada en proyectos te permite aprender mientras construyes, una forma probada para que las nuevas habilidades "se queden". -**Muchas gracias a nuestros autores:** [Jasmine Greenaway](https://www.twitter.com/paladique), [Dmitry Soshnikov](http://soshnikov.com), [Nitya Narasimhan](https://twitter.com/nitya), [Jalen McGee](https://twitter.com/JalenMcG), [Jen Looper](https://twitter.com/jenlooper), [Maud Levy](https://twitter.com/maudstweets), [Tiffany Souterre](https://twitter.com/TiffanySouterre), [Christopher Harrison](https://www.twitter.com/geektrainer). +**Un gran agradecimiento a nuestros autores:** [Jasmine Greenaway](https://www.twitter.com/paladique), [Dmitry Soshnikov](http://soshnikov.com), [Nitya Narasimhan](https://twitter.com/nitya), [Jalen McGee](https://twitter.com/JalenMcG), [Jen Looper](https://twitter.com/jenlooper), [Maud Levy](https://twitter.com/maudstweets), [Tiffany Souterre](https://twitter.com/TiffanySouterre), [Christopher Harrison](https://www.twitter.com/geektrainer). -**🙏 Agradecimientos especiales 🙏 a nuestros autores, revisores y colaboradores de contenido [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** destacando a Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), +**🙏 Agradecimiento especial 🙏 a nuestros autores, revisores y colaboradores de contenido de [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** especialmente Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/es/00-Title.8af36cd35da1ac55.webp)| +|![Sketchnote por @sketchthedocs https://sketchthedocs.dev](../../translated_images/es/00-Title.8af36cd35da1ac55.webp)| |:---:| | Ciencia de Datos para Principiantes - _Sketchnote por [@nitya](https://twitter.com/nitya)_ | ### 🌐 Soporte Multilenguaje -#### Soportado vía GitHub Action (Automatizado y Siempre Actualizado) +#### Soportado mediante GitHub Action (Automatizado y siempre actualizado) -[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](./README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) +[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh-CN/README.md) | [Chinese (Traditional, Hong Kong)](../zh-HK/README.md) | [Chinese (Traditional, Macau)](../zh-MO/README.md) | [Chinese (Traditional, Taiwan)](../zh-TW/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../pt-BR/README.md) | [Portuguese (Portugal)](../pt-PT/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](./README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) > **¿Prefieres clonar localmente?** -> Este repositorio incluye traducciones en más de 50 idiomas, lo que aumenta significativamente el tamaño de la descarga. Para clonar sin las traducciones, usa sparse checkout: +> Este repositorio incluye más de 50 traducciones que aumentan significativamente el tamaño de la descarga. Para clonar sin traducciones, usa sparse checkout: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/Data-Science-For-Beginners.git > cd Data-Science-For-Beginners @@ -55,21 +46,21 @@ Los Azure Cloud Advocates de Microsoft tienen el placer de ofrecer un currículo > Esto te da todo lo que necesitas para completar el curso con una descarga mucho más rápida. -**Si deseas soportar idiomas adicionales de traducción estos están listados [aquí](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Si deseas que se soporten idiomas adicionales, están listados [aquí](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** #### Únete a Nuestra Comunidad [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Tenemos una serie de aprendizaje con IA en Discord, aprende más y únete en [Learn with AI Series](https://aka.ms/learnwithai/discord) del 18 al 30 de septiembre de 2025. Obtendrás consejos y trucos para usar GitHub Copilot para Ciencia de Datos. +Tenemos una serie en Discord llamada Aprende con IA en curso, aprende más y únete en [Serie Aprende con IA](https://aka.ms/learnwithai/discord) del 18 al 30 de septiembre de 2025. Recibirás consejos y trucos para usar GitHub Copilot en Ciencia de Datos. -![Learn with AI series](../../../../translated_images/es/1.2b28cdc6205e26fe.webp) +![Serie Aprende con IA](../../translated_images/es/1.2b28cdc6205e26fe.webp) # ¿Eres estudiante? Comienza con los siguientes recursos: -- [Página del Hub para Estudiantes](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) En esta página encontrarás recursos para principiantes, paquetes para estudiantes e incluso formas de obtener un cupón de certificación gratis. Esta es una página que querrás marcar y revisar de vez en cuando ya que cambiamos contenido al menos mensualmente. -- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Únete a una comunidad global de embajadores estudiantiles, esta podría ser tu puerta de entrada a Microsoft. +- [Página del Centro de Estudiantes](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) En esta página encontrarás recursos para principiantes, paquetes para estudiantes e incluso formas de obtener un cupón para certificación gratuita. Esta es una página que querrás marcar y revisar de vez en cuando ya que actualizamos el contenido al menos mensualmente. +- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Únete a una comunidad global de embajadores estudiantes, esta podría ser tu entrada a Microsoft. # Comenzando @@ -79,23 +70,23 @@ Comienza con los siguientes recursos: - **[Guía de Uso](USAGE.md)** - Ejemplos y flujos de trabajo comunes - **[Resolución de Problemas](TROUBLESHOOTING.md)** - Soluciones a problemas comunes - **[Guía para Contribuir](CONTRIBUTING.md)** - Cómo contribuir a este proyecto -- **[Para Profesores](for-teachers.md)** - Orientación para enseñanza y recursos para el aula +- **[Para Profesores](for-teachers.md)** - Guía para enseñanza y recursos para el aula ## 👨‍🎓 Para Estudiantes -> **Principiantes Completos**: ¿Nuevo en ciencia de datos? Comienza con nuestros [ejemplos para principiantes](examples/README.md)! Estos ejemplos simples y bien comentados te ayudarán a entender lo básico antes de sumergirte en el currículo completo. -> **[Estudiantes](https://aka.ms/student-page)**: para usar este currículo por tu cuenta, haz un fork de todo el repositorio y completa los ejercicios por tu cuenta, empezando con un cuestionario previo a la lección. Luego lee la lección y completa el resto de las actividades. Intenta crear los proyectos comprendiendo las lecciones en lugar de copiar el código solución; sin embargo, ese código está disponible en las carpetas /solutions en cada lección orientada a proyectos. Otra idea sería formar un grupo de estudio con amigos y revisar el contenido juntos. Para estudio adicional, recomendamos [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum). +> **Principiantes Completos**: ¿Nuevo en ciencia de datos? Comienza con nuestros [ejemplos amigables para principiantes](examples/README.md)! Estos ejemplos simples y bien comentados te ayudarán a comprender lo básico antes de adentrarte en el plan completo. +> **[Estudiantes](https://aka.ms/student-page)**: para usar este plan de estudios por tu cuenta, haz un fork de todo el repositorio y completa los ejercicios comenzando con un cuestionario previo a la lección. Luego lee la lección y completa el resto de las actividades. Trata de crear los proyectos comprendiendo las lecciones en lugar de copiar el código solución; sin embargo, ese código está disponible en las carpetas /solutions en cada lección orientada a proyectos. Otra idea es formar un grupo de estudio con amigos y revisar el contenido juntos. Para estudio adicional, recomendamos [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum). **Inicio rápido:** 1. Revisa la [Guía de Instalación](INSTALLATION.md) para configurar tu entorno -2. Revisa la [Guía de Uso](USAGE.md) para aprender a trabajar con el currículo +2. Revisa la [Guía de Uso](USAGE.md) para aprender a trabajar con el plan de estudios 3. Comienza con la Lección 1 y avanza secuencialmente -4. Únete a nuestra [comunidad en Discord](https://aka.ms/ds4beginners/discord) para apoyo +4. Únete a nuestra [comunidad de Discord](https://aka.ms/ds4beginners/discord) para soporte ## 👩‍🏫 Para Profesores -> **Profesores**: hemos [incluido algunas sugerencias](for-teachers.md) sobre cómo usar este currículo. ¡Nos encantaría recibir sus comentarios [en nuestro foro de discusión](https://github.com/microsoft/Data-Science-For-Beginners/discussions)! - +> **Profesores**: hemos [incluido algunas sugerencias](for-teachers.md) sobre cómo usar este plan de estudios. ¡Nos encantaría recibir sus comentarios [en nuestro foro de discusión](https://github.com/microsoft/Data-Science-For-Beginners/discussions)! ## Conoce al Equipo + [![Video promocional](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "Video promocional") **Gif por** [Mohit Jaisal](https://www.linkedin.com/in/mohitjaisal) @@ -104,107 +95,105 @@ Comienza con los siguientes recursos: ## Pedagogía -Hemos elegido dos principios pedagógicos al construir este currículo: asegurar que sea basado en proyectos y que incluya cuestionarios frecuentes. Al final de esta serie, los estudiantes habrán aprendido principios básicos de la ciencia de datos, incluyendo conceptos éticos, preparación de datos, diferentes formas de trabajar con datos, visualización de datos, análisis de datos, casos de uso reales de la ciencia de datos, y más. +Hemos elegido dos principios pedagógicos al construir este plan de estudios: asegurarnos de que sea basado en proyectos y que incluya cuestionarios frecuentes. Al final de esta serie, los estudiantes habrán aprendido los principios básicos de la ciencia de datos, incluidos conceptos éticos, preparación de datos, diferentes formas de trabajar con datos, visualización de datos, análisis de datos, casos de uso del mundo real de la ciencia de datos y más. -Además, un cuestionario de baja presión antes de una clase establece la intención del estudiante hacia el aprendizaje de un tema, mientras que un segundo cuestionario después de la clase asegura una mayor retención. Este currículo fue diseñado para ser flexible y divertido y puede tomarse en su totalidad o en parte. Los proyectos comienzan pequeños y se vuelven cada vez más complejos al final del ciclo de 10 semanas. +Además, un cuestionario de baja presión antes de una clase establece la intención del estudiante hacia el aprendizaje de un tema, mientras que un segundo cuestionario después de la clase asegura una mayor retención. Este plan de estudios fue diseñado para ser flexible y divertido y puede tomarse en su totalidad o en parte. Los proyectos comienzan pequeños y se vuelven cada vez más complejos al final del ciclo de 10 semanas. -> Encuentra nuestro [Código de Conducta](CODE_OF_CONDUCT.md), [Contribuciones](CONTRIBUTING.md), [Traducción](TRANSLATIONS.md) y directrices. ¡Agradecemos tus comentarios constructivos! +> Encuentra nuestro [Código de Conducta](CODE_OF_CONDUCT.md), pautas de [Contribución](CONTRIBUTING.md), [Traducción](TRANSLATIONS.md). ¡Agradecemos tus comentarios constructivos! ## Cada lección incluye: - Sketchnote opcional -- Video complementario opcional -- Cuestionario de calentamiento previo a la lección +- Video suplementario opcional +- Cuestionario previo a la lección para calentamiento - Lección escrita - Para lecciones basadas en proyectos, guías paso a paso sobre cómo construir el proyecto -- Chequeos de conocimiento +- Verificaciones de conocimiento - Un desafío -- Lectura complementaria +- Lectura suplementaria - Tarea - [Cuestionario posterior a la lección](https://ff-quizzes.netlify.app/en/) -> **Una nota sobre los cuestionarios**: Todos los cuestionarios están contenidos en la carpeta Quiz-App, con un total de 40 cuestionarios de tres preguntas cada uno. Están enlazados desde dentro de las lecciones, pero la aplicación de cuestionario puede ejecutarse localmente o desplegarse en Azure; sigue las instrucciones en la carpeta `quiz-app`. Se están localizando gradualmente. +> **Una nota sobre los cuestionarios**: Todos los cuestionarios están contenidos en la carpeta Quiz-App, con un total de 40 cuestionarios de tres preguntas cada uno. Están vinculados dentro de las lecciones, pero la aplicación de cuestionarios puede ejecutarse localmente o desplegarse en Azure; sigue las instrucciones en la carpeta `quiz-app`. Están siendo localizados gradualmente. -## 🎓 Ejemplos para Principiantes +## 🎓 Ejemplos Amigables para Principiantes **¿Nuevo en Ciencia de Datos?** Hemos creado un [directorio de ejemplos](examples/README.md) especial con código simple y bien comentado para ayudarte a comenzar: - 🌟 **Hola Mundo** - Tu primer programa de ciencia de datos -- 📂 **Cargar Datos** - Aprende a leer y explorar conjuntos de datos +- 📂 **Cargando Datos** - Aprende a leer y explorar conjuntos de datos - 📊 **Análisis Simple** - Calcula estadísticas y encuentra patrones - 📈 **Visualización Básica** - Crea gráficos y diagramas -- 🔬 **Proyecto del Mundo Real** - Flujo de trabajo completo desde el inicio hasta el fin +- 🔬 **Proyecto del Mundo Real** - Flujo de trabajo completo de inicio a fin -Cada ejemplo incluye comentarios detallados explicando cada paso, ¡perfecto para principiantes absolutos! +Cada ejemplo incluye comentarios detallados que explican cada paso, ¡perfecto para principiantes absolutos! 👉 **[Comienza con los ejemplos](examples/README.md)** 👈 ## Lecciones - -|![ Sketchnote por @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/es/00-Roadmap.4905d6567dff4753.webp)| +|![ Sketchnote por @sketchthedocs https://sketchthedocs.dev](../../translated_images/es/00-Roadmap.4905d6567dff4753.webp)| |:---:| | Ciencia de Datos para Principiantes: Hoja de Ruta - _Sketchnote por [@nitya](https://twitter.com/nitya)_ | - -| Número de Lección | Tema | Agrupación de Lección | Objetivos de Aprendizaje | Lección Enlazada | Autor | -| :---------------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: | -| 01 | Definiendo Ciencia de Datos | [Introducción](1-Introduction/README.md) | Aprende los conceptos básicos detrás de la ciencia de datos y cómo se relaciona con inteligencia artificial, aprendizaje automático y big data. | [lección](1-Introduction/01-defining-data-science/README.md) [video](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) | -| 02 | Ética en Ciencia de Datos | [Introducción](1-Introduction/README.md) | Conceptos, desafíos y marcos de la ética en datos. | [lección](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | +| Número de Lección | Tema | Agrupación de Lección | Objetivos de Aprendizaje | Lección Vinculada | Autor | +| :--------------: | :---------------------------------------: | :------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: | +| 01 | Definiendo Ciencia de Datos | [Introducción](1-Introduction/README.md) | Aprende los conceptos básicos de la ciencia de datos y cómo se relaciona con inteligencia artificial, aprendizaje automático y big data. | [lección](1-Introduction/01-defining-data-science/README.md) [video](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) | +| 02 | Ética en Ciencia de Datos | [Introducción](1-Introduction/README.md) | Conceptos, desafíos y marcos éticos en datos. | [lección](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | | 03 | Definiendo Datos | [Introducción](1-Introduction/README.md) | Cómo se clasifican los datos y sus fuentes comunes. | [lección](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) | -| 04 | Introducción a Estadística y Probabilidad | [Introducción](1-Introduction/README.md) | Técnicas matemáticas de probabilidad y estadística para entender los datos. | [lección](1-Introduction/04-stats-and-probability/README.md) [video](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) | -| 05 | Trabajando con Datos Relacionales | [Trabajando con Datos](2-Working-With-Data/README.md) | Introducción a datos relacionales y los conceptos básicos de exploración y análisis de datos relacionales con el Lenguaje de Consulta Estructurado, también conocido como SQL (pronunciado “see-quell”). | [lección](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | | -| 06 | Trabajando con Datos NoSQL | [Trabajando con Datos](2-Working-With-Data/README.md) | Introducción a datos no relacionales, sus diversos tipos y los fundamentos de exploración y análisis de bases de datos de documentos. | [lección](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)| -| 07 | Trabajando con Python | [Trabajando con Datos](2-Working-With-Data/README.md) | Conceptos básicos de uso de Python para la exploración de datos con bibliotecas como Pandas. Se recomienda un entendimiento fundamental de programación en Python. | [lección](2-Working-With-Data/07-python/README.md) [video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) | -| 08 | Preparación de Datos | [Trabajando con Datos](2-Working-With-Data/README.md) | Temas sobre técnicas de datos para limpiar y transformar los datos para manejar desafíos de datos faltantes, inexactos o incompletos. | [lección](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) | -| 09 | Visualizando Cantidades | [Visualización de Datos](3-Data-Visualization/README.md) | Aprende a usar Matplotlib para visualizar datos de aves 🦆 | [lección](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) | -| 10 | Visualizando Distribuciones de Datos | [Visualización de Datos](3-Data-Visualization/README.md) | Visualizando observaciones y tendencias dentro de un intervalo. | [lección](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) | -| 11 | Visualizando Proporciones | [Visualización de Datos](3-Data-Visualization/README.md) | Visualizando porcentajes discretos y agrupados. | [lección](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) | -| 12 | Visualizando Relaciones | [Visualización de Datos](3-Data-Visualization/README.md) | Visualizando conexiones y correlaciones entre conjuntos de datos y sus variables. | [lección](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) | -| 13 | Visualizaciones Significativas | [Visualización de Datos](3-Data-Visualization/README.md) | Técnicas y consejos para hacer tus visualizaciones valiosas para la resolución efectiva de problemas y obtención de insights. | [lección](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) | -| 14 | Introducción al ciclo de vida de la Ciencia de Datos | [Ciclo de Vida](4-Data-Science-Lifecycle/README.md) | Introducción al ciclo de vida de la ciencia de datos y su primer paso de adquisición y extracción de datos. | [lección](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) | -| 15 | Analizando | [Ciclo de Vida](4-Data-Science-Lifecycle/README.md) | Esta fase del ciclo de vida de la ciencia de datos se enfoca en técnicas para analizar datos. | [lección](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | | -| 16 | Comunicación | [Ciclo de Vida](4-Data-Science-Lifecycle/README.md) | Esta fase del ciclo de vida de la ciencia de datos se enfoca en presentar los insights de los datos de una manera que facilite la comprensión a los tomadores de decisiones. | [lección](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | | +| 04 | Introducción a Estadística y Probabilidad | [Introducción](1-Introduction/README.md) | Técnicas matemáticas de probabilidad y estadística para entender datos. | [lección](1-Introduction/04-stats-and-probability/README.md) [video](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) | +| 05 | Trabajando con Datos Relacionales | [Trabajando con Datos](2-Working-With-Data/README.md) | Introducción a datos relacionales y las bases del análisis y exploración de datos relacionales con Structured Query Language, también conocido como SQL (pronunciado “see-quell”). | [lección](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | +| 06 | Trabajando con Datos NoSQL | [Trabajando con Datos](2-Working-With-Data/README.md) | Introducción a datos no relacionales, sus tipos y lo básico para explorar y analizar bases de datos de documentos. | [lección](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)| +| 07 | Trabajando con Python | [Trabajando con Datos](2-Working-With-Data/README.md) | Bases del uso de Python para exploración de datos con bibliotecas como Pandas. Se recomienda comprensión fundamental de programación en Python. | [lección](2-Working-With-Data/07-python/README.md) [video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) | +| 08 | Preparación de Datos | [Trabajando con Datos](2-Working-With-Data/README.md) | Temas sobre técnicas para limpiar y transformar datos para manejar desafíos de datos faltantes, inexactos o incompletos. | [lección](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) | +| 09 | Visualización de Cantidades | [Visualización de Datos](3-Data-Visualization/README.md) | Aprende a usar Matplotlib para visualizar datos de aves 🦆 | [lección](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) | +| 10 | Visualización de Distribuciones de Datos | [Visualización de Datos](3-Data-Visualization/README.md) | Visualización de observaciones y tendencias dentro de un intervalo. | [lección](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) | +| 11 | Visualización de Proporciones | [Visualización de Datos](3-Data-Visualization/README.md) | Visualización de porcentajes discretos y agrupados. | [lección](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) | +| 12 | Visualización de Relaciones | [Visualización de Datos](3-Data-Visualization/README.md) | Visualización de conexiones y correlaciones entre conjuntos de datos y sus variables. | [lección](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) | +| 13 | Visualizaciones Significativas | [Visualización de Datos](3-Data-Visualization/README.md) | Técnicas y guía para hacer visualizaciones valiosas para una resolución de problemas efectiva y obtener insights. | [lección](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) | +| 14 | Introducción al ciclo de vida de Ciencia de Datos | [Ciclo de Vida](4-Data-Science-Lifecycle/README.md) | Introducción al ciclo de vida de ciencia de datos y su primer paso que es adquirir y extraer datos. | [lección](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) | +| 15 | Analizando | [Ciclo de Vida](4-Data-Science-Lifecycle/README.md) | Esta fase del ciclo de vida de ciencia de datos se enfoca en técnicas para analizar datos. | [lección](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | +| 16 | Comunicación | [Ciclo de Vida](4-Data-Science-Lifecycle/README.md) | Esta fase del ciclo de vida de ciencia de datos se enfoca en presentar los insights de los datos de forma que facilite la comprensión de los tomadores de decisiones. | [lección](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | 17 | Ciencia de Datos en la Nube | [Datos en la Nube](5-Data-Science-In-Cloud/README.md) | Esta serie de lecciones introduce la ciencia de datos en la nube y sus beneficios. | [lección](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) y [Maud](https://twitter.com/maudstweets) | | 18 | Ciencia de Datos en la Nube | [Datos en la Nube](5-Data-Science-In-Cloud/README.md) | Entrenamiento de modelos usando herramientas Low Code. |[lección](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) y [Maud](https://twitter.com/maudstweets) | | 19 | Ciencia de Datos en la Nube | [Datos en la Nube](5-Data-Science-In-Cloud/README.md) | Despliegue de modelos con Azure Machine Learning Studio. | [lección](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) y [Maud](https://twitter.com/maudstweets) | -| 20 | Ciencia de Datos en el Mundo Real | [En el Mundo Real](6-Data-Science-In-Wild/README.md) | Proyectos impulsados por ciencia de datos en el mundo real. | [lección](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) | +| 20 | Ciencia de Datos en el Mundo Real | [En el Mundo](6-Data-Science-In-Wild/README.md) | Proyectos impulsados por ciencia de datos en el mundo real. | [lección](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) | ## GitHub Codespaces Sigue estos pasos para abrir este ejemplo en un Codespace: -1. Haz clic en el menú desplegable Code y selecciona la opción Open with Codespaces. -2. Selecciona + New codespace en la parte inferior del panel. +1. Haz clic en el menú desplegable Código y selecciona la opción Abrir con Codespaces. +2. Selecciona + Nuevo codespace en la parte inferior del panel. Para más información, consulta la [documentación de GitHub](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace). ## VSCode Remote - Containers Sigue estos pasos para abrir este repositorio en un contenedor usando tu máquina local y VSCode con la extensión VS Code Remote - Containers: -1. Si es la primera vez que usas un contenedor de desarrollo, asegúrate de que tu sistema cumple con los requisitos previos (es decir, tener Docker instalado) en [la documentación de inicio](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started). +1. Si es tu primera vez usando un contenedor de desarrollo, asegúrate que tu sistema cumple los requisitos previos (por ejemplo, tener Docker instalado) en [la documentación para comenzar](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started). -Para usar este repositorio, puedes abrirlo en un volumen Docker aislado: +Para usar este repositorio, puedes abrirlo tanto en un volumen Docker aislado: -**Nota**: Internamente, esto usará el comando Remote-Containers: **Clone Repository in Container Volume...** para clonar el código fuente en un volumen Docker en lugar del sistema de archivos local. [Los volúmenes](https://docs.docker.com/storage/volumes/) son el mecanismo preferido para persistir datos del contenedor. +**Nota**: Bajo el capó, esto usará el comando Remote-Containers: **Clonar repositorio en volumen de contenedor...** para clonar el código fuente en un volumen Docker en lugar de en el sistema de archivos local. [Los volúmenes](https://docs.docker.com/storage/volumes/) son el mecanismo preferido para persistir datos de contenedor. -O abre una versión clonada o descargada localmente del repositorio: +O abrir una versión clonada o descargada localmente: - Clona este repositorio en tu sistema de archivos local. -- Presiona F1 y selecciona el comando **Remote-Containers: Open Folder in Container...**. -- Selecciona la copia clonada de esta carpeta, espera a que el contenedor arranque y prueba. +- Presiona F1 y selecciona el comando **Remote-Containers: Abrir carpeta en contenedor...**. +- Selecciona la copia clonada de esta carpeta, espera a que el contenedor inicie, y pruébalo. ## Acceso sin conexión -Puedes ejecutar esta documentación sin conexión usando [Docsify](https://docsify.js.org/#/). Haz un fork de este repositorio, [instala Docsify](https://docsify.js.org/#/quickstart) en tu máquina local, luego en la carpeta raíz de este repositorio, escribe `docsify serve`. El sitio web se servirá en el puerto 3000 en tu local: `localhost:3000`. +Puedes ejecutar esta documentación sin conexión usando [Docsify](https://docsify.js.org/#/). Haz un fork de este repositorio, [instala Docsify](https://docsify.js.org/#/quickstart) en tu máquina local, luego en la carpeta raíz de este repo escribe `docsify serve`. El sitio se servirá en el puerto 3000 de tu localhost: `localhost:3000`. -> Nota, los notebooks no se renderizarán con Docsify, así que cuando necesites ejecutar un notebook, hazlo por separado en VS Code usando un kernel de Python. +> Nota, los notebooks no se visualizarán vía Docsify, por lo que cuando necesites ejecutar un notebook, hazlo por separado en VS Code con un kernel Python. -## Otros Currículos +## Otros Planes de Estudio -¡Nuestro equipo produce otros currículos! Mira: +¡Nuestro equipo produce otros planes de estudio! Revisa: ### LangChain -[![LangChain4j para Principiantes](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) +[![LangChain4j para principiantes](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) [![LangChain.js para Principiantes](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) --- @@ -225,7 +214,7 @@ Puedes ejecutar esta documentación sin conexión usando [Docsify](https://docsi --- -### Aprendizaje Básico +### Aprendizaje Central [![ML para Principiantes](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) [![Ciencia de Datos para Principiantes](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) [![IA para Principiantes](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) @@ -237,26 +226,26 @@ Puedes ejecutar esta documentación sin conexión usando [Docsify](https://docsi --- ### Serie Copilot -[![Copilot para Programación Emparejada con IA](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![Copilot para Programación por Pares con IA](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) [![Copilot para C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) [![Aventura Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) ## Obtener ayuda -**¿Tienes problemas?** Consulta nuestra [Guía de Solución de Problemas](TROUBLESHOOTING.md) para encontrar soluciones a problemas comunes. +**¿Tienes problemas?** Consulta nuestra [Guía de solución de problemas](TROUBLESHOOTING.md) para soluciones a problemas comunes. -Si te quedas atascado o tienes alguna pregunta sobre cómo crear aplicaciones de IA, únete a otros aprendices y desarrolladores experimentados en discusiones sobre MCP. Es una comunidad de apoyo donde las preguntas son bienvenidas y el conocimiento se comparte libremente. +Si te quedas atascado o tienes preguntas sobre cómo construir aplicaciones de IA. Únete a otros estudiantes y desarrolladores experimentados en discusiones sobre MCP. Es una comunidad de apoyo donde las preguntas son bienvenidas y el conocimiento se comparte libremente. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Si tienes comentarios sobre el producto o encuentras errores mientras desarrollas, visita: +Si tienes comentarios sobre el producto o errores mientras construyes visita: -[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) +[![Foro de desarrolladores Microsoft Foundry](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- -**Aviso legal**: -Este documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Aunque nos esforzamos por la precisión, tenga en cuenta que las traducciones automáticas pueden contener errores o inexactitudes. El documento original en su idioma nativo debe considerarse la fuente autorizada. Para información crítica, se recomienda una traducción profesional realizada por humanos. No nos hacemos responsables de malentendidos o interpretaciones erróneas derivadas del uso de esta traducción. +**Descargo de responsabilidad**: +Este documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Aunque nos esforzamos por la exactitud, tenga en cuenta que las traducciones automáticas pueden contener errores o inexactitudes. El documento original en su idioma nativo debe considerarse la fuente autorizada. Para información crítica, se recomienda la traducción profesional realizada por humanos. No nos hacemos responsables de ningún malentendido o interpretación errónea que pueda surgir del uso de esta traducción. \ No newline at end of file diff --git a/translations/es/SECURITY.md b/translations/es/SECURITY.md index eafd2d54..a2e63458 100644 --- a/translations/es/SECURITY.md +++ b/translations/es/SECURITY.md @@ -1,12 +1,3 @@ - ## Seguridad Microsoft se toma muy en serio la seguridad de nuestros productos y servicios de software, lo que incluye todos los repositorios de código fuente gestionados a través de nuestras organizaciones de GitHub, que incluyen [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin) y [nuestras organizaciones de GitHub](https://opensource.microsoft.com/). diff --git a/translations/es/SUPPORT.md b/translations/es/SUPPORT.md index 297770c3..90fccd78 100644 --- a/translations/es/SUPPORT.md +++ b/translations/es/SUPPORT.md @@ -1,12 +1,3 @@ - # Soporte ## Cómo reportar problemas y obtener ayuda diff --git a/translations/es/TROUBLESHOOTING.md b/translations/es/TROUBLESHOOTING.md index b94990e3..4ce3a28c 100644 --- a/translations/es/TROUBLESHOOTING.md +++ b/translations/es/TROUBLESHOOTING.md @@ -1,12 +1,3 @@ - # Guía de Solución de Problemas Esta guía ofrece soluciones a problemas comunes que podrías encontrar mientras trabajas con el currículo de Ciencia de Datos para Principiantes. diff --git a/translations/es/USAGE.md b/translations/es/USAGE.md index 536c5e93..c96c4451 100644 --- a/translations/es/USAGE.md +++ b/translations/es/USAGE.md @@ -1,12 +1,3 @@ - # Guía de Uso Esta guía proporciona ejemplos y flujos de trabajo comunes para utilizar el currículo de Ciencia de Datos para Principiantes. diff --git a/translations/es/docs/_sidebar.md b/translations/es/docs/_sidebar.md index 404043fd..3e1e35c1 100644 --- a/translations/es/docs/_sidebar.md +++ b/translations/es/docs/_sidebar.md @@ -1,12 +1,3 @@ - - Introducción - [Definiendo la Ciencia de Datos](../1-Introduction/01-defining-data-science/README.md) - [Ética de la Ciencia de Datos](../1-Introduction/02-ethics/README.md) diff --git a/translations/es/examples/README.md b/translations/es/examples/README.md index 24b86d11..6bef580b 100644 --- a/translations/es/examples/README.md +++ b/translations/es/examples/README.md @@ -1,12 +1,3 @@ - # Ejemplos de Ciencia de Datos para Principiantes ¡Bienvenido al directorio de ejemplos! Esta colección de ejemplos simples y bien comentados está diseñada para ayudarte a comenzar con la ciencia de datos, incluso si eres un principiante total. diff --git a/translations/es/for-teachers.md b/translations/es/for-teachers.md index 2adc6c91..0bfd2366 100644 --- a/translations/es/for-teachers.md +++ b/translations/es/for-teachers.md @@ -1,12 +1,3 @@ - ## Para Educadores ¿Te gustaría usar este plan de estudios en tu aula? ¡Siéntete libre de hacerlo! diff --git a/translations/es/quiz-app/README.md b/translations/es/quiz-app/README.md index 98abd071..568e8475 100644 --- a/translations/es/quiz-app/README.md +++ b/translations/es/quiz-app/README.md @@ -1,12 +1,3 @@ - # Cuestionarios Estos cuestionarios son los cuestionarios previos y posteriores a las lecciones del plan de estudios de ciencia de datos en https://aka.ms/datascience-beginners diff --git a/translations/es/sketchnotes/README.md b/translations/es/sketchnotes/README.md index b49f8017..a58e27b3 100644 --- a/translations/es/sketchnotes/README.md +++ b/translations/es/sketchnotes/README.md @@ -1,12 +1,3 @@ - Encuentra todas las notas visuales aquí. ## Créditos diff --git a/translations/fr/.co-op-translator.json b/translations/fr/.co-op-translator.json new file mode 100644 index 00000000..aa281171 --- /dev/null +++ b/translations/fr/.co-op-translator.json @@ -0,0 +1,422 @@ +{ + "1-Introduction/01-defining-data-science/README.md": { + "original_hash": "43212cc1ac137b7bb1dcfb37ca06b0f4", + "translation_date": "2025-10-25T18:33:03+00:00", + "source_file": "1-Introduction/01-defining-data-science/README.md", + "language_code": "fr" + }, + "1-Introduction/01-defining-data-science/assignment.md": { + "original_hash": "4e0f1773b9bee1be3b28f9fe2c71b3de", + "translation_date": "2025-08-25T16:56:28+00:00", + "source_file": "1-Introduction/01-defining-data-science/assignment.md", + "language_code": "fr" + }, + "1-Introduction/01-defining-data-science/solution/assignment.md": { + "original_hash": 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b/translations/fr/1-Introduction/01-defining-data-science/assignment.md index c992726f..edd59bc7 100644 --- a/translations/fr/1-Introduction/01-defining-data-science/assignment.md +++ b/translations/fr/1-Introduction/01-defining-data-science/assignment.md @@ -1,12 +1,3 @@ - # Devoir : Scénarios en Science des Données Dans ce premier devoir, nous vous demandons de réfléchir à un processus ou un problème réel dans différents domaines, et comment vous pourriez l'améliorer en utilisant le processus de la Science des Données. Pensez aux points suivants : diff --git a/translations/fr/1-Introduction/01-defining-data-science/solution/assignment.md b/translations/fr/1-Introduction/01-defining-data-science/solution/assignment.md index f9d86351..86640ef9 100644 --- a/translations/fr/1-Introduction/01-defining-data-science/solution/assignment.md +++ b/translations/fr/1-Introduction/01-defining-data-science/solution/assignment.md @@ -1,12 +1,3 @@ - # Devoir : Scénarios en Science des Données Dans ce premier devoir, nous vous demandons de réfléchir à un processus ou un problème réel dans différents domaines, et comment vous pourriez l'améliorer en utilisant le processus de la Science des Données. Pensez aux points suivants : diff --git a/translations/fr/1-Introduction/02-ethics/README.md b/translations/fr/1-Introduction/02-ethics/README.md index a05b9b84..43720c16 100644 --- a/translations/fr/1-Introduction/02-ethics/README.md +++ b/translations/fr/1-Introduction/02-ethics/README.md @@ -1,12 +1,3 @@ - # Introduction à l'éthique des données |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/02-Ethics.png)| diff --git a/translations/fr/1-Introduction/02-ethics/assignment.md b/translations/fr/1-Introduction/02-ethics/assignment.md index bb5acc69..bae7d41e 100644 --- a/translations/fr/1-Introduction/02-ethics/assignment.md +++ b/translations/fr/1-Introduction/02-ethics/assignment.md @@ -1,12 +1,3 @@ - ## Rédiger une étude de cas sur l'éthique des données ## Instructions diff --git a/translations/fr/1-Introduction/03-defining-data/README.md b/translations/fr/1-Introduction/03-defining-data/README.md index c986c58c..4b914479 100644 --- a/translations/fr/1-Introduction/03-defining-data/README.md +++ b/translations/fr/1-Introduction/03-defining-data/README.md @@ -1,12 +1,3 @@ - # Définir les données |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/03-DefiningData.png)| diff --git a/translations/fr/1-Introduction/03-defining-data/assignment.md b/translations/fr/1-Introduction/03-defining-data/assignment.md index 8fdf91f7..c4f9547e 100644 --- a/translations/fr/1-Introduction/03-defining-data/assignment.md +++ b/translations/fr/1-Introduction/03-defining-data/assignment.md @@ -1,12 +1,3 @@ - # Classification des ensembles de données ## Instructions diff --git a/translations/fr/1-Introduction/04-stats-and-probability/README.md b/translations/fr/1-Introduction/04-stats-and-probability/README.md index 732efb84..5686915f 100644 --- a/translations/fr/1-Introduction/04-stats-and-probability/README.md +++ b/translations/fr/1-Introduction/04-stats-and-probability/README.md @@ -1,12 +1,3 @@ - # Une brève introduction aux statistiques et probabilités |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/04-Statistics-Probability.png)| @@ -64,7 +55,7 @@ Pour nous aider à comprendre la distribution des données, il est utile de parl Graphiquement, nous pouvons représenter la relation entre la médiane et les quartiles dans un diagramme appelé **boîte à moustaches** : -Explication du diagramme en boîte +Explication du diagramme en boîte Ici, nous calculons également l'**étendue interquartile** IQR=Q3-Q1, et les **valeurs aberrantes** - des valeurs qui se situent en dehors des limites [Q1-1.5*IQR,Q3+1.5*IQR]. diff --git a/translations/fr/1-Introduction/04-stats-and-probability/assignment.md b/translations/fr/1-Introduction/04-stats-and-probability/assignment.md index d84c2220..f54d3842 100644 --- a/translations/fr/1-Introduction/04-stats-and-probability/assignment.md +++ b/translations/fr/1-Introduction/04-stats-and-probability/assignment.md @@ -1,12 +1,3 @@ - # Petite étude sur le diabète Dans cet exercice, nous travaillerons avec un petit ensemble de données de patients atteints de diabète, disponible [ici](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html). diff --git a/translations/fr/1-Introduction/README.md b/translations/fr/1-Introduction/README.md index 1b4e943a..f93bce5d 100644 --- a/translations/fr/1-Introduction/README.md +++ b/translations/fr/1-Introduction/README.md @@ -1,12 +1,3 @@ - # Introduction à la Science des Données ![données en action](../../../translated_images/fr/data.48e22bb7617d8d92.webp) diff --git a/translations/fr/2-Working-With-Data/05-relational-databases/README.md b/translations/fr/2-Working-With-Data/05-relational-databases/README.md index dd64bb88..68da7f6c 100644 --- a/translations/fr/2-Working-With-Data/05-relational-databases/README.md +++ b/translations/fr/2-Working-With-Data/05-relational-databases/README.md @@ -1,12 +1,3 @@ - # Travailler avec les données : bases de données relationnelles |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/05-RelationalData.png)| diff --git a/translations/fr/2-Working-With-Data/05-relational-databases/assignment.md b/translations/fr/2-Working-With-Data/05-relational-databases/assignment.md index ef86c9ce..6e47f24a 100644 --- a/translations/fr/2-Working-With-Data/05-relational-databases/assignment.md +++ b/translations/fr/2-Working-With-Data/05-relational-databases/assignment.md @@ -1,12 +1,3 @@ - # Affichage des données des aéroports On vous a fourni une [base de données](https://raw.githubusercontent.com/Microsoft/Data-Science-For-Beginners/main/2-Working-With-Data/05-relational-databases/airports.db) construite sur [SQLite](https://sqlite.org/index.html) contenant des informations sur les aéroports. Le schéma est affiché ci-dessous. Vous utiliserez l'[extension SQLite](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum) dans [Visual Studio Code](https://code.visualstudio.com?WT.mc_id=academic-77958-bethanycheum) pour afficher des informations sur les aéroports de différentes villes. diff --git a/translations/fr/2-Working-With-Data/06-non-relational/README.md b/translations/fr/2-Working-With-Data/06-non-relational/README.md index 427673f8..7d144d18 100644 --- a/translations/fr/2-Working-With-Data/06-non-relational/README.md +++ b/translations/fr/2-Working-With-Data/06-non-relational/README.md @@ -1,12 +1,3 @@ - # Travailler avec les données : données non relationnelles |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/06-NoSQL.png)| diff --git a/translations/fr/2-Working-With-Data/06-non-relational/assignment.md b/translations/fr/2-Working-With-Data/06-non-relational/assignment.md index 4ff4a438..c4b95130 100644 --- a/translations/fr/2-Working-With-Data/06-non-relational/assignment.md +++ b/translations/fr/2-Working-With-Data/06-non-relational/assignment.md @@ -1,12 +1,3 @@ - # Profits de Soda ## Instructions diff --git a/translations/fr/2-Working-With-Data/07-python/README.md b/translations/fr/2-Working-With-Data/07-python/README.md index 51934958..8297c1f2 100644 --- a/translations/fr/2-Working-With-Data/07-python/README.md +++ b/translations/fr/2-Working-With-Data/07-python/README.md @@ -1,12 +1,3 @@ - # Travailler avec des données : Python et la bibliothèque Pandas | ![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/07-WorkWithPython.png) | diff --git a/translations/fr/2-Working-With-Data/07-python/assignment.md b/translations/fr/2-Working-With-Data/07-python/assignment.md index ff0655e1..318b46a4 100644 --- a/translations/fr/2-Working-With-Data/07-python/assignment.md +++ b/translations/fr/2-Working-With-Data/07-python/assignment.md @@ -1,12 +1,3 @@ - # Devoir sur le traitement des données en Python Dans ce devoir, nous vous demandons d'approfondir le code que nous avons commencé à développer dans nos défis. Le devoir se compose de deux parties : diff --git a/translations/fr/2-Working-With-Data/08-data-preparation/README.md b/translations/fr/2-Working-With-Data/08-data-preparation/README.md index 8355504b..97c1da85 100644 --- a/translations/fr/2-Working-With-Data/08-data-preparation/README.md +++ b/translations/fr/2-Working-With-Data/08-data-preparation/README.md @@ -1,12 +1,3 @@ - # Travailler avec les données : Préparation des données |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/08-DataPreparation.png)| diff --git a/translations/fr/2-Working-With-Data/08-data-preparation/assignment.md b/translations/fr/2-Working-With-Data/08-data-preparation/assignment.md index a0e30c7c..1e32fd26 100644 --- a/translations/fr/2-Working-With-Data/08-data-preparation/assignment.md +++ b/translations/fr/2-Working-With-Data/08-data-preparation/assignment.md @@ -1,12 +1,3 @@ - # Évaluation des données d'un formulaire Un client a testé un [petit formulaire](../../../../2-Working-With-Data/08-data-preparation/index.html) pour recueillir des données de base sur sa clientèle. Il vous a transmis ses résultats pour valider les données collectées. Vous pouvez ouvrir la page `index.html` dans le navigateur pour examiner le formulaire. diff --git a/translations/fr/2-Working-With-Data/README.md b/translations/fr/2-Working-With-Data/README.md index 3726a049..aac735bc 100644 --- a/translations/fr/2-Working-With-Data/README.md +++ b/translations/fr/2-Working-With-Data/README.md @@ -1,12 +1,3 @@ - # Travailler avec les données ![amour des données](../../../translated_images/fr/data-love.a22ef29e6742c852.webp) diff --git a/translations/fr/3-Data-Visualization/09-visualization-quantities/README.md b/translations/fr/3-Data-Visualization/09-visualization-quantities/README.md index dd635d5d..cf3e3e3e 100644 --- a/translations/fr/3-Data-Visualization/09-visualization-quantities/README.md +++ b/translations/fr/3-Data-Visualization/09-visualization-quantities/README.md @@ -1,12 +1,3 @@ - # Visualiser des quantités |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/09-Visualizing-Quantities.png)| diff --git a/translations/fr/3-Data-Visualization/09-visualization-quantities/assignment.md b/translations/fr/3-Data-Visualization/09-visualization-quantities/assignment.md index 5de509f1..e2e27c90 100644 --- a/translations/fr/3-Data-Visualization/09-visualization-quantities/assignment.md +++ b/translations/fr/3-Data-Visualization/09-visualization-quantities/assignment.md @@ -1,12 +1,3 @@ - # Lignes, Nuages de points et Barres ## Instructions diff --git a/translations/fr/3-Data-Visualization/10-visualization-distributions/README.md b/translations/fr/3-Data-Visualization/10-visualization-distributions/README.md index 2f2826f2..523b121f 100644 --- a/translations/fr/3-Data-Visualization/10-visualization-distributions/README.md +++ b/translations/fr/3-Data-Visualization/10-visualization-distributions/README.md @@ -1,12 +1,3 @@ - # Visualiser les distributions |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/10-Visualizing-Distributions.png)| diff --git a/translations/fr/3-Data-Visualization/10-visualization-distributions/assignment.md b/translations/fr/3-Data-Visualization/10-visualization-distributions/assignment.md index 8fb9ecc7..ef5a0e5d 100644 --- a/translations/fr/3-Data-Visualization/10-visualization-distributions/assignment.md +++ b/translations/fr/3-Data-Visualization/10-visualization-distributions/assignment.md @@ -1,12 +1,3 @@ - # Appliquez vos compétences ## Instructions diff --git a/translations/fr/3-Data-Visualization/11-visualization-proportions/README.md b/translations/fr/3-Data-Visualization/11-visualization-proportions/README.md index 9aa8c0a6..eaaae88a 100644 --- a/translations/fr/3-Data-Visualization/11-visualization-proportions/README.md +++ b/translations/fr/3-Data-Visualization/11-visualization-proportions/README.md @@ -1,12 +1,3 @@ - # Visualiser les proportions |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/11-Visualizing-Proportions.png)| diff --git a/translations/fr/3-Data-Visualization/11-visualization-proportions/assignment.md b/translations/fr/3-Data-Visualization/11-visualization-proportions/assignment.md index 93c32309..c9ce11ca 100644 --- a/translations/fr/3-Data-Visualization/11-visualization-proportions/assignment.md +++ b/translations/fr/3-Data-Visualization/11-visualization-proportions/assignment.md @@ -1,12 +1,3 @@ - # Essayez-le dans Excel ## Instructions diff --git a/translations/fr/3-Data-Visualization/12-visualization-relationships/README.md b/translations/fr/3-Data-Visualization/12-visualization-relationships/README.md index 6bb048fb..e8eaff75 100644 --- a/translations/fr/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/fr/3-Data-Visualization/12-visualization-relationships/README.md @@ -1,12 +1,3 @@ - # Visualiser les relations : Tout sur le miel 🍯 |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/12-Visualizing-Relationships.png)| diff --git a/translations/fr/3-Data-Visualization/12-visualization-relationships/assignment.md b/translations/fr/3-Data-Visualization/12-visualization-relationships/assignment.md index 5fd70248..1b7286a1 100644 --- a/translations/fr/3-Data-Visualization/12-visualization-relationships/assignment.md +++ b/translations/fr/3-Data-Visualization/12-visualization-relationships/assignment.md @@ -1,12 +1,3 @@ - # Plongez dans la ruche ## Instructions diff --git a/translations/fr/3-Data-Visualization/13-meaningful-visualizations/README.md b/translations/fr/3-Data-Visualization/13-meaningful-visualizations/README.md index 540961f5..4847a1e6 100644 --- a/translations/fr/3-Data-Visualization/13-meaningful-visualizations/README.md +++ b/translations/fr/3-Data-Visualization/13-meaningful-visualizations/README.md @@ -1,12 +1,3 @@ - # Créer des visualisations significatives |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/13-MeaningfulViz.png)| diff --git a/translations/fr/3-Data-Visualization/13-meaningful-visualizations/assignment.md b/translations/fr/3-Data-Visualization/13-meaningful-visualizations/assignment.md index 2b6d56f0..4006b5b8 100644 --- a/translations/fr/3-Data-Visualization/13-meaningful-visualizations/assignment.md +++ b/translations/fr/3-Data-Visualization/13-meaningful-visualizations/assignment.md @@ -1,12 +1,3 @@ - # Créez votre propre visualisation personnalisée ## Instructions diff --git a/translations/fr/3-Data-Visualization/13-meaningful-visualizations/solution/README.md b/translations/fr/3-Data-Visualization/13-meaningful-visualizations/solution/README.md index 7c2a5d63..d20b5104 100644 --- a/translations/fr/3-Data-Visualization/13-meaningful-visualizations/solution/README.md +++ b/translations/fr/3-Data-Visualization/13-meaningful-visualizations/solution/README.md @@ -1,12 +1,3 @@ - # Projet de visualisation des données Dangerous Liaisons Pour commencer, assurez-vous que NPM et Node sont installés et fonctionnent sur votre machine. Installez les dépendances (npm install) puis exécutez le projet en local (npm run serve) : diff --git a/translations/fr/3-Data-Visualization/13-meaningful-visualizations/starter/README.md b/translations/fr/3-Data-Visualization/13-meaningful-visualizations/starter/README.md index b0697c00..724cbb36 100644 --- a/translations/fr/3-Data-Visualization/13-meaningful-visualizations/starter/README.md +++ b/translations/fr/3-Data-Visualization/13-meaningful-visualizations/starter/README.md @@ -1,12 +1,3 @@ - # Projet de visualisation des données Dangerous Liaisons Pour commencer, assurez-vous que NPM et Node sont installés et fonctionnent sur votre machine. Installez les dépendances (npm install), puis exécutez le projet en local (npm run serve) : diff --git a/translations/fr/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/fr/3-Data-Visualization/R/09-visualization-quantities/README.md index 74630ac0..cb696e37 100644 --- a/translations/fr/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/fr/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -1,12 +1,3 @@ - # Visualiser des quantités |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/sketchnotes/09-Visualizing-Quantities.png)| |:---:| diff --git a/translations/fr/3-Data-Visualization/R/09-visualization-quantities/assignment.md b/translations/fr/3-Data-Visualization/R/09-visualization-quantities/assignment.md index dcfbb11e..f329ef08 100644 --- a/translations/fr/3-Data-Visualization/R/09-visualization-quantities/assignment.md +++ b/translations/fr/3-Data-Visualization/R/09-visualization-quantities/assignment.md @@ -1,12 +1,3 @@ - # Lignes, Nuages de points et Barres ## Instructions diff --git a/translations/fr/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/fr/3-Data-Visualization/R/10-visualization-distributions/README.md index c00b88d8..78cc4122 100644 --- a/translations/fr/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/fr/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -1,12 +1,3 @@ - # Visualiser les distributions |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/sketchnotes/10-Visualizing-Distributions.png)| diff --git a/translations/fr/3-Data-Visualization/R/10-visualization-distributions/assignment.md b/translations/fr/3-Data-Visualization/R/10-visualization-distributions/assignment.md index d4917517..a2b070ae 100644 --- a/translations/fr/3-Data-Visualization/R/10-visualization-distributions/assignment.md +++ b/translations/fr/3-Data-Visualization/R/10-visualization-distributions/assignment.md @@ -1,12 +1,3 @@ - # Mettez vos compétences en pratique ## Instructions diff --git a/translations/fr/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/fr/3-Data-Visualization/R/11-visualization-proportions/README.md index 6af86f84..9e96efd0 100644 --- a/translations/fr/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/fr/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -1,12 +1,3 @@ - # Visualiser les proportions |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/11-Visualizing-Proportions.png)| diff --git a/translations/fr/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/fr/3-Data-Visualization/R/12-visualization-relationships/README.md index d2a1b910..9713bb61 100644 --- a/translations/fr/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/fr/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -1,12 +1,3 @@ - # Visualiser les relations : Tout sur le miel 🍯 |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/12-Visualizing-Relationships.png)| diff --git a/translations/fr/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/fr/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 71a47464..7161369e 100644 --- a/translations/fr/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/fr/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -1,12 +1,3 @@ - # Créer des Visualisations Significatives |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/13-MeaningfulViz.png)| diff --git a/translations/fr/3-Data-Visualization/README.md b/translations/fr/3-Data-Visualization/README.md index c23beb3c..97f4164f 100644 --- a/translations/fr/3-Data-Visualization/README.md +++ b/translations/fr/3-Data-Visualization/README.md @@ -1,12 +1,3 @@ - # Visualisations ![une abeille sur une fleur de lavande](../../../translated_images/fr/bee.0aa1d91132b12e3a.webp) diff --git a/translations/fr/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/fr/4-Data-Science-Lifecycle/14-Introduction/README.md index f43eb9e0..147deb71 100644 --- a/translations/fr/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/fr/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -1,12 +1,3 @@ - # Introduction au cycle de vie de la science des données |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/14-DataScience-Lifecycle.png)| diff --git a/translations/fr/4-Data-Science-Lifecycle/14-Introduction/assignment.md b/translations/fr/4-Data-Science-Lifecycle/14-Introduction/assignment.md index 688c9498..42c4dcec 100644 --- a/translations/fr/4-Data-Science-Lifecycle/14-Introduction/assignment.md +++ b/translations/fr/4-Data-Science-Lifecycle/14-Introduction/assignment.md @@ -1,12 +1,3 @@ - # Évaluation d'un ensemble de données Un client a sollicité votre équipe pour l'aider à analyser les habitudes de dépenses saisonnières des clients de taxis à New York. diff --git a/translations/fr/4-Data-Science-Lifecycle/15-analyzing/README.md b/translations/fr/4-Data-Science-Lifecycle/15-analyzing/README.md index 4c61d852..a7f0c41f 100644 --- a/translations/fr/4-Data-Science-Lifecycle/15-analyzing/README.md +++ b/translations/fr/4-Data-Science-Lifecycle/15-analyzing/README.md @@ -1,12 +1,3 @@ - # Le cycle de vie de la science des données : Analyse |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/15-Analyzing.png)| diff --git a/translations/fr/4-Data-Science-Lifecycle/15-analyzing/assignment.md b/translations/fr/4-Data-Science-Lifecycle/15-analyzing/assignment.md index 129af41f..fbdf3e70 100644 --- a/translations/fr/4-Data-Science-Lifecycle/15-analyzing/assignment.md +++ b/translations/fr/4-Data-Science-Lifecycle/15-analyzing/assignment.md @@ -1,12 +1,3 @@ - # Exploration des réponses Ceci est une continuation de [l'exercice](../14-Introduction/assignment.md) de la leçon précédente, où nous avons brièvement examiné l'ensemble de données. Maintenant, nous allons examiner les données de manière plus approfondie. diff --git a/translations/fr/4-Data-Science-Lifecycle/16-communication/README.md b/translations/fr/4-Data-Science-Lifecycle/16-communication/README.md index 2b7105ae..e0308630 100644 --- a/translations/fr/4-Data-Science-Lifecycle/16-communication/README.md +++ b/translations/fr/4-Data-Science-Lifecycle/16-communication/README.md @@ -1,12 +1,3 @@ - # Le cycle de vie de la science des données : Communication |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/16-Communicating.png)| diff --git a/translations/fr/4-Data-Science-Lifecycle/16-communication/assignment.md b/translations/fr/4-Data-Science-Lifecycle/16-communication/assignment.md index 41bdd677..279973dc 100644 --- a/translations/fr/4-Data-Science-Lifecycle/16-communication/assignment.md +++ b/translations/fr/4-Data-Science-Lifecycle/16-communication/assignment.md @@ -1,12 +1,3 @@ - # Racontez une histoire ## Instructions diff --git a/translations/fr/4-Data-Science-Lifecycle/README.md b/translations/fr/4-Data-Science-Lifecycle/README.md index 0f6b76d2..bb5f55bc 100644 --- a/translations/fr/4-Data-Science-Lifecycle/README.md +++ b/translations/fr/4-Data-Science-Lifecycle/README.md @@ -1,12 +1,3 @@ - # Le cycle de vie de la science des données ![communication](../../../translated_images/fr/communication.06d8e2a88d30d168.webp) diff --git a/translations/fr/5-Data-Science-In-Cloud/17-Introduction/README.md b/translations/fr/5-Data-Science-In-Cloud/17-Introduction/README.md index f3d2bec6..3e8692a2 100644 --- a/translations/fr/5-Data-Science-In-Cloud/17-Introduction/README.md +++ b/translations/fr/5-Data-Science-In-Cloud/17-Introduction/README.md @@ -1,12 +1,3 @@ - # Introduction à la Science des Données dans le Cloud |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/17-DataScience-Cloud.png)| diff --git a/translations/fr/5-Data-Science-In-Cloud/17-Introduction/assignment.md b/translations/fr/5-Data-Science-In-Cloud/17-Introduction/assignment.md index b97b3d6d..2f1f7188 100644 --- a/translations/fr/5-Data-Science-In-Cloud/17-Introduction/assignment.md +++ b/translations/fr/5-Data-Science-In-Cloud/17-Introduction/assignment.md @@ -1,12 +1,3 @@ - # Recherche de Marché ## Instructions diff --git a/translations/fr/5-Data-Science-In-Cloud/18-Low-Code/README.md b/translations/fr/5-Data-Science-In-Cloud/18-Low-Code/README.md index 79dedb5c..afa9eb5c 100644 --- a/translations/fr/5-Data-Science-In-Cloud/18-Low-Code/README.md +++ b/translations/fr/5-Data-Science-In-Cloud/18-Low-Code/README.md @@ -1,12 +1,3 @@ - # La science des données dans le cloud : La méthode "Low code/No code" |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/18-DataScience-Cloud.png)| diff --git a/translations/fr/5-Data-Science-In-Cloud/18-Low-Code/assignment.md b/translations/fr/5-Data-Science-In-Cloud/18-Low-Code/assignment.md index 25552b5f..eb9827ae 100644 --- a/translations/fr/5-Data-Science-In-Cloud/18-Low-Code/assignment.md +++ b/translations/fr/5-Data-Science-In-Cloud/18-Low-Code/assignment.md @@ -1,12 +1,3 @@ - # Projet de Data Science Low code/No code sur Azure ML ## Instructions diff --git a/translations/fr/5-Data-Science-In-Cloud/19-Azure/README.md b/translations/fr/5-Data-Science-In-Cloud/19-Azure/README.md index 1f55146e..868f1246 100644 --- a/translations/fr/5-Data-Science-In-Cloud/19-Azure/README.md +++ b/translations/fr/5-Data-Science-In-Cloud/19-Azure/README.md @@ -1,12 +1,3 @@ - # La Science des Données dans le Cloud : La méthode "Azure ML SDK" |![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/19-DataScience-Cloud.png)| diff --git a/translations/fr/5-Data-Science-In-Cloud/19-Azure/assignment.md b/translations/fr/5-Data-Science-In-Cloud/19-Azure/assignment.md index 1c321c68..2743b5b3 100644 --- a/translations/fr/5-Data-Science-In-Cloud/19-Azure/assignment.md +++ b/translations/fr/5-Data-Science-In-Cloud/19-Azure/assignment.md @@ -1,12 +1,3 @@ - # Projet de Data Science avec Azure ML SDK ## Instructions diff --git a/translations/fr/5-Data-Science-In-Cloud/README.md b/translations/fr/5-Data-Science-In-Cloud/README.md index 5e159e14..d016b606 100644 --- a/translations/fr/5-Data-Science-In-Cloud/README.md +++ b/translations/fr/5-Data-Science-In-Cloud/README.md @@ -1,12 +1,3 @@ - # La Data Science dans le Cloud ![cloud-picture](../../../translated_images/fr/cloud-picture.f5526de3c6c6387b.webp) diff --git a/translations/fr/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/fr/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 9e235b00..c8695dc7 100644 --- a/translations/fr/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/fr/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -1,12 +1,3 @@ - # La science des données dans le monde réel | ![ Sketchnote par [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-RealWorld.png) | diff --git a/translations/fr/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/fr/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 96693492..72744e57 100644 --- a/translations/fr/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/fr/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -1,12 +1,3 @@ - # Explorer un Jeu de Données du Planetary Computer ## Instructions diff --git a/translations/fr/6-Data-Science-In-Wild/README.md b/translations/fr/6-Data-Science-In-Wild/README.md index bc0d2583..dc8765eb 100644 --- a/translations/fr/6-Data-Science-In-Wild/README.md +++ b/translations/fr/6-Data-Science-In-Wild/README.md @@ -1,12 +1,3 @@ - # La Data Science dans la Nature Applications concrètes de la data science dans divers secteurs. diff --git a/translations/fr/AGENTS.md b/translations/fr/AGENTS.md index 526918f5..d66fcd1a 100644 --- a/translations/fr/AGENTS.md +++ b/translations/fr/AGENTS.md @@ -1,12 +1,3 @@ - # AGENTS.md ## Aperçu du projet diff --git a/translations/fr/CODE_OF_CONDUCT.md b/translations/fr/CODE_OF_CONDUCT.md index 81cd4e6c..9d28d140 100644 --- a/translations/fr/CODE_OF_CONDUCT.md +++ b/translations/fr/CODE_OF_CONDUCT.md @@ -1,12 +1,3 @@ - # Code de conduite Open Source de Microsoft Ce projet a adopté le [Code de conduite Open Source de Microsoft](https://opensource.microsoft.com/codeofconduct/). diff --git a/translations/fr/CONTRIBUTING.md b/translations/fr/CONTRIBUTING.md index 1ba08a4b..56838434 100644 --- a/translations/fr/CONTRIBUTING.md +++ b/translations/fr/CONTRIBUTING.md @@ -1,12 +1,3 @@ - # Contribuer à Data Science pour les Débutants Merci de votre intérêt pour contribuer au programme Data Science pour les Débutants ! Nous accueillons les contributions de la communauté. diff --git a/translations/fr/INSTALLATION.md b/translations/fr/INSTALLATION.md index 4ca41f49..f5d2000d 100644 --- a/translations/fr/INSTALLATION.md +++ b/translations/fr/INSTALLATION.md @@ -1,12 +1,3 @@ - # Guide d'installation Ce guide vous aidera à configurer votre environnement pour travailler avec le programme "Data Science for Beginners". diff --git a/translations/fr/README.md b/translations/fr/README.md index 2a5e5418..2f775964 100644 --- a/translations/fr/README.md +++ b/translations/fr/README.md @@ -1,52 +1,43 @@ - -# Data Science pour débutants - Un programme - -[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198) - -[![GitHub license](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE) -[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/) -[![GitHub issues](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/) -[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/pulls/) -[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) - -[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/Data-Science-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/Data-Science-For-Beginners/watchers/) -[![GitHub forks](https://img.shields.io/github/forks/microsoft/Data-Science-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/Data-Science-For-Beginners/network/) -[![GitHub stars](https://img.shields.io/github/stars/microsoft/Data-Science-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/Data-Science-For-Beginners/stargazers/) +# Science des données pour débutants - Un programme + +[![Ouvrir dans GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198) + +[![Licence GitHub](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE) +[![Contributeurs GitHub](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/) +[![Problèmes GitHub](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/) +[![Demandes de tirage GitHub](https://img.shields.io/github/issues-pr/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/pulls/) +[![PRs Bienvenus](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) + +[![Veilleurs GitHub](https://img.shields.io/github/watchers/microsoft/Data-Science-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/Data-Science-For-Beginners/watchers/) +[![Branches GitHub](https://img.shields.io/github/forks/microsoft/Data-Science-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/Data-Science-For-Beginners/network/) +[![Étoiles GitHub](https://img.shields.io/github/stars/microsoft/Data-Science-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/Data-Science-For-Beginners/stargazers/) [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) +[![Forum développeur Microsoft Foundry](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) -Les Azure Cloud Advocates chez Microsoft sont heureux de proposer un programme de 10 semaines et 20 leçons entièrement consacré à la science des données. Chaque leçon comprend des quiz avant et après la leçon, des instructions écrites pour compléter la leçon, une solution, et un devoir. Notre pédagogie basée sur les projets vous permet d'apprendre en construisant, une méthode éprouvée pour que les nouvelles compétences « collent ». +Les Azure Cloud Advocates de Microsoft sont heureux de proposer un programme de 10 semaines, 20 leçons, entièrement dédié à la science des données. Chaque leçon inclut des quiz avant et après la leçon, des instructions écrites pour compléter la leçon, une solution, et un exercice. Notre pédagogie basée sur des projets vous permet d’apprendre tout en construisant, une méthode éprouvée pour que les nouvelles compétences restent bien ancrées. **Un grand merci à nos auteurs :** [Jasmine Greenaway](https://www.twitter.com/paladique), [Dmitry Soshnikov](http://soshnikov.com), [Nitya Narasimhan](https://twitter.com/nitya), [Jalen McGee](https://twitter.com/JalenMcG), [Jen Looper](https://twitter.com/jenlooper), [Maud Levy](https://twitter.com/maudstweets), [Tiffany Souterre](https://twitter.com/TiffanySouterre), [Christopher Harrison](https://www.twitter.com/geektrainer). -**🙏 Remerciements particuliers 🙏 à nos auteurs, relecteurs et contributeurs de contenu [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** notamment Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), +**🙏 Remerciements spéciaux 🙏 à nos auteurs, réviseurs et contributeurs de contenu [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** notamment Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/fr/00-Title.8af36cd35da1ac55.webp)| +|![Note visuelle par @sketchthedocs https://sketchthedocs.dev](../../translated_images/fr/00-Title.8af36cd35da1ac55.webp)| |:---:| -| Data Science Pour Débutants - _Sketchnote par [@nitya](https://twitter.com/nitya)_ | +| Science des données pour débutants - _Note visuelle par [@nitya](https://twitter.com/nitya)_ | ### 🌐 Support multilingue #### Pris en charge via GitHub Action (Automatisé & Toujours à jour) -[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](./README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) +[Arabe](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgare](../bg/README.md) | [Birman (Myanmar)](../my/README.md) | [Chinois (Simplifié)](../zh-CN/README.md) | [Chinois (Traditionnel, Hong Kong)](../zh-HK/README.md) | [Chinois (Traditionnel, Macao)](../zh-MO/README.md) | [Chinois (Traditionnel, Taïwan)](../zh-TW/README.md) | [Croate](../hr/README.md) | [Tchèque](../cs/README.md) | [Danois](../da/README.md) | [Néerlandais](../nl/README.md) | [Estonien](../et/README.md) | [Finnois](../fi/README.md) | [Français](./README.md) | [Allemand](../de/README.md) | [Grec](../el/README.md) | [Hébreu](../he/README.md) | [Hindi](../hi/README.md) | [Hongrois](../hu/README.md) | [Indonésien](../id/README.md) | [Italien](../it/README.md) | [Japonais](../ja/README.md) | [Kannada](../kn/README.md) | [Coréen](../ko/README.md) | [Lituanien](../lt/README.md) | [Malais](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Népalais](../ne/README.md) | [Pidgin nigérian](../pcm/README.md) | [Norvégien](../no/README.md) | [Persan (Farsi)](../fa/README.md) | [Polonais](../pl/README.md) | [Portugais (Brésil)](../pt-BR/README.md) | [Portugais (Portugal)](../pt-PT/README.md) | [Pendjabi (Gurmukhi)](../pa/README.md) | [Roumain](../ro/README.md) | [Russe](../ru/README.md) | [Serbe (Cyrillique)](../sr/README.md) | [Slovaque](../sk/README.md) | [Slovène](../sl/README.md) | [Espagnol](../es/README.md) | [Swahili](../sw/README.md) | [Suédois](../sv/README.md) | [Tagalog (Philippin)](../tl/README.md) | [Tamoul](../ta/README.md) | [Télougou](../te/README.md) | [Thaï](../th/README.md) | [Turc](../tr/README.md) | [Ukrainien](../uk/README.md) | [Ourdou](../ur/README.md) | [Vietnamien](../vi/README.md) -> **Vous préférez cloner localement ?** +> **Préférez cloner localement ?** -> Ce dépôt inclut plus de 50 traductions, ce qui augmente considérablement la taille du téléchargement. Pour cloner sans les traductions, utilisez le sparse checkout : +> Ce dépôt comprend plus de 50 traductions linguistiques ce qui augmente significativement la taille du téléchargement. Pour cloner sans les traductions, utilisez le sparse checkout : > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/Data-Science-For-Beginners.git > cd Data-Science-For-Beginners @@ -55,47 +46,47 @@ Les Azure Cloud Advocates chez Microsoft sont heureux de proposer un programme d > Cela vous donne tout ce dont vous avez besoin pour suivre le cours avec un téléchargement beaucoup plus rapide. -**Si vous souhaitez que d’autres langues de traduction soient prises en charge, la liste est disponible [ici](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Si vous souhaitez que d’autres langues de traduction soient prises en charge, elles sont listées [ici](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** #### Rejoignez notre communauté [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Nous avons une série Discord Apprenez avec l’IA en cours, apprenez-en plus et rejoignez-nous sur [Learn with AI Series](https://aka.ms/learnwithai/discord) du 18 au 30 septembre 2025. Vous y découvrirez des astuces pour utiliser GitHub Copilot en science des données. +Nous avons une série Discord « apprendre avec l’IA » en cours, apprenez-en plus et rejoignez-nous sur [Série Apprendre avec l’IA](https://aka.ms/learnwithai/discord) du 18 au 30 septembre 2025. Vous recevrez des astuces pour utiliser GitHub Copilot en science des données. -![Learn with AI series](../../../../translated_images/fr/1.2b28cdc6205e26fe.webp) +![Série Apprendre avec l’IA](../../translated_images/fr/1.2b28cdc6205e26fe.webp) # Êtes-vous étudiant ? Commencez avec les ressources suivantes : -- [Page du Student Hub](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) Sur cette page, vous trouverez des ressources pour débutants, des packs étudiants et même des moyens d’obtenir un bon pour une certification gratuite. C’est une page à mettre en favori et à consulter régulièrement, car le contenu y est renouvelé au moins chaque mois. +- [Page du hub étudiant](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) Sur cette page, vous trouverez des ressources pour débutants, des packs étudiants et même des moyens d’obtenir un voucher de certification gratuit. C’est une page que vous voudrez mettre en favori et consulter régulièrement car le contenu est renouvelé au moins chaque mois. - [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Rejoignez une communauté mondiale d’ambassadeurs étudiants, cela pourrait être votre porte d’entrée chez Microsoft. -# Premiers pas +# Pour commencer ## 📚 Documentation -- **[Guide d’installation](INSTALLATION.md)** - Instructions de configuration étape par étape pour débutants +- **[Guide d’installation](INSTALLATION.md)** - Instructions pas à pas pour débutants - **[Guide d’utilisation](USAGE.md)** - Exemples et flux de travail courants - **[Dépannage](TROUBLESHOOTING.md)** - Solutions aux problèmes fréquents - **[Guide de contribution](CONTRIBUTING.md)** - Comment contribuer à ce projet - **[Pour les enseignants](for-teachers.md)** - Conseils pédagogiques et ressources pour la classe ## 👨‍🎓 Pour les étudiants -> **Débutants complets** : Nouveau en science des données ? Commencez avec nos [exemples pour débutants](examples/README.md) ! Ces exemples simples et bien commentés vous aideront à comprendre les bases avant de vous plonger dans le programme complet. -> **[Étudiants](https://aka.ms/student-page)** : pour utiliser ce programme de façon autonome, créez un fork complet du dépôt et faites les exercices seul(e), en commençant par un quiz pré-conférence. Puis lisez la conférence et complétez les activités restantes. Essayez de réaliser les projets en comprenant les leçons plutôt qu’en copiant le code solution ; cependant, ce code est disponible dans les dossiers /solutions de chaque leçon centrée sur un projet. Une autre idée serait de former un groupe d’étude avec des amis et de parcourir le contenu ensemble. Pour approfondir, nous recommandons [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum). +> **Débutants complets** : Nouveau en science des données ? Commencez avec nos [exemples adaptés aux débutants](examples/README.md) ! Ces exemples simples et bien commentés vous aideront à comprendre les bases avant de plonger dans le programme complet. +> **[Étudiants](https://aka.ms/student-page)** : pour utiliser ce programme de manière autonome, forkez l’ensemble du dépôt et complétez les exercices par vous-même, en commençant par un quiz avant la leçon. Puis lisez la leçon et terminez les autres activités. Essayez de créer les projets en comprenant les leçons plutôt qu’en copiant le code solution ; cependant, ce code est disponible dans les dossiers /solutions de chaque leçon orientée projet. Une autre idée serait de former un groupe d’étude avec des amis et de parcourir le contenu ensemble. Pour approfondir, nous recommandons [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum). **Démarrage rapide :** 1. Consultez le [Guide d’installation](INSTALLATION.md) pour configurer votre environnement -2. Parcourez le [Guide d’utilisation](USAGE.md) pour apprendre à travailler avec le programme +2. Revoyez le [Guide d’utilisation](USAGE.md) pour apprendre à travailler avec le programme 3. Commencez par la leçon 1 et suivez-les dans l’ordre 4. Rejoignez notre [communauté Discord](https://aka.ms/ds4beginners/discord) pour obtenir de l’aide ## 👩‍🏫 Pour les enseignants -> **Enseignants** : nous avons [inclus quelques suggestions](for-teachers.md) sur la manière d’utiliser ce programme. Vos retours nous intéressent [dans notre forum de discussion](https://github.com/microsoft/Data-Science-For-Beginners/discussions) ! +> **Enseignants** : nous avons [inclus quelques suggestions](for-teachers.md) sur la manière d’utiliser ce programme. Nous serions ravis de votre retour [dans notre forum de discussion](https://github.com/microsoft/Data-Science-For-Beginners/discussions) ! +## Rencontrez l'équipe -## Rencontrez l’équipe [![Vidéo promo](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "Vidéo promo") **Gif par** [Mohit Jaisal](https://www.linkedin.com/in/mohitjaisal) @@ -104,159 +95,159 @@ Commencez avec les ressources suivantes : ## Pédagogie -Nous avons choisi deux principes pédagogiques lors de la construction de ce cursus : s'assurer qu'il soit basé sur des projets et qu'il inclue des quiz fréquents. À la fin de cette série, les étudiants auront appris les principes de base de la science des données, y compris les concepts éthiques, la préparation des données, différentes manières de travailler avec les données, la visualisation des données, l'analyse des données, des cas d'usage réels de la science des données, et plus encore. +Nous avons choisi deux principes pédagogiques lors de la construction de ce programme : garantir qu'il soit basé sur des projets et qu'il inclue des quiz fréquents. À la fin de cette série, les étudiants auront appris les principes de base de la science des données, y compris des concepts éthiques, la préparation des données, différentes façons de travailler avec les données, la visualisation des données, l'analyse des données, des cas d'utilisation réels de la science des données, et plus encore. -De plus, un quiz à faible enjeu avant un cours oriente l'intention de l'étudiant vers l'apprentissage d'un sujet, tandis qu'un second quiz après la classe assure une meilleure rétention. Ce cursus a été conçu pour être flexible et ludique et peut être suivi dans son intégralité ou en partie. Les projets commencent petits et deviennent de plus en plus complexes à la fin du cycle de 10 semaines. +De plus, un quiz à enjeu faible avant un cours fixe l'intention de l'étudiant envers l'apprentissage d'un sujet, tandis qu'un second quiz après le cours assure une meilleure rétention. Ce programme a été conçu pour être flexible et amusant et peut être suivi en totalité ou en partie. Les projets commencent petits et deviennent de plus en plus complexes à la fin du cycle de 10 semaines. -> Retrouvez notre [Code de conduite](CODE_OF_CONDUCT.md), [Contributions](CONTRIBUTING.md), [Traduction](TRANSLATIONS.md). Nous accueillons vos retours constructifs ! +> Trouvez notre [Code de Conduite](CODE_OF_CONDUCT.md), [Contribuer](CONTRIBUTING.md), [Traduction](TRANSLATIONS.md) directives. Nous accueillons vos retours constructifs ! ## Chaque leçon inclut : - Sketchnote optionnel - Vidéo complémentaire optionnelle -- Quiz d’échauffement avant la leçon +- Quiz d'échauffement avant la leçon - Leçon écrite -- Pour les leçons basées sur un projet, des guides étape par étape pour construire le projet +- Pour les leçons basées sur des projets, des guides étape par étape pour construire le projet - Vérifications des connaissances - Un défi -- Lectures complémentaires -- Devoirs +- Lecture complémentaire +- Devoir - [Quiz post-leçon](https://ff-quizzes.netlify.app/en/) -> **Une note sur les quiz** : Tous les quiz se trouvent dans le dossier Quiz-App, pour un total de 40 quiz de trois questions chacun. Ils sont liés depuis les leçons, mais l'application de quiz peut être exécutée localement ou déployée sur Azure ; suivez les instructions dans le dossier `quiz-app`. Ils sont progressivement localisés. +> **Une note sur les quiz** : Tous les quiz sont contenus dans le dossier Quiz-App, pour un total de 40 quiz de trois questions chacun. Ils sont liés depuis les leçons, mais l'application de quiz peut être exécutée localement ou déployée sur Azure ; suivez les instructions dans le dossier `quiz-app`. Ils sont progressivement localisés. -## 🎓 Exemples pour débutants +## 🎓 Exemples adaptés aux débutants -**Nouveau en science des données ?** Nous avons créé un répertoire spécial [exemples](examples/README.md) avec des codes simples et bien commentés pour vous aider à démarrer : +**Nouveau en science des données ?** Nous avons créé un [répertoire d'exemples](examples/README.md) spécial avec du code simple et bien commenté pour vous aider à démarrer : - 🌟 **Hello World** - Votre premier programme de science des données -- 📂 **Chargement de données** - Apprenez à lire et explorer des jeux de données -- 📊 **Analyse simple** - Calculer des statistiques et trouver des motifs -- 📈 **Visualisation basique** - Créer des graphiques et des diagrammes +- 📂 **Chargement des données** - Apprenez à lire et explorer des ensembles de données +- 📊 **Analyse simple** - Calculez des statistiques et trouvez des motifs +- 📈 **Visualisation de base** - Créez des graphiques et des diagrammes - 🔬 **Projet réel** - Flux de travail complet du début à la fin -Chaque exemple inclut des commentaires détaillés expliquant chaque étape, parfait pour les débutants absolus ! +Chaque exemple inclut des commentaires détaillés expliquant chaque étape, parfaitement adapté aux débutants absolus ! -👉 **[Commencez par les exemples](examples/README.md)** 👈 +👉 **[Commencez avec les exemples](examples/README.md)** 👈 ## Leçons -|![ Sketchnote par @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/fr/00-Roadmap.4905d6567dff4753.webp)| +|![ Sketchnote par @sketchthedocs https://sketchthedocs.dev](../../translated_images/fr/00-Roadmap.4905d6567dff4753.webp)| |:---:| | Science des données pour débutants : feuille de route - _Sketchnote par [@nitya](https://twitter.com/nitya)_ | -| Numéro de leçon | Sujet | Groupe de leçons | Objectifs d'apprentissage | Leçon liée | Auteur | +| Numéro de leçon | Sujet | Regroupement de leçon | Objectifs d’apprentissage | Leçon liée | Auteur | | :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: | -| 01 | Définir la science des données | [Introduction](1-Introduction/README.md) | Apprendre les concepts de base derrière la science des données et comment elle est liée à l'intelligence artificielle, au machine learning et au big data. | [leçon](1-Introduction/01-defining-data-science/README.md) [vidéo](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) | -| 02 | Éthique en science des données | [Introduction](1-Introduction/README.md) | Concepts, défis et cadres éthiques des données. | [leçon](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | -| 03 | Définir les données | [Introduction](1-Introduction/README.md) | Comment les données sont classifiées et leurs sources communes. | [leçon](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) | -| 04 | Introduction aux statistiques & probabilités | [Introduction](1-Introduction/README.md) | Techniques mathématiques de la probabilité et des statistiques pour comprendre les données. | [leçon](1-Introduction/04-stats-and-probability/README.md) [vidéo](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) | -| 05 | Travailler avec des données relationnelles | [Working With Data](2-Working-With-Data/README.md) | Introduction aux données relationnelles et aux bases de l'exploration et de l'analyse avec le langage de requête structurée, également appelé SQL (prononcé « see-quell »). | [leçon](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | | -| 06 | Travailler avec des données NoSQL | [Working With Data](2-Working-With-Data/README.md) | Introduction aux données non relationnelles, leurs différents types et les bases de l'exploration et de l'analyse des bases de données documentaires. | [leçon](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)| -| 07 | Travailler avec Python | [Working With Data](2-Working-With-Data/README.md) | Bases de l'utilisation de Python pour explorer les données avec des bibliothèques comme Pandas. Une compréhension de base de la programmation Python est recommandée. | [leçon](2-Working-With-Data/07-python/README.md) [vidéo](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) | -| 08 | Préparation des données | [Working With Data](2-Working-With-Data/README.md) | Techniques pour nettoyer et transformer les données afin de gérer les défis des données manquantes, inexactes ou incomplètes. | [leçon](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) | -| 09 | Visualiser les quantités | [Data Visualization](3-Data-Visualization/README.md) | Apprenez à utiliser Matplotlib pour visualiser les données d'oiseaux 🦆 | [leçon](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) | -| 10 | Visualiser les distributions de données | [Data Visualization](3-Data-Visualization/README.md) | Visualiser les observations et tendances au sein d'un intervalle. | [leçon](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) | -| 11 | Visualiser les proportions | [Data Visualization](3-Data-Visualization/README.md) | Visualiser des pourcentages discrets et groupés. | [leçon](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) | +| 01 | Définir la science des données | [Introduction](1-Introduction/README.md) | Apprenez les concepts de base de la science des données et comment elle est liée à l’intelligence artificielle, l’apprentissage automatique et le Big Data. | [leçon](1-Introduction/01-defining-data-science/README.md) [vidéo](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) | +| 02 | Éthique de la science des données | [Introduction](1-Introduction/README.md) | Concepts, défis et cadres éthiques des données. | [leçon](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | +| 03 | Définir les données | [Introduction](1-Introduction/README.md) | Comment les données sont classifiées et leurs sources courantes. | [leçon](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) | +| 04 | Introduction aux statistiques & probabilités | [Introduction](1-Introduction/README.md) | Les techniques mathématiques des probabilités et des statistiques pour comprendre les données. | [leçon](1-Introduction/04-stats-and-probability/README.md) [vidéo](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) | +| 05 | Travailler avec des données relationnelles | [Working With Data](2-Working-With-Data/README.md) | Introduction aux données relationnelles et bases de l’exploration et de l’analyse des données relationnelles avec le langage SQL (prononcé « see-quell »). | [leçon](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | | +| 06 | Travailler avec des données NoSQL | [Working With Data](2-Working-With-Data/README.md) | Introduction aux données non relationnelles, leurs différents types et bases de l’exploration et de l’analyse des bases de données documentaires. | [leçon](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)| +| 07 | Travailler avec Python | [Working With Data](2-Working-With-Data/README.md) | Bases de l’utilisation de Python pour l’exploration des données avec des bibliothèques telles que Pandas. Une compréhension fondationnelle de la programmation Python est recommandée. | [leçon](2-Working-With-Data/07-python/README.md) [vidéo](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) | +| 08 | Préparation des données | [Working With Data](2-Working-With-Data/README.md) | Sujets sur les techniques de nettoyage et de transformation des données pour gérer les défis des données manquantes, inexactes ou incomplètes. | [leçon](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) | +| 09 | Visualiser les quantités | [Data Visualization](3-Data-Visualization/README.md) | Apprenez à utiliser Matplotlib pour visualiser les données sur les oiseaux 🦆 | [leçon](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) | +| 10 | Visualiser les distributions des données | [Data Visualization](3-Data-Visualization/README.md) | Visualiser les observations et les tendances dans un intervalle. | [leçon](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) | +| 11 | Visualiser les proportions | [Data Visualization](3-Data-Visualization/README.md) | Visualiser les pourcentages discrets et groupés. | [leçon](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) | | 12 | Visualiser les relations | [Data Visualization](3-Data-Visualization/README.md) | Visualiser les connexions et corrélations entre ensembles de données et leurs variables. | [leçon](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) | -| 13 | Visualisations significatives | [Data Visualization](3-Data-Visualization/README.md) | Techniques et conseils pour rendre vos visualisations utiles pour une résolution efficace des problèmes et des insights. | [leçon](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) | -| 14 | Introduction au cycle de vie de la science des données | [Lifecycle](4-Data-Science-Lifecycle/README.md) | Introduction au cycle de vie de la science des données et à sa première étape d’acquisition et d’extraction des données. | [leçon](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) | -| 15 | Analyser | [Lifecycle](4-Data-Science-Lifecycle/README.md) | Cette phase du cycle de vie de la science des données se concentre sur les techniques d'analyse des données. | [leçon](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | | -| 16 | Communication | [Lifecycle](4-Data-Science-Lifecycle/README.md) | Cette phase du cycle de vie de la science des données se concentre sur la présentation des insights extraits des données d'une manière qui facilite la compréhension par les décideurs. | [leçon](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | | +| 13 | Visualisations significatives | [Data Visualization](3-Data-Visualization/README.md) | Techniques et conseils pour rendre vos visualisations précieuses pour une résolution efficace de problèmes et des insights. | [leçon](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) | +| 14 | Introduction au cycle de vie de la science des données | [Lifecycle](4-Data-Science-Lifecycle/README.md) | Introduction au cycle de vie de la science des données et sa première étape d’acquisition et d’extraction des données. | [leçon](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) | +| 15 | Analyse | [Lifecycle](4-Data-Science-Lifecycle/README.md) | Cette phase du cycle de vie de la science des données se concentre sur des techniques d’analyse des données. | [leçon](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | | +| 16 | Communication | [Lifecycle](4-Data-Science-Lifecycle/README.md) | Cette phase du cycle de vie de la science des données met l’accent sur la présentation des insights des données de manière à faciliter la compréhension aux décideurs. | [leçon](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | | | 17 | Science des données dans le cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Cette série de leçons introduit la science des données dans le cloud et ses avantages. | [leçon](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) et [Maud](https://twitter.com/maudstweets) | | 18 | Science des données dans le cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Entraînement de modèles avec des outils Low Code. |[leçon](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) et [Maud](https://twitter.com/maudstweets) | | 19 | Science des données dans le cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Déploiement de modèles avec Azure Machine Learning Studio. | [leçon](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) et [Maud](https://twitter.com/maudstweets) | -| 20 | Science des données sur le terrain | [In the Wild](6-Data-Science-In-Wild/README.md) | Projets de science des données appliqués dans le monde réel. | [leçon](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) | +| 20 | Science des données en pratique | [In the Wild](6-Data-Science-In-Wild/README.md) | Projets drivés par la science des données dans le monde réel. | [leçon](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) | -## Codespaces GitHub +## GitHub Codespaces Suivez ces étapes pour ouvrir cet exemple dans un Codespace : -1. Cliquez sur le menu déroulant Code et sélectionnez l'option Open with Codespaces. -2. Sélectionnez + New codespace en bas du panneau. -Pour plus d'informations, consultez la [documentation GitHub](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace). +1. Cliquez sur le menu déroulant Code et sélectionnez l’option Ouvrir avec Codespaces. +2. Sélectionnez + Nouveau codespace en bas du panneau. +Pour plus d’informations, consultez la [documentation GitHub](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace). ## VSCode Remote - Containers Suivez ces étapes pour ouvrir ce dépôt dans un conteneur en utilisant votre machine locale et VSCode avec l’extension VS Code Remote - Containers : -1. Si c’est votre première fois à utiliser un conteneur de développement, assurez-vous que votre système répond aux prérequis (c’est-à-dire avoir Docker installé) dans [la documentation de démarrage](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started). +1. Si c’est la première fois que vous utilisez un conteneur de développement, assurez-vous que votre système respecte les prérequis (par exemple avoir Docker installé) dans [la documentation de démarrage](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started). Pour utiliser ce dépôt, vous pouvez soit ouvrir le dépôt dans un volume Docker isolé : -**Note** : Sous le capot, cela utilisera la commande Remote-Containers : **Clone Repository in Container Volume...** pour cloner le code source dans un volume Docker plutôt que sur le système de fichiers local. Les [volumes](https://docs.docker.com/storage/volumes/) sont le mécanisme préféré pour la persistance des données des conteneurs. +**Note** : En coulisses, cela utilisera la commande Remote-Containers : **Clone Repository in Container Volume...** pour cloner le code source dans un volume Docker au lieu du système de fichiers local. Les [volumes](https://docs.docker.com/storage/volumes/) sont le mécanisme préféré pour la persistance des données du conteneur. -Ou ouvrez une version localement clonée ou téléchargée du dépôt : +Ou ouvrir une version clonée ou téléchargée localement du dépôt : -- Cloner ce dépôt sur votre système de fichiers local. -- Appuyez sur F1 et sélectionnez la commande **Remote-Containers: Open Folder in Container...**. -- Sélectionnez la copie clonée de ce dossier, attendez que le conteneur démarre, puis essayez. +- Clonez ce dépôt sur votre système de fichiers local. +- Appuyez sur F1 et sélectionnez la commande **Remote-Containers : Open Folder in Container...**. +- Sélectionnez la copie clonée de ce dossier, attendez que le conteneur démarre et essayez. ## Accès hors ligne -Vous pouvez consulter cette documentation hors ligne en utilisant [Docsify](https://docsify.js.org/#/). Forkez ce dépôt, [installez Docsify](https://docsify.js.org/#/quickstart) sur votre machine locale, puis dans le dossier racine de ce dépôt, tapez `docsify serve`. Le site web sera servi sur le port 3000 de votre localhost : `localhost:3000`. +Vous pouvez consulter cette documentation hors ligne en utilisant [Docsify](https://docsify.js.org/#/). Forkez ce dépôt, [installez Docsify](https://docsify.js.org/#/quickstart) sur votre machine locale, puis dans le dossier racine de ce dépôt, tapez `docsify serve`. Le site sera servi sur le port 3000 sur votre localhost : `localhost:3000`. -> Note, les notebooks ne seront pas rendus via Docsify, donc quand vous avez besoin d'exécuter un notebook, faites-le séparément dans VS Code avec un kernel Python. +> Note, les notebooks ne seront pas rendus via Docsify, donc lorsque vous devez exécuter un notebook, faites-le séparément dans VS Code avec un noyau Python. -## Autres cursus +## Autres programmes -Notre équipe produit d’autres cursus ! Découvrez : +Notre équipe produit d'autres programmes ! Découvrez : ### LangChain [![LangChain4j pour débutants](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) -[![LangChain.js pour débutants](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) +[![LangChain.js pour Débutants](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) --- ### Azure / Edge / MCP / Agents -[![AZD pour débutants](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Edge AI pour débutants](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![MCP pour débutants](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Agents IA pour débutants](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AZD pour Débutants](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Edge AI pour Débutants](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![MCP pour Débutants](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Agents IA pour Débutants](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) --- - -### Série IA Générative -[![IA Générative pour débutants](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) + +### Série Intelligence Artificielle Générative +[![IA Générative pour Débutants](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) [![IA Générative (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) [![IA Générative (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) [![IA Générative (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) --- - + ### Apprentissage Fondamental -[![ML pour débutants](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) -[![Science des données pour débutants](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) -[![IA pour débutants](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) -[![Cybersécurité pour débutants](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) -[![Développement Web pour débutants](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) -[![IoT pour débutants](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) -[![Développement XR pour débutants](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) +[![ML pour Débutants](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) +[![Science des Données pour Débutants](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) +[![IA pour Débutants](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) +[![Cybersécurité pour Débutants](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) +[![Développement Web pour Débutants](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) +[![IoT pour Débutants](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) +[![Développement XR pour Débutants](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- - + ### Série Copilot -[![Copilot pour programmation assistée par IA](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![Copilot pour Programmation Assistée par IA](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) [![Copilot pour C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) [![Aventure Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) -## Obtenir de l'aide +## Obtenir de l'Aide -**Des problèmes rencontrés ?** Consultez notre [Guide de dépannage](TROUBLESHOOTING.md) pour des solutions aux problèmes courants. +**Vous rencontrez des problèmes ?** Consultez notre [Guide de dépannage](TROUBLESHOOTING.md) pour des solutions aux problèmes courants. -Si vous êtes bloqué ou avez des questions sur la création d'applications IA. Rejoignez d'autres apprenants et développeurs expérimentés dans des discussions sur MCP. C'est une communauté bienveillante où les questions sont les bienvenues et où le savoir est partagé librement. +Si vous êtes bloqué ou avez des questions sur la création d'applications IA, rejoignez les autres apprenants et développeurs expérimentés pour des discussions autour de MCP. C'est une communauté bienveillante où les questions sont les bienvenues et les connaissances partagées librement. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Si vous avez des retours sur le produit ou des erreurs lors du développement, rendez-vous sur : +Si vous avez des retours produit ou rencontrez des erreurs lors de la création, visitez : [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- -**Clause de non-responsabilité** : -Ce document a été traduit à l’aide du service de traduction IA [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforcions d’assurer l’exactitude, veuillez noter que les traductions automatiques peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue d’origine doit être considéré comme la source faisant foi. Pour les informations critiques, une traduction professionnelle réalisée par un humain est recommandée. Nous déclinons toute responsabilité en cas de malentendus ou de mauvaises interprétations résultant de l’utilisation de cette traduction. +**Avertissement** : +Ce document a été traduit à l’aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforcions d’assurer l’exactitude, veuillez noter que les traductions automatiques peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue natale doit être considéré comme la source faisant foi. Pour des informations cruciales, une traduction professionnelle réalisée par un humain est recommandée. Nous déclinons toute responsabilité en cas de malentendus ou d’interprétations erronées résultant de l’utilisation de cette traduction. \ No newline at end of file diff --git a/translations/fr/SECURITY.md b/translations/fr/SECURITY.md index 549fde8f..fd7f63d0 100644 --- a/translations/fr/SECURITY.md +++ b/translations/fr/SECURITY.md @@ -1,12 +1,3 @@ - ## Sécurité Microsoft prend très au sérieux la sécurité de ses produits logiciels et services, y compris tous les dépôts de code source gérés via nos organisations GitHub, qui incluent [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), et [nos organisations GitHub](https://opensource.microsoft.com/). diff --git a/translations/fr/SUPPORT.md b/translations/fr/SUPPORT.md index 95508525..42ec9e12 100644 --- a/translations/fr/SUPPORT.md +++ b/translations/fr/SUPPORT.md @@ -1,12 +1,3 @@ - # Support ## Comment signaler des problèmes et obtenir de l'aide diff --git a/translations/fr/TROUBLESHOOTING.md b/translations/fr/TROUBLESHOOTING.md index ccf58889..54489fe7 100644 --- a/translations/fr/TROUBLESHOOTING.md +++ b/translations/fr/TROUBLESHOOTING.md @@ -1,12 +1,3 @@ - # Guide de dépannage Ce guide propose des solutions aux problèmes courants que vous pourriez rencontrer en travaillant avec le programme Data Science for Beginners. diff --git a/translations/fr/USAGE.md b/translations/fr/USAGE.md index a8e71a8a..7be792cc 100644 --- a/translations/fr/USAGE.md +++ b/translations/fr/USAGE.md @@ -1,12 +1,3 @@ - # Guide d'utilisation Ce guide fournit des exemples et des workflows courants pour utiliser le programme "Data Science for Beginners". diff --git a/translations/fr/docs/_sidebar.md b/translations/fr/docs/_sidebar.md index ebf19592..b5e67c71 100644 --- a/translations/fr/docs/_sidebar.md +++ b/translations/fr/docs/_sidebar.md @@ -1,12 +1,3 @@ - - Introduction - [Définir la science des données](../1-Introduction/01-defining-data-science/README.md) - [Éthique de la science des données](../1-Introduction/02-ethics/README.md) diff --git a/translations/fr/examples/README.md b/translations/fr/examples/README.md index fd8d4673..ec0b72ff 100644 --- a/translations/fr/examples/README.md +++ b/translations/fr/examples/README.md @@ -1,12 +1,3 @@ - # Exemples de Data Science pour Débutants Bienvenue dans le répertoire des exemples ! Cette collection d'exemples simples et bien commentés est conçue pour vous aider à débuter en data science, même si vous êtes complètement novice. diff --git a/translations/fr/for-teachers.md b/translations/fr/for-teachers.md index 6e2fac5b..aac0009f 100644 --- a/translations/fr/for-teachers.md +++ b/translations/fr/for-teachers.md @@ -1,12 +1,3 @@ - ## Pour les enseignants Souhaitez-vous utiliser ce programme dans votre classe ? N'hésitez pas ! diff --git a/translations/fr/quiz-app/README.md b/translations/fr/quiz-app/README.md index 3a326628..7844ac1e 100644 --- a/translations/fr/quiz-app/README.md +++ b/translations/fr/quiz-app/README.md @@ -1,12 +1,3 @@ - # Quiz Ces quiz sont les quiz avant et après les cours du programme de science des données disponible sur https://aka.ms/datascience-beginners diff --git a/translations/fr/sketchnotes/README.md b/translations/fr/sketchnotes/README.md index 0a7b0384..48458555 100644 --- a/translations/fr/sketchnotes/README.md +++ b/translations/fr/sketchnotes/README.md @@ -1,12 +1,3 @@ - Retrouvez toutes les sketchnotes ici ! ## Crédits diff --git a/translations/hk/1-Introduction/01-defining-data-science/README.md b/translations/hk/1-Introduction/01-defining-data-science/README.md index 4c915179..a4a360b5 100644 --- a/translations/hk/1-Introduction/01-defining-data-science/README.md +++ b/translations/hk/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![數據科學的定義影片](../../../../translated_images/hk/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) +[![數據科學的定義影片](../../../../translated_images/zh-HK/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ CO_OP_TRANSLATOR_METADATA: 在這次挑戰中，我們將透過分析文本來尋找與數據科學領域相關的概念。我們會選取一篇關於數據科學的維基百科文章，下載並處理文本，然後建立一個像這樣的文字雲： -![數據科學文字雲](../../../../translated_images/hk/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) +![數據科學文字雲](../../../../translated_images/zh-HK/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) 請訪問 [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') 閱讀程式碼。你也可以執行程式碼，並即時查看它如何進行所有數據轉換。 diff --git a/translations/hk/1-Introduction/04-stats-and-probability/README.md b/translations/hk/1-Introduction/04-stats-and-probability/README.md index 1a20327f..21ef75da 100644 --- a/translations/hk/1-Introduction/04-stats-and-probability/README.md +++ b/translations/hk/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 統計學與概率論是數學中兩個密切相關的領域，對於數據科學非常重要。雖然在沒有深入數學知識的情況下也可以處理數據，但了解一些基本概念仍然是有益的。在這裡，我們將提供一個簡短的介紹，幫助你入門。 -[![介紹影片](../../../../translated_images/hk/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) +[![介紹影片](../../../../translated_images/zh-HK/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ CO_OP_TRANSLATOR_METADATA: 我們只能討論變數落在某個值區間內的概率，例如 P(t1≤X2)。在這種情況下，概率分佈由 **概率密度函數** p(x) 描述，其公式如下： -![P(t_1\le X 更多相關性和協方差的例子可以在 [附帶的筆記本](notebook.ipynb) 中找到。 diff --git a/translations/hk/1-Introduction/README.md b/translations/hk/1-Introduction/README.md index 76eeebcd..aaa0c6e0 100644 --- a/translations/hk/1-Introduction/README.md +++ b/translations/hk/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 數據科學入門 -![數據運作中](../../../translated_images/hk/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) +![數據運作中](../../../translated_images/zh-HK/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > 圖片由 Stephen Dawson 提供，來源於 Unsplash 在這些課程中，你將了解什麼是數據科學，並學習數據科學家必須考慮的倫理問題。你還會學習數據的定義，並簡單了解統計學和概率論，這些是數據科學的核心學術領域。 diff --git a/translations/hk/2-Working-With-Data/07-python/README.md b/translations/hk/2-Working-With-Data/07-python/README.md index c7367a42..a4b216f6 100644 --- a/translations/hk/2-Working-With-Data/07-python/README.md +++ b/translations/hk/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | 使用 Python - _由 [@nitya](https://twitter.com/nitya) 繪製的手繪筆記_ | -[![介紹影片](../../../../translated_images/hk/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) +[![介紹影片](../../../../translated_images/zh-HK/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) 雖然數據庫提供了非常高效的方式來存儲數據並使用查詢語言進行查詢，但最靈活的數據處理方式是編寫自己的程序來操作數據。在許多情況下，使用數據庫查詢可能更有效。然而，在某些需要更複雜數據處理的情況下，使用 SQL 並不容易完成這些操作。 @@ -74,7 +74,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![時間序列圖](../../../../translated_images/hk/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) +![時間序列圖](../../../../translated_images/zh-HK/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) 假設每週我們都會為朋友舉辦派對，並額外準備 10 盒冰淇淋。我們可以創建另一個以週為索引的 Series 來展示這一點： ```python @@ -85,7 +85,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W" total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![時間序列圖](../../../../translated_images/hk/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) +![時間序列圖](../../../../translated_images/zh-HK/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **注意**：我們並未使用簡單的語法 `total_items+additional_items`。如果這樣做，結果 Series 中會有許多 `NaN`（*非數值*）值。這是因為在 `additional_items` Series 的某些索引點上存在缺失值，而將 `NaN` 與任何值相加的結果都是 `NaN`。因此，我們需要在相加時指定 `fill_value` 參數。 @@ -94,7 +94,7 @@ total_items.plot() monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![每月時間序列平均值](../../../../translated_images/hk/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) +![每月時間序列平均值](../../../../translated_images/zh-HK/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -220,7 +220,7 @@ df = pd.read_csv('file.csv') 由於我們想展示如何處理數據，我們邀請你打開 [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) 並從頭到尾閱讀。你也可以執行單元格，並完成我們在最後留下的一些挑戰。 -![COVID 傳播](../../../../translated_images/hk/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) +![COVID 傳播](../../../../translated_images/zh-HK/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > 如果你不知道如何在 Jupyter Notebook 中運行代碼，可以查看 [這篇文章](https://soshnikov.com/education/how-to-execute-notebooks-from-github/)。 @@ -242,7 +242,7 @@ df = pd.read_csv('file.csv') 打開 [`notebook-papers.ipynb`](notebook-papers.ipynb) 並從頭到尾閱讀。你也可以執行單元格，並完成我們在最後留下的一些挑戰。 -![COVID 醫療處理](../../../../translated_images/hk/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) +![COVID 醫療處理](../../../../translated_images/zh-HK/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## 處理圖片數據 diff --git a/translations/hk/2-Working-With-Data/README.md b/translations/hk/2-Working-With-Data/README.md index cfce3a8b..2b3c51b3 100644 --- a/translations/hk/2-Working-With-Data/README.md +++ b/translations/hk/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 處理數據 -![數據之愛](../../../translated_images/hk/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) +![數據之愛](../../../translated_images/zh-HK/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > 照片由 Alexander Sinn 提供，來自 Unsplash 在這些課程中，你將學習一些管理、操作和應用數據的方法。你會了解關聯式和非關聯式數據庫，以及數據如何存儲在其中。你將學習使用 Python 管理數據的基礎知識，並探索多種使用 Python 管理和挖掘數據的方法。 diff --git a/translations/hk/3-Data-Visualization/12-visualization-relationships/README.md b/translations/hk/3-Data-Visualization/12-visualization-relationships/README.md index 8991a643..a0b0776c 100644 --- a/translations/hk/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/hk/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/hk/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) +![scatterplot 1](../../../../translated_images/zh-HK/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) 接下來，使用蜂蜜色調展示價格隨年份的變化。您可以通過添加 'hue' 參數來展示每年的變化： @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/hk/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) +![scatterplot 2](../../../../translated_images/zh-HK/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) 通過這種色彩方案的改變，您可以清楚地看到蜂蜜每磅價格隨年份的明顯增長趨勢。事實上，如果您查看數據中的樣本集（例如選擇一個州，亞利桑那州），您可以看到價格每年增長的模式，只有少數例外： @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` 您可以看到點的大小逐漸增大。 -![scatterplot 3](../../../../translated_images/hk/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) +![scatterplot 3](../../../../translated_images/zh-HK/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) 這是否是一個簡單的供需問題？由於氣候變化和蜂群崩潰等因素，是否每年可供購買的蜂蜜減少，因此價格上漲？ @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` 答案：是的，但在2003年左右有一些例外： -![line chart 1](../../../../translated_images/hk/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) +![line chart 1](../../../../translated_images/zh-HK/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ 由於 Seaborn 將數據聚合到一條線上，它通過繪製均值和均值周圍的95%置信區間來顯示每個 x 值的多個測量值。[來源](https://seaborn.pydata.org/tutorial/relational.html)。這種耗時的行為可以通過添加 `ci=None` 禁用。 @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/hk/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) +![line chart 2](../../../../translated_images/zh-HK/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) 答案：並不完全。如果您查看總生產量，實際上在那一年似乎有所增加，儘管總體而言蜂蜜的生產量在這些年中呈下降趨勢。 @@ -139,7 +139,7 @@ sns.relplot( ``` 在這個視覺化中，您可以比較每年的每群產量和蜂群數量，並將列的 wrap 設置為3： -![facet grid](../../../../translated_images/hk/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) +![facet grid](../../../../translated_images/zh-HK/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) 對於這個數據集，關於蜂群數量和每群產量，按年份和州比較並沒有特別突出的地方。是否有其他方式來尋找這兩個變量之間的相關性？ @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/hk/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) +![superimposed plots](../../../../translated_images/zh-HK/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) 雖然在2003年沒有明顯的異常，但這讓我們以一個稍微樂觀的結論結束本課：儘管蜂群數量總體上在下降，但蜂群數量正在穩定，即使每群產量在減少。 diff --git a/translations/hk/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/hk/3-Data-Visualization/R/09-visualization-quantities/README.md index c27d50f0..9ef70619 100644 --- a/translations/hk/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/hk/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` 在這裡，你安裝了 `ggplot2` 套件，然後使用 `library("ggplot2")` 命令將其導入工作空間。要在 ggplot 中繪製任何圖表，使用 `ggplot()` 函數並指定數據集、x 和 y 變量作為屬性。在這種情況下，我們使用 `geom_line()` 函數，因為我們的目標是繪製折線圖。 -![MaxWingspan-lineplot](../../../../../translated_images/hk/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) +![MaxWingspan-lineplot](../../../../../translated_images/zh-HK/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) 你立即注意到什麼？似乎至少有一個異常值——那是一個相當大的翼展！2000+ 厘米的翼展超過 20 米——明尼蘇達州有翼龍在飛嗎？讓我們調查一下。 @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` 我們在 `theme` 中指定角度，並在 `xlab()` 和 `ylab()` 中分別指定 x 和 y 軸標籤。`ggtitle()` 為圖表/圖形命名。 -![MaxWingspan-lineplot-improved](../../../../../translated_images/hk/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/zh-HK/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) 即使將標籤的旋轉設置為 45 度，仍然有太多標籤難以閱讀。讓我們嘗試另一種策略：僅標記那些異常值並在圖表內設置標籤。你可以使用散點圖來為標籤留出更多空間： @@ -100,7 +100,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + 你發現了什麼？ -![MaxWingspan-scatterplot](../../../../../translated_images/hk/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) +![MaxWingspan-scatterplot](../../../../../translated_images/zh-HK/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## 篩選數據 @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` 我們創建了一個新的數據框 `birds_filtered`，然後繪製了一個散點圖。通過篩選掉異常值，你的數據現在更加一致且易於理解。 -![MaxWingspan-scatterplot-improved](../../../../../translated_images/hk/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/zh-HK/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) 現在我們至少在翼展方面有了一個更乾淨的數據集，讓我們了解更多關於這些鳥類的信息。 @@ -162,7 +162,7 @@ birds_filtered %>% group_by(Category) %>% ``` 在以下代碼片段中，我們安裝了 [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) 和 [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) 套件，以幫助操作和分組數據以繪製堆疊條形圖。首先，你按鳥類的 `Category` 分組數據，然後總結 `MinLength`、`MaxLength`、`MinBodyMass`、`MaxBodyMass`、`MinWingspan`、`MaxWingspan` 列。然後，使用 `ggplot2` 套件繪製條形圖並指定不同類別的顏色和標籤。 -![堆疊條形圖](../../../../../translated_images/hk/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) +![堆疊條形圖](../../../../../translated_images/zh-HK/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) 然而，這個條形圖難以閱讀，因為有太多未分組的數據。你需要選擇你想要繪製的數據，所以讓我們看看基於鳥類類別的鳥類長度。 @@ -177,7 +177,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` 你首先計算 `Category` 列中的唯一值，然後將它們排序到一個新的數據框 `birds_count` 中。這些排序後的數據在相同層次中進行分級，以便按排序方式繪製。使用 `ggplot2`，你然後在條形圖中繪製數據。`coord_flip()` 繪製水平條形圖。 -![類別-長度](../../../../../translated_images/hk/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) +![類別-長度](../../../../../translated_images/zh-HK/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) 這個條形圖很好地展示了每個類別中鳥類的數量。一眼就能看出，在這個地區最多的鳥類是鴨/鵝/水禽類別。明尼蘇達州是“萬湖之地”，所以這並不令人驚訝！ @@ -200,7 +200,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` 我們按 `Category` 分組 `birds_filtered` 數據，然後繪製條形圖。 -![比較數據](../../../../../translated_images/hk/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) +![比較數據](../../../../../translated_images/zh-HK/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) 這裡沒有什麼令人驚訝的：蜂鳥的最大長度比鵜鶘或鵝要小得多。當數據符合邏輯時，這是件好事！ @@ -212,7 +212,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![疊加值](../../../../../translated_images/hk/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) +![疊加值](../../../../../translated_images/zh-HK/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 挑戰 diff --git a/translations/hk/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/hk/3-Data-Visualization/R/10-visualization-distributions/README.md index 64cc975e..0feffff7 100644 --- a/translations/hk/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/hk/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![每目最大長度](../../../../../translated_images/hk/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) +![每目最大長度](../../../../../translated_images/zh-HK/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) 這提供了每個鳥類目身體長度分佈的概覽，但這並不是顯示真實分佈的最佳方式。這個任務通常通過創建直方圖來完成。 @@ -57,7 +57,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![整個數據集的分佈](../../../../../translated_images/hk/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) +![整個數據集的分佈](../../../../../translated_images/zh-HK/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) 如你所見，這個數據集中大多數 400 多種鳥類的最大體重都在 2000 以下。通過將 `bins` 參數設置為更高的數值（例如 30），可以獲得更多的數據洞察： @@ -65,7 +65,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![30 個區間的分佈](../../../../../translated_images/hk/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) +![30 個區間的分佈](../../../../../translated_images/zh-HK/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) 這個圖表以更細緻的方式顯示了分佈。通過僅選擇給定範圍內的數據，可以創建一個不那麼偏向左側的圖表： @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![篩選後的直方圖](../../../../../translated_images/hk/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) +![篩選後的直方圖](../../../../../translated_images/zh-HK/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ 試試其他篩選條件和數據點。若要查看數據的完整分佈，移除 `['MaxBodyMass']` 篩選條件以顯示帶標籤的分佈。 @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` 可以看到這兩個元素之間沿著預期軸線存在預期的相關性，並且有一個特別強的匯聚點： -![2D 圖表](../../../../../translated_images/hk/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) +![2D 圖表](../../../../../translated_images/zh-HK/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) 直方圖對於數值數據效果很好。如果需要查看基於文本數據的分佈該怎麼辦？ @@ -123,7 +123,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![翼展與保育狀況的對比](../../../../../translated_images/hk/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) +![翼展與保育狀況的對比](../../../../../translated_images/zh-HK/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) 最小翼展與保育狀況之間似乎沒有明顯的相關性。使用這種方法測試數據集的其他元素。你也可以嘗試不同的篩選條件。你發現了任何相關性嗎？ @@ -137,7 +137,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![密度圖](../../../../../translated_images/hk/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) +![密度圖](../../../../../translated_images/zh-HK/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) 你可以看到這個圖表反映了之前的最小翼展數據，只是更平滑了一些。如果你想重新查看第二個圖表中那條不平滑的最大體重線，可以使用這種方法將其非常平滑地重現： @@ -145,7 +145,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![體重密度](../../../../../translated_images/hk/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) +![體重密度](../../../../../translated_images/zh-HK/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) 如果你想要一條平滑但不過於平滑的線，可以編輯 `adjust` 參數： @@ -153,7 +153,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![較少平滑的體重線](../../../../../translated_images/hk/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) +![較少平滑的體重線](../../../../../translated_images/zh-HK/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ 閱讀此類圖表可用的參數並進行實驗！ @@ -163,7 +163,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![每目體重密度](../../../../../translated_images/hk/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) +![每目體重密度](../../../../../translated_images/zh-HK/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 挑戰 diff --git a/translations/hk/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/hk/3-Data-Visualization/R/11-visualization-proportions/README.md index d1f53125..c7129cfa 100644 --- a/translations/hk/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/hk/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -93,7 +93,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` 完成，一個圓餅圖展示了根據這兩類蘑菇的比例數據。正確排列標籤的順序非常重要，尤其是在這裡，因此請務必核對標籤數組的構建順序！ -![圓餅圖](../../../../../translated_images/hk/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) +![圓餅圖](../../../../../translated_images/zh-HK/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## 甜甜圈圖！ @@ -128,7 +128,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![甜甜圈圖](../../../../../translated_images/hk/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) +![甜甜圈圖](../../../../../translated_images/zh-HK/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) 這段代碼使用了兩個庫——ggplot2 和 webr。使用 webr 庫的 PieDonut 函數，我們可以輕鬆創建甜甜圈圖！ @@ -166,7 +166,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual 使用華夫圖，你可以清楚地看到這個蘑菇數據集中菌蓋顏色的比例。有趣的是，有許多綠色菌蓋的蘑菇！ -![華夫圖](../../../../../translated_images/hk/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) +![華夫圖](../../../../../translated_images/zh-HK/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) 在這節課中，你學到了三種視覺化比例的方法。首先，你需要將數據分組到分類中，然後決定哪種方式最適合展示數據——圓餅圖、甜甜圈圖或華夫圖。這些方法都很有趣，能讓用戶快速了解數據集。 diff --git a/translations/hk/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/hk/3-Data-Visualization/R/12-visualization-relationships/README.md index 86337905..ee0a00d6 100644 --- a/translations/hk/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/hk/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/hk/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) +![scatterplot 1](../../../../../translated_images/zh-HK/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) 現在，使用蜂蜜色彩方案展示價格隨年份的變化。您可以通過添加 'scale_color_gradientn' 參數來顯示每年的變化： @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/hk/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) +![scatterplot 2](../../../../../translated_images/zh-HK/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) 使用這種色彩方案，您可以看到蜂蜜每磅價格隨年份的明顯增長趨勢。事實上，如果您查看數據中的樣本集（例如選擇亞利桑那州），您可以看到價格每年逐漸上漲，僅有少數例外： @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` 您可以看到點的大小逐漸增大。 -![scatterplot 3](../../../../../translated_images/hk/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) +![scatterplot 3](../../../../../translated_images/zh-HK/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) 這是否是一個簡單的供需問題？由於氣候變化和蜂群崩潰等因素，是否每年可供購買的蜂蜜減少，導致價格上漲？ @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` 答案：是的，但在2003年左右有一些例外： -![line chart 1](../../../../../translated_images/hk/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) +![line chart 1](../../../../../translated_images/zh-HK/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) 問題：那麼在2003年，我們是否也能看到蜂蜜供應的激增？如果您查看每年的總生產量呢？ @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/hk/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) +![line chart 2](../../../../../translated_images/zh-HK/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) 答案：並不完全。如果您查看總生產量，實際上在那一年似乎有所增加，儘管總體而言蜂蜜的生產量在這些年中呈下降趨勢。 @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` 在此視覺化中，您可以比較每群產量和蜂群數量每年每州的變化，並將列數設置為3： -![facet grid](../../../../../translated_images/hk/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) +![facet grid](../../../../../translated_images/zh-HK/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) 對於此數據集，關於蜂群數量和每群產量每年每州的變化，並未有特別突出的地方。是否有其他方式可以找到這兩個變量之間的相關性？ @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/hk/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) +![superimposed plots](../../../../../translated_images/zh-HK/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) 雖然在2003年並未有明顯的異常，但這讓我們可以以一個稍微樂觀的結論結束本課：儘管蜂群數量總體上在下降，但蜂群數量正在穩定，即使每群產量在減少。 diff --git a/translations/hk/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/hk/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index bfb15291..8d4cb860 100644 --- a/translations/hk/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/hk/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: 即使數據科學家謹慎地為正確的數據選擇了合適的圖表，仍然有許多方法可以以誤導的方式展示數據，通常是為了證明某個觀點，卻犧牲了數據的真實性。有許多誤導性圖表和信息圖的例子！ -[![Alberto Cairo 的《How Charts Lie》](../../../../../translated_images/hk/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![Alberto Cairo 的《How Charts Lie》](../../../../../translated_images/zh-HK/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 點擊上方圖片觀看關於誤導性圖表的會議演講 這張圖表反轉了 X 軸，根據日期顯示了與事實相反的內容： -![糟糕的圖表 1](../../../../../translated_images/hk/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) +![糟糕的圖表 1](../../../../../translated_images/zh-HK/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [這張圖表](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) 更加誤導，因為視覺上吸引人注意的是右側，讓人得出隨時間推移各縣的 COVID 病例數下降的結論。事實上，如果仔細查看日期，你會發現它們被重新排列以製造出誤導性的下降趨勢。 -![糟糕的圖表 2](../../../../../translated_images/hk/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) +![糟糕的圖表 2](../../../../../translated_images/zh-HK/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) 這個臭名昭著的例子使用顏色和反轉的 Y 軸來誤導：原本應該得出槍支友好立法通過後槍支死亡率激增的結論，卻讓人誤以為情況正好相反： -![糟糕的圖表 3](../../../../../translated_images/hk/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) +![糟糕的圖表 3](../../../../../translated_images/zh-HK/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) 這張奇怪的圖表展示了比例如何被操控，效果令人捧腹： -![糟糕的圖表 4](../../../../../translated_images/hk/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) +![糟糕的圖表 4](../../../../../translated_images/zh-HK/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) 比較不可比的事物是另一種不正當的手段。有一個[精彩的網站](https://tylervigen.com/spurious-correlations)專門展示「虛假的相關性」，例如顯示緬因州的離婚率與人造奶油的消耗量之間的「事實」相關性。一個 Reddit 群組也收集了[糟糕的數據使用](https://www.reddit.com/r/dataisugly/top/?t=all)。 @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: 如果你的數據在 X 軸上是文本且冗長，可以將文本角度調整以提高可讀性。[plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) 提供了 3D 繪圖功能，如果你的數據支持它，可以使用它來生成更高級的數據視覺化。 -![3D 圖表](../../../../../translated_images/hk/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) +![3D 圖表](../../../../../translated_images/zh-HK/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## 動畫和 3D 圖表展示 如今一些最好的數據視覺化是動畫化的。Shirley Wu 使用 D3 創作了令人驚嘆的作品，例如「[電影之花](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)」，每朵花都是一部電影的視覺化。另一個例子是《衛報》的「Bussed Out」，這是一個結合 Greensock 和 D3 的視覺化與滾動敘事文章格式的互動體驗，展示了紐約市如何通過將無家可歸者送出城市來處理其無家可歸問題。 -![Bussed Out](../../../../../translated_images/hk/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) +![Bussed Out](../../../../../translated_images/zh-HK/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > 「Bussed Out: How America Moves its Homeless」來自[衛報](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study)。視覺化由 Nadieh Bremer 和 Shirley Wu 創作 @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: 你將完成一個網頁應用，展示這個社交網絡的動畫視圖。它使用了一個庫來創建[網絡視覺化](https://github.com/emiliorizzo/vue-d3-network)，基於 Vue.js 和 D3。當應用運行時，你可以在屏幕上拖動節點，重新排列數據。 -![危險關係](../../../../../translated_images/hk/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) +![危險關係](../../../../../translated_images/zh-HK/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## 項目：使用 D3.js 構建一個展示網絡的圖表 diff --git a/translations/hk/3-Data-Visualization/README.md b/translations/hk/3-Data-Visualization/README.md index 46e61938..416664c7 100644 --- a/translations/hk/3-Data-Visualization/README.md +++ b/translations/hk/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 視覺化 -![一隻蜜蜂停在薰衣草花上](../../../translated_images/hk/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) +![一隻蜜蜂停在薰衣草花上](../../../translated_images/zh-HK/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > 照片由 Jenna Lee 提供，來源於 Unsplash 視覺化數據是數據科學家最重要的任務之一。圖片勝過千言萬語，視覺化可以幫助你識別數據中的各種有趣部分，例如峰值、異常值、分組、趨勢等等，這些都能幫助你理解數據背後的故事。 diff --git a/translations/hk/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/hk/4-Data-Science-Lifecycle/14-Introduction/README.md index 0b32a3da..0c6748c8 100644 --- a/translations/hk/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/hk/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 本課程將重點介紹生命周期中的三個部分：捕獲、處理和維護。 -![數據科學生命周期圖](../../../../translated_images/hk/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) +![數據科學生命周期圖](../../../../translated_images/zh-HK/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > 圖片來源：[Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## 捕獲 @@ -98,7 +98,7 @@ CO_OP_TRANSLATOR_METADATA: |團隊數據科學過程 (TDSP)|跨行業數據挖掘標準過程 (CRISP-DM)| |--|--| -|![團隊數據科學生命周期](../../../../translated_images/hk/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![數據科學過程聯盟圖片](../../../../translated_images/hk/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | +|![團隊數據科學生命周期](../../../../translated_images/zh-HK/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![數據科學過程聯盟圖片](../../../../translated_images/zh-HK/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | 圖片來源：[Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | 圖片來源：[Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [課後測驗](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/hk/4-Data-Science-Lifecycle/README.md b/translations/hk/4-Data-Science-Lifecycle/README.md index 2bb7f14b..383d2064 100644 --- a/translations/hk/4-Data-Science-Lifecycle/README.md +++ b/translations/hk/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 數據科學生命周期 -![communication](../../../translated_images/hk/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) +![communication](../../../translated_images/zh-HK/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > 圖片由 Headway 提供，來自 Unsplash 在這些課程中，你將探索數據科學生命周期的一些方面，包括數據的分析和溝通。 diff --git a/translations/hk/5-Data-Science-In-Cloud/README.md b/translations/hk/5-Data-Science-In-Cloud/README.md index ac12d145..48777b76 100644 --- a/translations/hk/5-Data-Science-In-Cloud/README.md +++ b/translations/hk/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # 雲端中的數據科學 -![cloud-picture](../../../translated_images/hk/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) +![cloud-picture](../../../translated_images/zh-HK/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > 圖片來源：[Jelleke Vanooteghem](https://unsplash.com/@ilumire) 來自 [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) 當涉及到使用大數據進行數據科學時，雲端可以成為改變遊戲規則的關鍵。在接下來的三節課中，我們將了解什麼是雲端以及為什麼它非常有用。我們還將探索一個心臟衰竭數據集，並建立一個模型來幫助評估某人發生心臟衰竭的可能性。我們將利用雲端的強大功能來訓練、部署和以兩種不同的方式使用模型。一種方式是僅使用用戶界面，以低代碼/無代碼的方式進行；另一種方式是使用 Azure 機器學習軟件開發工具包 (Azure ML SDK)。 -![project-schema](../../../translated_images/hk/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) +![project-schema](../../../translated_images/zh-HK/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### 主題 diff --git a/translations/hk/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/hk/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 64540a4b..4d5cd0a5 100644 --- a/translations/hk/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/hk/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: * [數據科學在醫療保健中的應用](https://data-flair.training/blogs/data-science-in-healthcare/) - 強調應用包括醫學影像（例如 MRI、X光、CT掃描）、基因組學（DNA測序）、藥物開發（風險評估、成功預測）、預測分析（患者護理和供應物流）、疾病追蹤和預防等。 -![數據科學在現實世界中的應用](../../../../translated_images/hk/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) 圖片來源：[Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![數據科學在現實世界中的應用](../../../../translated_images/zh-HK/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) 圖片來源：[Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) 該圖展示了其他領域和應用數據科學技術的例子。想探索更多應用？查看下面的[回顧與自學](../../../../6-Data-Science-In-Wild/20-Real-World-Examples)部分。 diff --git a/translations/hk/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/hk/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index d710c085..d2e049fa 100644 --- a/translations/hk/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/hk/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Explorer界面（如下圖所示）允許你選擇一個數據集（從提供的 2. 探索數據集[目錄](https://planetarycomputer.microsoft.com/catalog)——了解每個數據集的用途。 3. 使用Explorer——選擇一個感興趣的數據集，選擇相關的查詢和渲染選項。 -![行星電腦Explorer](../../../../translated_images/hk/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) +![行星電腦Explorer](../../../../translated_images/zh-HK/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `你的任務：` 現在，研究瀏覽器中渲染的可視化，並回答以下問題： diff --git a/translations/hk/CONTRIBUTING.md b/translations/hk/CONTRIBUTING.md index 0d262c71..1a86a4c3 100644 --- a/translations/hk/CONTRIBUTING.md +++ b/translations/hk/CONTRIBUTING.md @@ -311,7 +311,7 @@ def calculate_mean(data): import pandas as pd ``` ```` -- 為圖片添加替代文字：`![Alt text](../../translated_images/hk/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` +- 為圖片添加替代文字：`![Alt text](../../translated_images/zh-HK/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - 保持合理的行長（約 80-100 字元） ### Python diff --git a/translations/hk/README.md b/translations/hk/README.md index 25f639d9..6dea364c 100644 --- a/translations/hk/README.md +++ b/translations/hk/README.md @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: **🙏 特別感謝 🙏 我們的 [Microsoft 學生大使](https://studentambassadors.microsoft.com/) 作者、審查者及內容貢獻者，** 包括 Aaryan Arora、[Aditya Garg](https://github.com/AdityaGarg00)、[Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/)、[Ankita Singh](https://www.linkedin.com/in/ankitasingh007)、[Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/)、[Arpita Das](https://www.linkedin.com/in/arpitadas01/)、ChhailBihari Dubey、[Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor)、[Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb)、[Majd Safi](https://www.linkedin.com/in/majd-s/)、[Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/)、[Miguel Correa](https://www.linkedin.com/in/miguelmque/)、[Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119)、[Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum)、[Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/)、[Rohit Yadav](https://www.linkedin.com/in/rty2423)、Samridhi Sharma、[Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200)、[Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/)、[Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/)、Yogendrasingh Pawar、[Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/)、[Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/hk/00-Title.8af36cd35da1ac55.webp)| +|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/zh-HK/00-Title.8af36cd35da1ac55.webp)| |:---:| | 初學者數據科學 - _筆記速寫由 [@nitya](https://twitter.com/nitya) 製作_ | @@ -61,7 +61,7 @@ CO_OP_TRANSLATOR_METADATA: 我們正在 Discord 舉辦 Learn with AI 系列活動，詳情及加入請見 [Learn with AI 系列](https://aka.ms/learnwithai/discord)。活動期間為 2025 年 9 月 18 日至 30 日。你將學到如何使用 GitHub Copilot 進行數據科學的技巧。 -![Learn with AI series](../../../../translated_images/hk/1.2b28cdc6205e26fe.webp) +![Learn with AI series](../../../../translated_images/zh-HK/1.2b28cdc6205e26fe.webp) # 你是學生嗎？ @@ -141,7 +141,7 @@ CO_OP_TRANSLATOR_METADATA: ## 課程列表 -|![ Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/hk/00-Roadmap.4905d6567dff4753.webp)| +|![ Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/zh-HK/00-Roadmap.4905d6567dff4753.webp)| |:---:| | 資料科學初學者路線圖 - _草圖筆記由 [@nitya](https://twitter.com/nitya) 製作_ | diff --git a/translations/hk/sketchnotes/README.md b/translations/hk/sketchnotes/README.md index 9efed63d..69c2cbcb 100644 --- a/translations/hk/sketchnotes/README.md +++ b/translations/hk/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: Nitya Narasimhan，藝術家 -![roadmap sketchnote](../../../translated_images/hk/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) +![roadmap sketchnote](../../../translated_images/zh-HK/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) **免責聲明**： 本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋不承擔責任。 \ No newline at end of file diff --git a/translations/mo/1-Introduction/01-defining-data-science/README.md b/translations/mo/1-Introduction/01-defining-data-science/README.md index a1292dc2..867c0561 100644 --- a/translations/mo/1-Introduction/01-defining-data-science/README.md +++ b/translations/mo/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![定義數據科學影片](../../../../translated_images/mo/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) +[![定義數據科學影片](../../../../translated_images/zh-MO/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ CO_OP_TRANSLATOR_METADATA: 在這個挑戰中，我們將透過分析文本來尋找與資料科學領域相關的概念。我們會選取一篇關於資料科學的維基百科文章，下載並處理文本，然後建立一個像這樣的文字雲： -![資料科學文字雲](../../../../translated_images/mo/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) +![資料科學文字雲](../../../../translated_images/zh-MO/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) 請訪問 [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') 來閱讀程式碼。你也可以執行程式碼，並即時查看它如何進行所有的資料轉換。 diff --git a/translations/mo/1-Introduction/04-stats-and-probability/README.md b/translations/mo/1-Introduction/04-stats-and-probability/README.md index 6d944906..902709a6 100644 --- a/translations/mo/1-Introduction/04-stats-and-probability/README.md +++ b/translations/mo/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 統計學與機率論是數學中兩個密切相關的領域，對於數據科學非常重要。雖然在沒有深入數學知識的情況下也可以處理數據，但了解一些基本概念仍然是有益的。在這裡，我們將提供一個簡短的介紹，幫助你入門。 -[![介紹影片](../../../../translated_images/mo/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) +[![介紹影片](../../../../translated_images/zh-MO/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ CO_OP_TRANSLATOR_METADATA: 我們只能討論變數落在某個值區間內的機率，例如 P(t1≤X2)。在這種情況下，機率分佈由 **機率密度函數** p(x) 描述，其公式如下： -![P(t_1\le X 更多關於相關性和協方差的例子可以在 [配套筆記本](notebook.ipynb) 中找到。 diff --git a/translations/mo/1-Introduction/README.md b/translations/mo/1-Introduction/README.md index 24682751..0b103bad 100644 --- a/translations/mo/1-Introduction/README.md +++ b/translations/mo/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 資料科學簡介 -![數據運作](../../../translated_images/mo/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) +![數據運作](../../../translated_images/zh-MO/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > 照片由 Stephen Dawson 提供，來源於 Unsplash 在這些課程中，您將了解資料科學的定義，並學習作為資料科學家必須考慮的倫理問題。此外，您還會學習資料的定義，並簡單了解統計學和機率，這些是資料科學的核心學術領域。 diff --git a/translations/mo/2-Working-With-Data/07-python/README.md b/translations/mo/2-Working-With-Data/07-python/README.md index 15bd8867..138106e2 100644 --- a/translations/mo/2-Working-With-Data/07-python/README.md +++ b/translations/mo/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | 使用 Python - _由 [@nitya](https://twitter.com/nitya) 繪製的速記筆記_ | -[![介紹影片](../../../../translated_images/mo/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) +[![介紹影片](../../../../translated_images/zh-MO/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) 雖然資料庫提供了非常高效的方式來存儲數據並使用查詢語言進行查詢，但最靈活的數據處理方式是編寫自己的程式來操作數據。在許多情況下，使用資料庫查詢可能更有效。然而，當需要更複雜的數據處理時，使用 SQL 可能不容易完成。 @@ -74,7 +74,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![時間序列圖](../../../../translated_images/mo/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) +![時間序列圖](../../../../translated_images/zh-MO/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) 假設每週我們都會為朋友舉辦派對，並額外準備 10 盒冰淇淋。我們可以創建另一個以週為索引的 Series 來展示這一點： ```python @@ -85,7 +85,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W" total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![時間序列圖](../../../../translated_images/mo/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) +![時間序列圖](../../../../translated_images/zh-MO/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **注意**：我們沒有使用簡單的語法 `total_items+additional_items`。如果使用該語法，我們會在結果 Series 中得到許多 `NaN`（*非數值*）值。這是因為在 `additional_items` Series 的某些索引點缺少值，並且將 `NaN` 與任何值相加都會得到 `NaN`。因此，我們需要在相加時指定 `fill_value` 參數。 @@ -94,7 +94,7 @@ total_items.plot() monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![每月時間序列平均值](../../../../translated_images/mo/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) +![每月時間序列平均值](../../../../translated_images/zh-MO/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -220,7 +220,7 @@ df = pd.read_csv('file.csv') 由於我們想展示如何處理數據，我們邀請你打開 [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) 並從頭到尾閱讀。你也可以執行單元格，並完成我們在最後留下的一些挑戰。 -![COVID 傳播](../../../../translated_images/mo/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) +![COVID 傳播](../../../../translated_images/zh-MO/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > 如果你不知道如何在 Jupyter Notebook 中運行代碼，可以查看 [這篇文章](https://soshnikov.com/education/how-to-execute-notebooks-from-github/)。 @@ -242,7 +242,7 @@ df = pd.read_csv('file.csv') 打開 [`notebook-papers.ipynb`](notebook-papers.ipynb) 並從頭到尾閱讀。你也可以執行單元格，並完成我們在最後留下的一些挑戰。 -![COVID 醫療處理](../../../../translated_images/mo/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) +![COVID 醫療處理](../../../../translated_images/zh-MO/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## 處理圖像數據 diff --git a/translations/mo/2-Working-With-Data/README.md b/translations/mo/2-Working-With-Data/README.md index 97ee9800..8f9d79d2 100644 --- a/translations/mo/2-Working-With-Data/README.md +++ b/translations/mo/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 資料處理 -![data love](../../../translated_images/mo/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) +![data love](../../../translated_images/zh-MO/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > 照片由 Alexander Sinn 提供，來自 Unsplash 在這些課程中，您將學習一些管理、操作和應用資料的方法。您將了解關聯式和非關聯式資料庫，以及資料如何存儲於其中。您還會學習使用 Python 管理資料的基礎知識，並探索使用 Python 管理和挖掘資料的多種方式。 diff --git a/translations/mo/3-Data-Visualization/12-visualization-relationships/README.md b/translations/mo/3-Data-Visualization/12-visualization-relationships/README.md index 4bebf280..c1273891 100644 --- a/translations/mo/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/mo/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/mo/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) +![scatterplot 1](../../../../translated_images/zh-MO/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) 接下來，使用蜂蜜色系展示價格如何隨年份演變。您可以通過添加 'hue' 參數來顯示年份的變化： @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/mo/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) +![scatterplot 2](../../../../translated_images/zh-MO/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) 使用這種色彩方案，您可以清楚地看到蜂蜜每磅價格在多年來的明顯增長趨勢。事實上，如果您查看數據中的樣本集（例如選擇一個州，亞利桑那州），您會發現價格每年都有增長，只有少數例外： @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` 您可以看到點的大小逐漸增大。 -![scatterplot 3](../../../../translated_images/mo/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) +![scatterplot 3](../../../../translated_images/zh-MO/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) 這是否只是供需的簡單案例？由於氣候變化和蜂群崩潰等因素，是否每年可供購買的蜂蜜減少，因此價格上漲？ @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` 答案：是的，除了2003年左右有一些例外： -![line chart 1](../../../../translated_images/mo/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) +![line chart 1](../../../../translated_images/zh-MO/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ 由於 Seaborn 將數據聚合到一條線上，它通過繪製均值和均值周圍的95%置信區間來顯示「每個 x 值的多個測量值」。[來源](https://seaborn.pydata.org/tutorial/relational.html)。這種耗時的行為可以通過添加 `ci=None` 禁用。 @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/mo/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) +![line chart 2](../../../../translated_images/zh-MO/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) 答案：並不完全。如果您查看總產量，實際上在那一年似乎有所增加，儘管總體而言，蜂蜜的生產量在這些年中呈下降趨勢。 @@ -139,7 +139,7 @@ sns.relplot( ``` 在這個視覺化中，您可以比較每年的每群產量和蜂群數量，並將列的包裹設置為3： -![facet grid](../../../../translated_images/mo/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) +![facet grid](../../../../translated_images/zh-MO/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) 對於這個數據集，關於蜂群數量和每群產量，按年份和州比較並沒有特別突出的地方。是否有其他方式來尋找這兩個變數之間的相關性？ @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/mo/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) +![superimposed plots](../../../../translated_images/zh-MO/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) 雖然在2003年沒有明顯的異常，但這讓我們以一個稍微樂觀的結論結束這節課：儘管蜂群數量總體上在下降，但蜂群數量正在穩定，即使每群產量在減少。 diff --git a/translations/mo/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/mo/3-Data-Visualization/R/09-visualization-quantities/README.md index cf65a976..39d35ecb 100644 --- a/translations/mo/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/mo/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -67,7 +67,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` 在這裡，您安裝了 `ggplot2` 套件，然後使用 `library("ggplot2")` 命令將其導入工作區。要在 ggplot 中繪製任何圖表，使用 `ggplot()` 函數並指定數據集、x 和 y 變數作為屬性。在此情況下，我們使用 `geom_line()` 函數，因為我們的目標是繪製折線圖。 -![MaxWingspan-lineplot](../../../../../translated_images/mo/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) +![MaxWingspan-lineplot](../../../../../translated_images/zh-MO/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) 您立即注意到什麼？似乎至少有一個異常值——那是一個相當大的翼展！2000+ 公分的翼展超過了 20 公尺——明尼蘇達州有翼龍在飛嗎？讓我們調查一下。 @@ -85,7 +85,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` 我們在 `theme` 中指定了角度，並在 `xlab()` 和 `ylab()` 中分別指定了 x 和 y 軸標籤。`ggtitle()` 為圖表/圖形命名。 -![MaxWingspan-lineplot-improved](../../../../../translated_images/mo/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/zh-MO/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) 即使將標籤的旋轉設置為 45 度，仍然有太多標籤難以閱讀。讓我們嘗試另一種策略：僅標記那些異常值並在圖表內設置標籤。您可以使用散點圖來為標籤留出更多空間： @@ -101,7 +101,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + 您發現了什麼？ -![MaxWingspan-scatterplot](../../../../../translated_images/mo/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) +![MaxWingspan-scatterplot](../../../../../translated_images/zh-MO/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## 篩選數據 @@ -120,7 +120,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` 我們創建了一個新的數據框 `birds_filtered`，然後繪製了一個散點圖。通過篩選掉異常值，您的數據現在更加一致且易於理解。 -![MaxWingspan-scatterplot-improved](../../../../../translated_images/mo/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/zh-MO/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) 現在我們至少在翼展方面有了一個更乾淨的數據集，讓我們進一步探索這些鳥類。 @@ -163,7 +163,7 @@ birds_filtered %>% group_by(Category) %>% ``` 在以下代碼片段中，我們安裝了 [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) 和 [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) 套件，以幫助操作和分組數據以繪製堆疊條形圖。首先，您按鳥類的 `Category` 分組數據，然後總結 `MinLength`、`MaxLength`、`MinBodyMass`、`MaxBodyMass`、`MinWingspan`、`MaxWingspan` 列。接著，使用 `ggplot2` 套件繪製條形圖並指定不同類別的顏色和標籤。 -![Stacked bar chart](../../../../../translated_images/mo/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) +![Stacked bar chart](../../../../../translated_images/zh-MO/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) 然而，這個條形圖因為有太多未分組的數據而難以閱讀。您需要選擇僅想要繪製的數據，因此讓我們看看基於鳥類類別的鳥類長度。 @@ -178,7 +178,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` 您首先計算 `Category` 列中的唯一值，然後將它們排序到新的數據框 `birds_count` 中。這些排序後的數據在相同層次中進行分級，以便按排序方式繪製。使用 `ggplot2`，您接著繪製條形圖。`coord_flip()` 則繪製水平條形圖。 -![category-length](../../../../../translated_images/mo/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) +![category-length](../../../../../translated_images/zh-MO/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) 這個條形圖很好地展示了每個類別中鳥類的數量。一眼就能看出，在這個地區最多的鳥類是鴨/鵝/水禽類別。明尼蘇達州是“萬湖之地”，所以這並不令人驚訝！ @@ -201,7 +201,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` 我們按 `Category` 分組 `birds_filtered` 數據，然後繪製條形圖。 -![comparing data](../../../../../translated_images/mo/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) +![comparing data](../../../../../translated_images/zh-MO/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) 這裡沒有什麼令人驚訝的：蜂鳥的最大長度比鵜鶘或鵝要小得多。當數據符合邏輯時，這是件好事！ @@ -213,7 +213,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/mo/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) +![super-imposed values](../../../../../translated_images/zh-MO/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 挑戰 diff --git a/translations/mo/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/mo/3-Data-Visualization/R/10-visualization-distributions/README.md index c893a479..e15675b8 100644 --- a/translations/mo/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/mo/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![每目最大長度](../../../../../translated_images/mo/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) +![每目最大長度](../../../../../translated_images/zh-MO/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) 這提供了每個鳥類目的一般身體長度分佈概況，但這並不是顯示真實分佈的最佳方式。通常使用直方圖來完成這項任務。 ## 使用直方圖 @@ -56,7 +56,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![整個數據集的分佈](../../../../../translated_images/mo/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) +![整個數據集的分佈](../../../../../translated_images/zh-MO/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) 如你所見，這個數據集中的 400 多種鳥類大多數最大體重都低於 2000。通過將 `bins` 參數更改為更高的數字，例如 30，可以獲得更多的洞察： @@ -64,7 +64,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![30個區間的分佈](../../../../../translated_images/mo/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) +![30個區間的分佈](../../../../../translated_images/zh-MO/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) 此圖表以更細緻的方式顯示了分佈。通過確保僅選擇特定範圍內的數據，可以創建一個不那麼偏向左側的圖表： @@ -76,7 +76,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![篩選後的直方圖](../../../../../translated_images/mo/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) +![篩選後的直方圖](../../../../../translated_images/zh-MO/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ 嘗試其他篩選條件和數據點。要查看數據的完整分佈，移除 `['MaxBodyMass']` 篩選器以顯示標籤分佈。 @@ -90,7 +90,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` 沿著預期的軸，這兩個元素之間似乎存在預期的相關性，其中有一個特別強的收斂點： -![2D 圖表](../../../../../translated_images/mo/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) +![2D 圖表](../../../../../translated_images/zh-MO/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) 直方圖默認適用於數值型數據。如果你需要查看基於文本數據的分佈該怎麼辦？ ## 使用文本數據探索數據集的分佈 @@ -121,7 +121,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![翼展與保育狀態的對比](../../../../../translated_images/mo/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) +![翼展與保育狀態的對比](../../../../../translated_images/zh-MO/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) 最小翼展和保育狀態之間似乎沒有良好的相關性。使用此方法測試數據集的其他元素。你可以嘗試不同的篩選條件。你是否發現了任何相關性？ @@ -135,7 +135,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![密度圖](../../../../../translated_images/mo/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) +![密度圖](../../../../../translated_images/zh-MO/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) 你可以看到該圖表反映了之前的最小翼展數據；它只是稍微平滑了一些。如果你想重新訪問第二個圖表中那條鋸齒狀的最大體重線，可以通過使用此方法非常好地將其平滑化： @@ -143,7 +143,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![體重密度](../../../../../translated_images/mo/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) +![體重密度](../../../../../translated_images/zh-MO/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) 如果你想要一條平滑但不過於平滑的線，可以編輯 `adjust` 參數： @@ -151,7 +151,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![較不平滑的體重密度](../../../../../translated_images/mo/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) +![較不平滑的體重密度](../../../../../translated_images/zh-MO/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ 閱讀此類圖表可用的參數並進行實驗！ @@ -161,7 +161,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![每目體重密度](../../../../../translated_images/mo/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) +![每目體重密度](../../../../../translated_images/zh-MO/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 挑戰 diff --git a/translations/mo/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/mo/3-Data-Visualization/R/11-visualization-proportions/README.md index c084b92a..a8607952 100644 --- a/translations/mo/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/mo/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -93,7 +93,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` 完成了，一個圓餅圖展示了根據這兩類蘑菇的數據比例。正確排列標籤的順序非常重要，尤其是在這裡，因此請務必確認標籤數組的構建順序！ -![圓餅圖](../../../../../translated_images/mo/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) +![圓餅圖](../../../../../translated_images/zh-MO/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## 甜甜圈圖！ @@ -128,7 +128,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![甜甜圈圖](../../../../../translated_images/mo/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) +![甜甜圈圖](../../../../../translated_images/zh-MO/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) 這段代碼使用了兩個庫——ggplot2 和 webr。使用 webr 庫的 PieDonut 函數，我們可以輕鬆創建甜甜圈圖！ @@ -166,7 +166,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual 使用華夫圖，你可以清楚地看到這個蘑菇數據集中帽顏色的比例。有趣的是，有許多綠色帽子的蘑菇！ -![華夫圖](../../../../../translated_images/mo/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) +![華夫圖](../../../../../translated_images/zh-MO/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) 在這節課中，你學到了三種視覺化比例的方法。首先，你需要將數據分組到分類中，然後決定哪種方式最適合展示數據——圓餅圖、甜甜圈圖或華夫圖。這些方法都很有趣，並能讓用戶快速了解數據集。 diff --git a/translations/mo/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/mo/3-Data-Visualization/R/12-visualization-relationships/README.md index db91edac..426472f3 100644 --- a/translations/mo/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/mo/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/mo/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) +![scatterplot 1](../../../../../translated_images/zh-MO/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) 現在，使用蜂蜜色彩方案展示價格如何隨年份演變。您可以通過添加 'scale_color_gradientn' 參數來展示年份的變化： @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/mo/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) +![scatterplot 2](../../../../../translated_images/zh-MO/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) 通過這種色彩方案的改變，您可以明顯看到蜂蜜每磅價格在多年來的強烈增長趨勢。事實上，如果您查看數據中的樣本集（例如選擇亞利桑那州），您可以看到價格逐年上漲的模式，僅有少數例外： @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` 您可以看到點的大小逐漸增大。 -![scatterplot 3](../../../../../translated_images/mo/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) +![scatterplot 3](../../../../../translated_images/zh-MO/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) 這是否是一個簡單的供需問題？由於氣候變化和蜂群崩潰等因素，是否每年可供購買的蜂蜜減少，導致價格上漲？ @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` 答案：是的，但在2003年左右有一些例外： -![line chart 1](../../../../../translated_images/mo/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) +![line chart 1](../../../../../translated_images/zh-MO/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) 問題：那麼在2003年，我們是否也能看到蜂蜜供應的激增？如果您查看總產量逐年變化呢？ @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/mo/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) +![line chart 2](../../../../../translated_images/zh-MO/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) 答案：並不完全。如果您查看總產量，實際上在那一年似乎有所增加，儘管總體而言蜂蜜的生產量在這些年中呈下降趨勢。 @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` 在此視覺化中，您可以比較每群產量和蜂群數量逐年變化，並將列數設置為3： -![facet grid](../../../../../translated_images/mo/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) +![facet grid](../../../../../translated_images/zh-MO/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) 對於此數據集，逐年和逐州比較蜂群數量和每群產量，並未顯示出特別突出的情況。是否有其他方式來尋找這兩個變量之間的相關性？ @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/mo/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) +![superimposed plots](../../../../../translated_images/zh-MO/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) 雖然在2003年並未有明顯的異常，但這讓我們可以以一個稍微樂觀的結論結束本課：儘管蜂群數量總體上在下降，但蜂群數量正在穩定，即使每群產量在減少。 diff --git a/translations/mo/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/mo/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 487bb79e..8ac03784 100644 --- a/translations/mo/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/mo/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: 即使數據科學家謹慎選擇了合適的圖表類型，數據仍然可能以某種方式被展示來支持某種觀點，往往以犧牲數據本身為代價。有許多誤導性圖表和信息圖的例子！ -[![Alberto Cairo 的《How Charts Lie》](../../../../../translated_images/mo/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![Alberto Cairo 的《How Charts Lie》](../../../../../translated_images/zh-MO/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 點擊上方圖片觀看有關誤導性圖表的會議演講 這張圖表反轉了 X 軸，根據日期顯示了與事實相反的內容： -![糟糕的圖表 1](../../../../../translated_images/mo/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) +![糟糕的圖表 1](../../../../../translated_images/zh-MO/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [這張圖表](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) 更具誤導性，因為人們的目光會被吸引到右側，得出隨時間推移各縣的 COVID 病例數下降的結論。事實上，如果仔細查看日期，你會發現日期被重新排列以製造出誤導性的下降趨勢。 -![糟糕的圖表 2](../../../../../translated_images/mo/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) +![糟糕的圖表 2](../../../../../translated_images/zh-MO/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) 這個臭名昭著的例子使用顏色和翻轉的 Y 軸來誤導：原本應該得出槍支友好立法通過後槍支死亡率激增的結論，事實上卻讓人誤以為情況正好相反： -![糟糕的圖表 3](../../../../../translated_images/mo/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) +![糟糕的圖表 3](../../../../../translated_images/zh-MO/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) 這張奇怪的圖表展示了比例如何被操控，效果令人捧腹： -![糟糕的圖表 4](../../../../../translated_images/mo/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) +![糟糕的圖表 4](../../../../../translated_images/zh-MO/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) 比較不可比的事物是另一種不正當的手段。有一個[精彩的網站](https://tylervigen.com/spurious-correlations)專門展示「虛假的相關性」，例如緬因州的離婚率與人造奶油的消耗量之間的「事實」相關性。一個 Reddit 群組也收集了[糟糕的數據使用](https://www.reddit.com/r/dataisugly/top/?t=all)。 @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: 如果你的數據在 X 軸上是文本且冗長，可以將文本角度調整以提高可讀性。[plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) 提供了 3D 繪圖功能，如果你的數據支持它，可以使用它製作更高級的數據視覺化。 -![3D 圖表](../../../../../translated_images/mo/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) +![3D 圖表](../../../../../translated_images/zh-MO/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## 動畫和 3D 圖表展示 如今一些最好的數據視覺化是動畫化的。Shirley Wu 使用 D3 創作了令人驚嘆的作品，例如「[電影之花](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)」，每朵花都是一部電影的視覺化。另一個例子是《衛報》的「Bussed Out」，這是一個結合 Greensock 和 D3 的視覺化和滾動敘事文章格式的互動體驗，展示了紐約市如何通過將無家可歸者送出城市來處理其無家可歸問題。 -![Bussed Out](../../../../../translated_images/mo/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) +![Bussed Out](../../../../../translated_images/zh-MO/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > 「Bussed Out: How America Moves its Homeless」來自[衛報](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study)。視覺化由 Nadieh Bremer 和 Shirley Wu 創作 @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: 你將完成一個網頁應用，展示這個社交網絡的動畫化視圖。它使用了一個庫，該庫旨在使用 Vue.js 和 D3 創建[網絡視覺化](https://github.com/emiliorizzo/vue-d3-network)。當應用運行時，你可以在屏幕上拖動節點來重新排列數據。 -![危險關係](../../../../../translated_images/mo/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) +![危險關係](../../../../../translated_images/zh-MO/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## 專案：使用 D3.js 建立一個展示網絡的圖表 diff --git a/translations/mo/3-Data-Visualization/README.md b/translations/mo/3-Data-Visualization/README.md index b7736f08..6dba1f5b 100644 --- a/translations/mo/3-Data-Visualization/README.md +++ b/translations/mo/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 視覺化 -![一隻蜜蜂停在薰衣草花上](../../../translated_images/mo/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) +![一隻蜜蜂停在薰衣草花上](../../../translated_images/zh-MO/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > 照片由 Jenna Lee 提供，來源於 Unsplash 視覺化數據是數據科學家最重要的任務之一。圖片勝過千言萬語，視覺化可以幫助你識別數據中的各種有趣部分，例如峰值、異常值、分組、趨勢等，這些都能幫助你理解數據背後的故事。 diff --git a/translations/mo/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/mo/4-Data-Science-Lifecycle/14-Introduction/README.md index 7add034d..102a17e8 100644 --- a/translations/mo/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/mo/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 本課程將重點放在生命週期的三個部分：資料捕捉、資料處理和資料維護。 -![資料科學生命週期圖示](../../../../translated_images/mo/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) +![資料科學生命週期圖示](../../../../translated_images/zh-MO/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > 圖片來源：[Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## 資料捕捉 @@ -98,7 +98,7 @@ CO_OP_TRANSLATOR_METADATA: |團隊資料科學過程 (TDSP)|跨行業標準資料挖掘過程 (CRISP-DM)| |--|--| -|![團隊資料科學生命週期](../../../../translated_images/mo/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![資料科學過程聯盟圖示](../../../../translated_images/mo/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | +|![團隊資料科學生命週期](../../../../translated_images/zh-MO/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![資料科學過程聯盟圖示](../../../../translated_images/zh-MO/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | 圖片來源：[Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | 圖片來源：[Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [課後測驗](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/mo/4-Data-Science-Lifecycle/README.md b/translations/mo/4-Data-Science-Lifecycle/README.md index fa6704ef..af5dc68d 100644 --- a/translations/mo/4-Data-Science-Lifecycle/README.md +++ b/translations/mo/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 數據科學生命週期 -![communication](../../../translated_images/mo/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) +![communication](../../../translated_images/zh-MO/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > 圖片由 Headway 提供，來自 Unsplash 在這些課程中，您將探索數據科學生命週期的一些方面，包括數據的分析和溝通。 diff --git a/translations/mo/5-Data-Science-In-Cloud/README.md b/translations/mo/5-Data-Science-In-Cloud/README.md index a94c7faf..97f2d2fc 100644 --- a/translations/mo/5-Data-Science-In-Cloud/README.md +++ b/translations/mo/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # 雲端中的數據科學 -![cloud-picture](../../../translated_images/mo/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) +![cloud-picture](../../../translated_images/zh-MO/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > 圖片來源：[Jelleke Vanooteghem](https://unsplash.com/@ilumire) 來自 [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) 當涉及到使用大數據進行數據科學時，雲端可以成為改變遊戲規則的關鍵。在接下來的三節課中，我們將了解什麼是雲端以及為什麼它非常有用。我們還將探索一個心臟衰竭數據集，並建立一個模型來幫助評估某人發生心臟衰竭的可能性。我們將利用雲端的強大功能來訓練、部署和以兩種不同的方式使用模型。一種方式是僅使用用戶界面，以低代碼/無代碼的方式進行；另一種方式是使用 Azure Machine Learning Software Developer Kit (Azure ML SDK)。 -![project-schema](../../../translated_images/mo/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) +![project-schema](../../../translated_images/zh-MO/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### 主題 diff --git a/translations/mo/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/mo/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 3cc4a835..71364e53 100644 --- a/translations/mo/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/mo/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: * [醫療保健中的數據科學](https://data-flair.training/blogs/data-science-in-healthcare/) - 強調應用如醫學影像（例如 MRI、X光、CT掃描）、基因組學（DNA測序）、藥物開發（風險評估、成功預測）、預測分析（患者護理和供應物流）、疾病追蹤和預防等。 -![數據科學在現實世界中的應用](../../../../translated_images/mo/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) 圖片來源：[Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![數據科學在現實世界中的應用](../../../../translated_images/zh-MO/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) 圖片來源：[Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) 該圖展示了其他領域和應用數據科學技術的例子。想探索更多應用？請查看下面的[回顧與自學](../../../../6-Data-Science-In-Wild/20-Real-World-Examples)部分。 diff --git a/translations/mo/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/mo/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 88522f95..ffae60f0 100644 --- a/translations/mo/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/mo/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Explorer界面（如下圖所示）允許您選擇一個數據集（從提供的 2. 探索數據集[目錄](https://planetarycomputer.microsoft.com/catalog)——了解每個數據集的用途。 3. 使用Explorer——選擇一個您感興趣的數據集，選擇相關的查詢和渲染選項。 -![行星電腦Explorer](../../../../translated_images/mo/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) +![行星電腦Explorer](../../../../translated_images/zh-MO/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `您的任務：` 現在，研究瀏覽器中渲染的可視化，並回答以下問題： diff --git a/translations/mo/CONTRIBUTING.md b/translations/mo/CONTRIBUTING.md index 3ec0e899..ac5c0ea2 100644 --- a/translations/mo/CONTRIBUTING.md +++ b/translations/mo/CONTRIBUTING.md @@ -311,7 +311,7 @@ def calculate_mean(data): import pandas as pd ``` ```` -- 為圖片添加替代文字：`![Alt text](../../translated_images/mo/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` +- 為圖片添加替代文字：`![Alt text](../../translated_images/zh-MO/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - 保持合理的行長度（約 80-100 字元） ### Python diff --git a/translations/mo/README.md b/translations/mo/README.md index bf074d88..1dc4d549 100644 --- a/translations/mo/README.md +++ b/translations/mo/README.md @@ -33,7 +33,7 @@ CO_OP_TRANSLATOR_METADATA: **🙏 特別鳴謝 🙏 我們的 [Microsoft 學生大使](https://studentambassadors.microsoft.com/) 作者、審稿人及內容貢獻者，** 其中包括 Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/mo/00-Title.8af36cd35da1ac55.webp)| +|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/zh-MO/00-Title.8af36cd35da1ac55.webp)| |:---:| | 初學者數據科學 - _手繪筆記由 [@nitya](https://twitter.com/nitya) 製作_ | @@ -62,7 +62,7 @@ CO_OP_TRANSLATOR_METADATA: 我們正在進行 Discord AI 系列學習活動，詳情與加入請訪問 [Learn with AI Series](https://aka.ms/learnwithai/discord)，活動期間為 2025 年 9 月 18日至 30日。你將學習使用 GitHub Copilot 進行數據科學的技巧與秘訣。 -![Learn with AI series](../../../../translated_images/mo/1.2b28cdc6205e26fe.webp) +![Learn with AI series](../../../../translated_images/zh-MO/1.2b28cdc6205e26fe.webp) # 你是學生嗎？ @@ -142,7 +142,7 @@ CO_OP_TRANSLATOR_METADATA: ## 課程列表 -|![速寫筆記由 @sketchthedocs https://sketchthedocs.dev 提供](../../../../translated_images/mo/00-Roadmap.4905d6567dff4753.webp)| +|![速寫筆記由 @sketchthedocs https://sketchthedocs.dev 提供](../../../../translated_images/zh-MO/00-Roadmap.4905d6567dff4753.webp)| |:---:| | 資料科學初學者路線圖 - _速寫筆記由 [@nitya](https://twitter.com/nitya) 提供_ | diff --git a/translations/mo/sketchnotes/README.md b/translations/mo/sketchnotes/README.md index 8334424b..648347a9 100644 --- a/translations/mo/sketchnotes/README.md +++ b/translations/mo/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: Nitya Narasimhan，藝術家 -![roadmap sketchnote](../../../translated_images/mo/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) +![roadmap sketchnote](../../../translated_images/zh-MO/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/pt/1-Introduction/01-defining-data-science/README.md b/translations/pt/1-Introduction/01-defining-data-science/README.md index abec1d7e..b09bc1a7 100644 --- a/translations/pt/1-Introduction/01-defining-data-science/README.md +++ b/translations/pt/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Vídeo Definindo Ciência de Dados](../../../../translated_images/pt/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) +[![Vídeo Definindo Ciência de Dados](../../../../translated_images/pt-PT/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Questionário pré-aula](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ Se quisermos ser ainda mais detalhados, podemos traçar o tempo gasto em cada m Neste desafio, vamos tentar encontrar conceitos relevantes para o campo da Ciência de Dados analisando textos. Vamos pegar um artigo da Wikipédia sobre Ciência de Dados, descarregar e processar o texto e, em seguida, criar uma nuvem de palavras como esta: -![Nuvem de Palavras para Ciência de Dados](../../../../translated_images/pt/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) +![Nuvem de Palavras para Ciência de Dados](../../../../translated_images/pt-PT/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) Visite [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') para ler o código. Também pode executar o código e ver como ele realiza todas as transformações de dados em tempo real. diff --git a/translations/pt/1-Introduction/04-stats-and-probability/README.md b/translations/pt/1-Introduction/04-stats-and-probability/README.md index 04c312ca..cb9bace4 100644 --- a/translations/pt/1-Introduction/04-stats-and-probability/README.md +++ b/translations/pt/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: A Teoria da Estatística e Probabilidade são duas áreas altamente relacionadas da Matemática que têm grande relevância para a Ciência de Dados. É possível trabalhar com dados sem um conhecimento profundo de matemática, mas é sempre melhor conhecer pelo menos alguns conceitos básicos. Aqui apresentaremos uma breve introdução que o ajudará a começar. -[![Vídeo de Introdução](../../../../translated_images/pt/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Vídeo de Introdução](../../../../translated_images/pt-PT/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Questionário pré-aula](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ A distribuição discreta mais conhecida é a **distribuição uniforme**, na qu Só podemos falar sobre a probabilidade de uma variável cair em um determinado intervalo de valores, por exemplo, P(t1≤X2). Nesse caso, a distribuição de probabilidade é descrita por uma **função densidade de probabilidade** p(x), tal que -![P(t_1\le X Mais exemplos de correlação e covariância podem ser encontrados no [notebook associado](notebook.ipynb). diff --git a/translations/pt/1-Introduction/README.md b/translations/pt/1-Introduction/README.md index e909d25e..39492431 100644 --- a/translations/pt/1-Introduction/README.md +++ b/translations/pt/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introdução à Ciência de Dados -![dados em ação](../../../translated_images/pt/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) +![dados em ação](../../../translated_images/pt-PT/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > Foto de Stephen Dawson no Unsplash Nestes módulos, irá descobrir como a Ciência de Dados é definida e aprender sobre as considerações éticas que devem ser tidas em conta por um cientista de dados. Também irá aprender como os dados são definidos e explorar um pouco de estatística e probabilidade, os domínios académicos centrais da Ciência de Dados. diff --git a/translations/pt/2-Working-With-Data/07-python/README.md b/translations/pt/2-Working-With-Data/07-python/README.md index 429c880a..0dc63ab1 100644 --- a/translations/pt/2-Working-With-Data/07-python/README.md +++ b/translations/pt/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Trabalhar com Python - _Sketchnote por [@nitya](https://twitter.com/nitya)_ | -[![Vídeo de Introdução](../../../../translated_images/pt/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) +[![Vídeo de Introdução](../../../../translated_images/pt-PT/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) Embora bases de dados ofereçam formas muito eficientes de armazenar e consultar dados usando linguagens de consulta, a maneira mais flexível de processar dados é escrever o seu próprio programa para manipulá-los. Em muitos casos, realizar uma consulta em uma base de dados seria mais eficaz. No entanto, em alguns casos, quando é necessário um processamento de dados mais complexo, isso não pode ser feito facilmente usando SQL. O processamento de dados pode ser programado em qualquer linguagem de programação, mas há certas linguagens que são mais adequadas para trabalhar com dados. Cientistas de dados geralmente preferem uma das seguintes linguagens: @@ -73,7 +73,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Gráfico de Série Temporal](../../../../translated_images/pt/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) +![Gráfico de Série Temporal](../../../../translated_images/pt-PT/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) Agora suponha que, a cada semana, organizamos uma festa para amigos e levamos 10 pacotes adicionais de sorvete para a festa. Podemos criar outra série, indexada por semana, para demonstrar isso: ```python @@ -84,7 +84,7 @@ Quando somamos duas séries, obtemos o número total: total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Gráfico de Série Temporal](../../../../translated_images/pt/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) +![Gráfico de Série Temporal](../../../../translated_images/pt-PT/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Nota** que não estamos usando a sintaxe simples `total_items+additional_items`. Se o fizéssemos, receberíamos muitos valores `NaN` (*Not a Number*) na série resultante. Isso ocorre porque há valores ausentes para alguns dos pontos de índice na série `additional_items`, e somar `NaN` a qualquer coisa resulta em `NaN`. Assim, precisamos especificar o parâmetro `fill_value` durante a soma. @@ -93,7 +93,7 @@ Com séries temporais, também podemos **reamostrar** a série com diferentes in monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Médias Mensais de Série Temporal](../../../../translated_images/pt/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) +![Médias Mensais de Série Temporal](../../../../translated_images/pt-PT/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -219,7 +219,7 @@ O primeiro problema em que nos vamos focar é o modelo de propagação epidémic Como queremos demonstrar como lidar com dados, convidamo-lo a abrir [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) e lê-lo de cima para baixo. Pode também executar as células e realizar alguns desafios que deixámos para si no final. -![Propagação da COVID](../../../../translated_images/pt/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) +![Propagação da COVID](../../../../translated_images/pt-PT/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Se não sabe como executar código no Jupyter Notebook, veja [este artigo](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). @@ -241,7 +241,7 @@ Um exemplo completo de análise deste conjunto de dados usando o serviço cognit Abra [`notebook-papers.ipynb`](notebook-papers.ipynb) e leia-o de cima para baixo. Pode também executar as células e realizar alguns desafios que deixámos para si no final. -![Tratamento Médico COVID](../../../../translated_images/pt/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) +![Tratamento Médico COVID](../../../../translated_images/pt-PT/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Processamento de Dados de Imagem diff --git a/translations/pt/2-Working-With-Data/README.md b/translations/pt/2-Working-With-Data/README.md index c7f7314a..11537f8d 100644 --- a/translations/pt/2-Working-With-Data/README.md +++ b/translations/pt/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Trabalhar com Dados -![amor pelos dados](../../../translated_images/pt/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) +![amor pelos dados](../../../translated_images/pt-PT/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > Foto por Alexander Sinn no Unsplash Nestes módulos, vais aprender algumas formas de gerir, manipular e utilizar dados em aplicações. Vais aprender sobre bases de dados relacionais e não relacionais e como os dados podem ser armazenados nelas. Vais aprender os fundamentos de trabalhar com Python para gerir dados e descobrir algumas das muitas maneiras de usar Python para gerir e explorar dados. diff --git a/translations/pt/3-Data-Visualization/12-visualization-relationships/README.md b/translations/pt/3-Data-Visualization/12-visualization-relationships/README.md index 36efdee2..302d2592 100644 --- a/translations/pt/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/pt/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ Crie um gráfico de dispersão básico para mostrar a relação entre o preço p ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![gráfico de dispersão 1](../../../../translated_images/pt/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) +![gráfico de dispersão 1](../../../../translated_images/pt-PT/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) Agora, mostre os mesmos dados com um esquema de cores de mel para ilustrar como o preço evolui ao longo dos anos. Pode-se fazer isso adicionando um parâmetro 'hue' para mostrar a mudança, ano após ano: @@ -60,7 +60,7 @@ Agora, mostre os mesmos dados com um esquema de cores de mel para ilustrar como ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![gráfico de dispersão 2](../../../../translated_images/pt/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) +![gráfico de dispersão 2](../../../../translated_images/pt-PT/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) Com esta mudança no esquema de cores, é possível perceber claramente uma forte progressão ao longo dos anos no preço do mel por libra. De fato, ao verificar um conjunto de amostras nos dados (escolha um estado, como o Arizona, por exemplo), é possível observar um padrão de aumento de preços ano após ano, com poucas exceções: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` Pode-se observar que o tamanho dos pontos aumenta gradualmente. -![gráfico de dispersão 3](../../../../translated_images/pt/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) +![gráfico de dispersão 3](../../../../translated_images/pt-PT/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) Será este um caso simples de oferta e procura? Devido a fatores como mudanças climáticas e o colapso das colónias, haverá menos mel disponível para compra ano após ano, e, assim, o preço aumenta? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` Resposta: Sim, com algumas exceções por volta do ano 2003: -![gráfico de linhas 1](../../../../translated_images/pt/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) +![gráfico de linhas 1](../../../../translated_images/pt-PT/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Como o Seaborn está a agregar dados numa única linha, ele exibe "as múltiplas medições em cada valor de x, traçando a média e o intervalo de confiança de 95% em torno da média". [Fonte](https://seaborn.pydata.org/tutorial/relational.html). Este comportamento, que consome tempo, pode ser desativado adicionando `ci=None`. @@ -114,7 +114,7 @@ Pergunta: Bem, em 2003 também podemos observar um pico na oferta de mel? E se a sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![gráfico de linhas 2](../../../../translated_images/pt/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) +![gráfico de linhas 2](../../../../translated_images/pt-PT/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) Resposta: Não exatamente. Ao observar a produção total, parece que ela realmente aumentou naquele ano específico, embora, de forma geral, a quantidade de mel produzido esteja em declínio durante esses anos. @@ -139,7 +139,7 @@ sns.relplot( ``` Nesta visualização, pode-se comparar a produção por colmeia e o número de colmeias ano após ano, lado a lado, com um limite de 3 colunas: -![grelha de facetas](../../../../translated_images/pt/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) +![grelha de facetas](../../../../translated_images/pt-PT/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) Para este conjunto de dados, nada particularmente se destaca em relação ao número de colmeias e sua produção, ano após ano e estado por estado. Existe uma forma diferente de encontrar uma correlação entre estas duas variáveis? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![gráficos sobrepostos](../../../../translated_images/pt/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) +![gráficos sobrepostos](../../../../translated_images/pt-PT/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) Embora nada salte aos olhos em relação ao ano de 2003, isso permite encerrar esta lição com uma nota um pouco mais feliz: embora o número de colmeias esteja em declínio geral, ele está a estabilizar, mesmo que a produção por colmeia esteja a diminuir. diff --git a/translations/pt/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/pt/3-Data-Visualization/R/09-visualization-quantities/README.md index 6a060e6b..24027c81 100644 --- a/translations/pt/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/pt/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Aqui, instalas o pacote `ggplot2` e depois importas para o ambiente de trabalho usando o comando `library("ggplot2")`. Para criar qualquer gráfico no ggplot, usa-se a função `ggplot()` e especifica-se o conjunto de dados, as variáveis x e y como atributos. Neste caso, usamos a função `geom_line()` porque queremos criar um gráfico de linhas. -![MaxWingspan-lineplot](../../../../../translated_images/pt/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) +![MaxWingspan-lineplot](../../../../../translated_images/pt-PT/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) O que notas imediatamente? Parece haver pelo menos um valor atípico - que envergadura impressionante! Uma envergadura de mais de 2000 centímetros equivale a mais de 20 metros - será que há Pterodáctilos a voar em Minnesota? Vamos investigar. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Especificamos o ângulo no `theme` e definimos os rótulos dos eixos x e y em `xlab()` e `ylab()` respetivamente. O `ggtitle()` dá um nome ao gráfico. -![MaxWingspan-lineplot-improved](../../../../../translated_images/pt/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/pt-PT/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) Mesmo com a rotação dos rótulos definida para 45 graus, há demasiados para ler. Vamos tentar uma estratégia diferente: rotular apenas os valores atípicos e definir os rótulos dentro do gráfico. Podes usar um gráfico de dispersão para criar mais espaço para os rótulos: @@ -100,7 +100,7 @@ O que está a acontecer aqui? Usaste a função `geom_point()` para criar pontos O que descobres? -![MaxWingspan-scatterplot](../../../../../translated_images/pt/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) +![MaxWingspan-scatterplot](../../../../../translated_images/pt-PT/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## Filtrar os teus dados @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` Criámos um novo dataframe `birds_filtered` e depois representámos um gráfico de dispersão. Ao filtrar os valores atípicos, os teus dados tornam-se mais coesos e compreensíveis. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/pt/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/pt-PT/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) Agora que temos um conjunto de dados mais limpo, pelo menos em termos de envergadura, vamos descobrir mais sobre estas aves. @@ -161,7 +161,7 @@ birds_filtered %>% group_by(Category) %>% ``` No seguinte trecho, instalamos os pacotes [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) e [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) para ajudar a manipular e agrupar dados para criar um gráfico de barras empilhado. Primeiro, agrupas os dados pela `Categoria` das aves e depois resumes as colunas `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan`. Em seguida, crias o gráfico de barras usando o pacote `ggplot2` e especificas as cores para as diferentes categorias e os rótulos. -![Stacked bar chart](../../../../../translated_images/pt/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) +![Stacked bar chart](../../../../../translated_images/pt-PT/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) Este gráfico de barras, no entanto, é ilegível porque há demasiados dados não agrupados. Precisamos de selecionar apenas os dados que queremos representar, então vamos observar o comprimento das aves com base na sua categoria. @@ -176,7 +176,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` Primeiro, contas os valores únicos na coluna `Categoria` e depois ordenas num novo dataframe `birds_count`. Estes dados ordenados são então considerados no mesmo nível para que sejam representados de forma ordenada. Usando o `ggplot2`, crias o gráfico de barras. O `coord_flip()` cria barras horizontais. -![category-length](../../../../../translated_images/pt/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) +![category-length](../../../../../translated_images/pt-PT/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) Este gráfico de barras mostra uma boa visão do número de aves em cada categoria. Num piscar de olhos, vês que o maior número de aves nesta região pertence à categoria de Patos/Gansos/AvesAquáticas. Minnesota é a 'terra dos 10.000 lagos', então isto não é surpreendente! @@ -199,7 +199,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` Agrupamos os dados `birds_filtered` pela `Categoria` e depois criamos um gráfico de barras. -![comparing data](../../../../../translated_images/pt/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) +![comparing data](../../../../../translated_images/pt-PT/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) Nada é surpreendente aqui: os beija-flores têm o menor MaxLength em comparação com os Pelicanos ou Gansos. É bom quando os dados fazem sentido lógico! @@ -211,7 +211,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/pt/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) +![super-imposed values](../../../../../translated_images/pt-PT/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 Desafio diff --git a/translations/pt/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/pt/3-Data-Visualization/R/10-visualization-distributions/README.md index 324a33de..5551eb9c 100644 --- a/translations/pt/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/pt/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![comprimento máximo por ordem](../../../../../translated_images/pt/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) +![comprimento máximo por ordem](../../../../../translated_images/pt-PT/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) Isto dá uma visão geral da distribuição do comprimento corporal por Ordem de aves, mas não é a forma ideal de exibir distribuições reais. Essa tarefa é geralmente realizada criando um Histograma. @@ -57,7 +57,7 @@ O `ggplot2` oferece ótimas formas de visualizar a distribuição de dados usand ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![distribuição em todo o conjunto de dados](../../../../../translated_images/pt/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) +![distribuição em todo o conjunto de dados](../../../../../translated_images/pt-PT/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) Como podes ver, a maioria das mais de 400 aves neste conjunto de dados tem uma Massa Corporal Máxima inferior a 2000. Obtém mais informações sobre os dados alterando o parâmetro `bins` para um número maior, como 30: @@ -65,7 +65,7 @@ Como podes ver, a maioria das mais de 400 aves neste conjunto de dados tem uma M ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![distribuição-30bins](../../../../../translated_images/pt/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) +![distribuição-30bins](../../../../../translated_images/pt-PT/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) Este gráfico mostra a distribuição de forma um pouco mais detalhada. Um gráfico menos enviesado para a esquerda pode ser criado garantindo que apenas selecionas dados dentro de um determinado intervalo: @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![histograma filtrado](../../../../../translated_images/pt/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) +![histograma filtrado](../../../../../translated_images/pt-PT/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ Experimenta outros filtros e pontos de dados. Para ver a distribuição completa dos dados, remove o filtro `['MaxBodyMass']` para mostrar distribuições rotuladas. @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` Parece haver uma correlação esperada entre estes dois elementos ao longo de um eixo esperado, com um ponto de convergência particularmente forte: -![gráfico 2d](../../../../../translated_images/pt/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) +![gráfico 2d](../../../../../translated_images/pt-PT/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Os histogramas funcionam bem por padrão para dados numéricos. E se precisares de ver distribuições de acordo com dados textuais? @@ -123,7 +123,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![envergadura e conservação](../../../../../translated_images/pt/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) +![envergadura e conservação](../../../../../translated_images/pt-PT/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) Não parece haver uma boa correlação entre a envergadura mínima e o estado de conservação. Testa outros elementos do conjunto de dados usando este método. Podes experimentar diferentes filtros também. Encontras alguma correlação? @@ -137,7 +137,7 @@ Vamos trabalhar agora com gráficos de densidade! ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![gráfico de densidade](../../../../../translated_images/pt/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) +![gráfico de densidade](../../../../../translated_images/pt-PT/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) Podes ver como o gráfico reflete o anterior para os dados de Envergadura Mínima; é apenas um pouco mais suave. Se quisesses revisitar aquela linha irregular de MaxBodyMass no segundo gráfico que construíste, poderias suavizá-la muito bem recriando-a usando este método: @@ -145,7 +145,7 @@ Podes ver como o gráfico reflete o anterior para os dados de Envergadura Mínim ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![densidade de massa corporal](../../../../../translated_images/pt/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) +![densidade de massa corporal](../../../../../translated_images/pt-PT/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) Se quiseres uma linha suave, mas não demasiado suave, edita o parâmetro `adjust`: @@ -153,7 +153,7 @@ Se quiseres uma linha suave, mas não demasiado suave, edita o parâmetro `adjus ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![massa corporal menos suave](../../../../../translated_images/pt/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) +![massa corporal menos suave](../../../../../translated_images/pt-PT/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ Lê sobre os parâmetros disponíveis para este tipo de gráfico e experimenta! @@ -163,7 +163,7 @@ Este tipo de gráfico oferece visualizações explicativas muito bonitas. Com al ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![massa corporal por ordem](../../../../../translated_images/pt/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) +![massa corporal por ordem](../../../../../translated_images/pt-PT/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 Desafio diff --git a/translations/pt/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/pt/3-Data-Visualization/R/11-visualization-proportions/README.md index bd0d65ec..bd49535a 100644 --- a/translations/pt/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/pt/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -92,7 +92,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` Voilà, um gráfico de pizza que mostra as proporções destes dados de acordo com estas duas classes de cogumelos. É muito importante garantir que a ordem das etiquetas esteja correta, especialmente aqui, por isso verifica sempre a ordem com que o array de etiquetas é construído! -![gráfico de pizza](../../../../../translated_images/pt/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) +![gráfico de pizza](../../../../../translated_images/pt-PT/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## Roscas! @@ -126,7 +126,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![gráfico de rosca](../../../../../translated_images/pt/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) +![gráfico de rosca](../../../../../translated_images/pt-PT/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) Este código utiliza duas bibliotecas - ggplot2 e webr. Usando a função PieDonut da biblioteca webr, podemos criar um gráfico de rosca facilmente! @@ -164,7 +164,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual Usando um gráfico de waffle, podes ver claramente as proporções das cores dos chapéus neste conjunto de dados de cogumelos. Curiosamente, existem muitos cogumelos com chapéus verdes! -![gráfico de waffle](../../../../../translated_images/pt/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) +![gráfico de waffle](../../../../../translated_images/pt-PT/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) Nesta lição, aprendeste três formas de visualizar proporções. Primeiro, precisas de agrupar os teus dados em categorias e depois decidir qual é a melhor forma de exibir os dados - pizza, rosca ou waffle. Todas são deliciosas e oferecem ao utilizador uma visão instantânea de um conjunto de dados. diff --git a/translations/pt/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/pt/3-Data-Visualization/R/12-visualization-relationships/README.md index 5d1daad1..b78ca104 100644 --- a/translations/pt/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/pt/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/pt/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) +![scatterplot 1](../../../../../translated_images/pt-PT/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) Agora, mostre os mesmos dados com um esquema de cores de mel para ilustrar como o preço evolui ao longo dos anos. Pode fazer isso adicionando o parâmetro 'scale_color_gradientn' para mostrar a mudança, ano após ano: @@ -61,7 +61,7 @@ Agora, mostre os mesmos dados com um esquema de cores de mel para ilustrar como ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/pt/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) +![scatterplot 2](../../../../../translated_images/pt-PT/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) Com esta mudança de esquema de cores, é possível ver claramente uma forte progressão ao longo dos anos no preço do mel por libra. De facto, ao verificar um conjunto de amostra nos dados (escolha um estado, como o Arizona), pode-se observar um padrão de aumento de preço ano após ano, com poucas exceções: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` Pode ver o tamanho dos pontos aumentando gradualmente. -![scatterplot 3](../../../../../translated_images/pt/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) +![scatterplot 3](../../../../../translated_images/pt-PT/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) Será este um caso simples de oferta e procura? Devido a fatores como mudanças climáticas e colapso de colónias, há menos mel disponível para compra ano após ano, e assim o preço aumenta? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` Resposta: Sim, com algumas exceções por volta do ano de 2003: -![line chart 1](../../../../../translated_images/pt/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) +![line chart 1](../../../../../translated_images/pt-PT/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) Pergunta: Bem, em 2003 também podemos ver um pico na oferta de mel? E se observarmos a produção total ano após ano? @@ -115,7 +115,7 @@ Pergunta: Bem, em 2003 também podemos ver um pico na oferta de mel? E se observ qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/pt/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) +![line chart 2](../../../../../translated_images/pt-PT/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) Resposta: Não exatamente. Se observar a produção total, parece que ela realmente aumentou nesse ano específico, embora, de forma geral, a quantidade de mel produzida esteja em declínio durante esses anos. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` Nesta visualização, pode comparar o rendimento por colónia e o número de colónias ano após ano, lado a lado, com uma disposição de 3 colunas: -![facet grid](../../../../../translated_images/pt/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) +![facet grid](../../../../../translated_images/pt-PT/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) Para este conjunto de dados, nada particularmente se destaca em relação ao número de colónias e ao seu rendimento, ano após ano e estado por estado. Existe uma forma diferente de encontrar uma correlação entre estas duas variáveis? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/pt/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) +![superimposed plots](../../../../../translated_images/pt-PT/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) Embora nada salte aos olhos em torno do ano de 2003, isso permite terminar esta lição com uma nota um pouco mais feliz: embora o número de colónias esteja em declínio geral, ele está a estabilizar, mesmo que o rendimento por colónia esteja a diminuir. diff --git a/translations/pt/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/pt/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index a4c855dd..6437239d 100644 --- a/translations/pt/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/pt/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ Em lições anteriores, você experimentou criar diversos tipos de visualizaçõ Mesmo que um cientista de dados seja cuidadoso ao escolher o gráfico certo para os dados certos, existem muitas maneiras de exibir dados de forma a provar um ponto, muitas vezes às custas de comprometer os próprios dados. Há muitos exemplos de gráficos e infográficos enganosos! -[![Como os Gráficos Enganam por Alberto Cairo](../../../../../translated_images/pt/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Como os gráficos enganam") +[![Como os Gráficos Enganam por Alberto Cairo](../../../../../translated_images/pt-PT/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Como os gráficos enganam") > 🎥 Clique na imagem acima para assistir a uma palestra sobre gráficos enganosos Este gráfico inverte o eixo X para mostrar o oposto da verdade, com base na data: -![gráfico ruim 1](../../../../../translated_images/pt/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) +![gráfico ruim 1](../../../../../translated_images/pt-PT/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [Este gráfico](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) é ainda mais enganoso, pois o olhar é atraído para a direita, levando à conclusão de que, ao longo do tempo, os casos de COVID diminuíram nos vários condados. Na verdade, ao observar atentamente as datas, percebe-se que elas foram reorganizadas para criar essa tendência descendente enganosa. -![gráfico ruim 2](../../../../../translated_images/pt/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) +![gráfico ruim 2](../../../../../translated_images/pt-PT/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) Este exemplo notório usa cor E um eixo Y invertido para enganar: em vez de concluir que as mortes por armas aumentaram após a aprovação de uma legislação favorável às armas, o olhar é enganado para pensar que o oposto é verdadeiro: -![gráfico ruim 3](../../../../../translated_images/pt/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) +![gráfico ruim 3](../../../../../translated_images/pt-PT/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) Este gráfico estranho mostra como a proporção pode ser manipulada, com efeito hilário: -![gráfico ruim 4](../../../../../translated_images/pt/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) +![gráfico ruim 4](../../../../../translated_images/pt-PT/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) Comparar o incomparável é mais um truque duvidoso. Existe um [site maravilhoso](https://tylervigen.com/spurious-correlations) dedicado a 'correlações espúrias', exibindo 'fatos' que correlacionam coisas como a taxa de divórcio no Maine e o consumo de margarina. Um grupo no Reddit também coleta os [usos feios](https://www.reddit.com/r/dataisugly/top/?t=all) de dados. @@ -100,13 +100,13 @@ Rotule os seus eixos, forneça uma legenda, se necessário, e ofereça tooltips Se os seus dados forem textuais e extensos no eixo X, você pode inclinar o texto para melhorar a legibilidade. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) oferece gráficos em 3D, se os seus dados suportarem. Visualizações de dados sofisticadas podem ser produzidas usando esta biblioteca. -![gráficos 3D](../../../../../translated_images/pt/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) +![gráficos 3D](../../../../../translated_images/pt-PT/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## Exibição de gráficos animados e em 3D Algumas das melhores visualizações de dados hoje em dia são animadas. Shirley Wu tem exemplos incríveis feitos com D3, como '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', onde cada flor é uma visualização de um filme. Outro exemplo para o Guardian é 'bussed out', uma experiência interativa que combina visualizações com Greensock e D3, além de um formato de artigo com narrativa para mostrar como NYC lida com o problema dos sem-teto, enviando pessoas para fora da cidade. -![busing](../../../../../translated_images/pt/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) +![busing](../../../../../translated_images/pt-PT/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: Como a América Move os Sem-Teto" do [Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Visualizações por Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ Embora esta lição não seja suficiente para ensinar em profundidade essas pode Você completará um aplicativo web que exibirá uma visão animada dessa rede social. Ele utiliza uma biblioteca criada para gerar uma [visualização de uma rede](https://github.com/emiliorizzo/vue-d3-network) usando Vue.js e D3. Quando o aplicativo estiver em execução, você pode mover os nós na tela para reorganizar os dados. -![liaisons](../../../../../translated_images/pt/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) +![liaisons](../../../../../translated_images/pt-PT/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Projeto: Crie um gráfico para mostrar uma rede usando D3.js diff --git a/translations/pt/3-Data-Visualization/README.md b/translations/pt/3-Data-Visualization/README.md index a4813d4f..070acd65 100644 --- a/translations/pt/3-Data-Visualization/README.md +++ b/translations/pt/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Visualizações -![uma abelha numa flor de lavanda](../../../translated_images/pt/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) +![uma abelha numa flor de lavanda](../../../translated_images/pt-PT/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > Foto de Jenna Lee no Unsplash Visualizar dados é uma das tarefas mais importantes de um cientista de dados. Imagens valem mais do que mil palavras, e uma visualização pode ajudá-lo a identificar vários aspetos interessantes dos seus dados, como picos, valores atípicos, agrupamentos, tendências e muito mais, que podem ajudá-lo a compreender a história que os seus dados estão a tentar contar. diff --git a/translations/pt/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/pt/4-Data-Science-Lifecycle/14-Introduction/README.md index bbb6763d..37d0ed1d 100644 --- a/translations/pt/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/pt/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ Neste ponto, provavelmente já percebeu que a ciência de dados é um processo. Esta lição foca-se em 3 partes do ciclo de vida: captura, processamento e manutenção. -![Diagrama do ciclo de vida da ciência de dados](../../../../translated_images/pt/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) +![Diagrama do ciclo de vida da ciência de dados](../../../../translated_images/pt-PT/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > Foto por [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## Captura @@ -101,7 +101,7 @@ Explore o [Ciclo de Vida do Processo de Ciência de Dados em Equipa](https://doc |Processo de Ciência de Dados em Equipa (TDSP)|Processo padrão da indústria para mineração de dados (CRISP-DM)| |--|--| -|![Ciclo de Vida do Processo de Ciência de Dados em Equipa](../../../../translated_images/pt/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Imagem do Processo de Ciência de Dados](../../../../translated_images/pt/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | +|![Ciclo de Vida do Processo de Ciência de Dados em Equipa](../../../../translated_images/pt-PT/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Imagem do Processo de Ciência de Dados](../../../../translated_images/pt-PT/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | Imagem por [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Imagem por [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [Questionário Pós-Aula](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/pt/4-Data-Science-Lifecycle/README.md b/translations/pt/4-Data-Science-Lifecycle/README.md index e1317a8e..34326396 100644 --- a/translations/pt/4-Data-Science-Lifecycle/README.md +++ b/translations/pt/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # O Ciclo de Vida da Ciência de Dados -![comunicação](../../../translated_images/pt/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) +![comunicação](../../../translated_images/pt-PT/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > Foto por Headway no Unsplash Nestes conteúdos, vais explorar alguns dos aspetos do ciclo de vida da Ciência de Dados, incluindo análise e comunicação de dados. diff --git a/translations/pt/5-Data-Science-In-Cloud/README.md b/translations/pt/5-Data-Science-In-Cloud/README.md index 50002682..4de545e4 100644 --- a/translations/pt/5-Data-Science-In-Cloud/README.md +++ b/translations/pt/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Ciência de Dados na Cloud -![cloud-picture](../../../translated_images/pt/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) +![cloud-picture](../../../translated_images/pt-PT/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > Foto de [Jelleke Vanooteghem](https://unsplash.com/@ilumire) no [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) Quando se trata de fazer ciência de dados com big data, a cloud pode ser um divisor de águas. Nas próximas três lições, vamos explorar o que é a cloud e por que ela pode ser tão útil. Também vamos analisar um conjunto de dados sobre insuficiência cardíaca e construir um modelo para ajudar a avaliar a probabilidade de alguém sofrer de insuficiência cardíaca. Utilizaremos o poder da cloud para treinar, implementar e consumir um modelo de duas formas diferentes. Uma forma será utilizando apenas a interface de utilizador, num estilo de "Low code/No code", e a outra será através do Azure Machine Learning Software Developer Kit (Azure ML SDK). -![project-schema](../../../translated_images/pt/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) +![project-schema](../../../translated_images/pt-PT/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### Tópicos diff --git a/translations/pt/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/pt/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index ca4bbc1b..6a98a664 100644 --- a/translations/pt/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/pt/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ Graças à democratização da IA, os desenvolvedores estão a encontrar formas * [Ciência de Dados na Saúde](https://data-flair.training/blogs/data-science-in-healthcare/) - destaca aplicações como imagiologia médica (e.g., ressonância magnética, raio-X, tomografia), genómica (sequenciamento de DNA), desenvolvimento de medicamentos (avaliação de risco, previsão de sucesso), análise preditiva (cuidados ao paciente e logística de fornecimento), rastreamento e prevenção de doenças, etc. -![Aplicações de Ciência de Dados no Mundo Real](../../../../translated_images/pt/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Crédito da Imagem: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Aplicações de Ciência de Dados no Mundo Real](../../../../translated_images/pt-PT/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Crédito da Imagem: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) A figura mostra outros domínios e exemplos de aplicação de técnicas de ciência de dados. Queres explorar outras aplicações? Consulta a secção [Revisão e Autoestudo](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) abaixo. diff --git a/translations/pt/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/pt/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index e7a834e5..1d1639b6 100644 --- a/translations/pt/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/pt/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ A interface do Explorer (mostrada na imagem abaixo) permite-te selecionar um con 2. Explorar o [Catálogo](https://planetarycomputer.microsoft.com/catalog) de conjuntos de dados - aprender o propósito de cada um. 3. Usar o Explorer - escolher um conjunto de dados do teu interesse, selecionar uma consulta relevante e uma opção de renderização. -![O Explorer do Planetary Computer](../../../../translated_images/pt/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) +![O Explorer do Planetary Computer](../../../../translated_images/pt-PT/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `A Tua Tarefa:` Agora analisa a visualização que foi gerada no navegador e responde às seguintes questões: diff --git a/translations/pt/CONTRIBUTING.md b/translations/pt/CONTRIBUTING.md index c49d3ac3..862488e8 100644 --- a/translations/pt/CONTRIBUTING.md +++ b/translations/pt/CONTRIBUTING.md @@ -316,7 +316,7 @@ Inclua na descrição do seu PR: ``` ```` -- Adicione texto alternativo às imagens: `![Texto alternativo](../../translated_images/pt/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` +- Adicione texto alternativo às imagens: `![Texto alternativo](../../translated_images/pt-PT/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Mantenha comprimentos de linha razoáveis (cerca de 80-100 caracteres) ### Python diff --git a/translations/pt/README.md b/translations/pt/README.md index 8fde8075..e1b9fc5c 100644 --- a/translations/pt/README.md +++ b/translations/pt/README.md @@ -33,7 +33,7 @@ Os Azure Cloud Advocates na Microsoft têm o prazer de oferecer um currículo de **🙏 Agradecimentos especiais 🙏 aos nossos autores, revisores e contribuintes de conteúdos do [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** nomeadamente Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/pt/00-Title.8af36cd35da1ac55.webp)| +|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/pt-PT/00-Title.8af36cd35da1ac55.webp)| |:---:| | Data Science For Beginners - _Sketchnote por [@nitya](https://twitter.com/nitya)_ | @@ -62,7 +62,7 @@ Os Azure Cloud Advocates na Microsoft têm o prazer de oferecer um currículo de Temos uma série de aprendizagem com IA no Discord em andamento, saiba mais e junte-se a nós em [Série Learn with AI](https://aka.ms/learnwithai/discord) entre 18 e 30 de setembro de 2025. Vai receber dicas e truques sobre como usar o GitHub Copilot para Ciência de Dados. -![Learn with AI series](../../../../translated_images/pt/1.2b28cdc6205e26fe.webp) +![Learn with AI series](../../../../translated_images/pt-PT/1.2b28cdc6205e26fe.webp) # É estudante? @@ -142,7 +142,7 @@ Cada exemplo inclui comentários detalhados que explicam cada passo, tornando-o ## Aulas -|![ Sketchnote de @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/pt/00-Roadmap.4905d6567dff4753.webp)| +|![ Sketchnote de @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/pt-PT/00-Roadmap.4905d6567dff4753.webp)| |:---:| | Ciência de Dados para Iniciantes: Roteiro - _Sketchnote por [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/pt/sketchnotes/README.md b/translations/pt/sketchnotes/README.md index 0a2e90d4..375b067c 100644 --- a/translations/pt/sketchnotes/README.md +++ b/translations/pt/sketchnotes/README.md @@ -13,7 +13,7 @@ Encontre todas as sketchnotes aqui! Nitya Narasimhan, artista -![sketchnote do roadmap](../../../translated_images/pt/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) +![sketchnote do roadmap](../../../translated_images/pt-PT/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) **Aviso Legal**: Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original no seu idioma nativo deve ser considerado a fonte autoritária. Para informações críticas, recomenda-se uma tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas resultantes do uso desta tradução. \ No newline at end of file diff --git a/translations/tw/1-Introduction/01-defining-data-science/README.md b/translations/tw/1-Introduction/01-defining-data-science/README.md index fc996387..79b14c42 100644 --- a/translations/tw/1-Introduction/01-defining-data-science/README.md +++ b/translations/tw/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![資料科學定義影片](../../../../translated_images/tw/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) +[![資料科學定義影片](../../../../translated_images/zh-TW/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ CO_OP_TRANSLATOR_METADATA: 在這次挑戰中，我們將嘗試通過分析文本來找出與資料科學領域相關的概念。我們將選取一篇關於資料科學的維基百科文章，下載並處理文本，然後建立一個像這樣的文字雲： -![資料科學文字雲](../../../../translated_images/tw/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) +![資料科學文字雲](../../../../translated_images/zh-TW/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) 請訪問 [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') 閱讀程式碼。您也可以執行程式碼，並即時查看它如何進行所有的資料轉換。 diff --git a/translations/tw/1-Introduction/04-stats-and-probability/README.md b/translations/tw/1-Introduction/04-stats-and-probability/README.md index 65ed2a69..f16a48b2 100644 --- a/translations/tw/1-Introduction/04-stats-and-probability/README.md +++ b/translations/tw/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 統計學與機率論是數學中高度相關的兩個領域，對於數據科學來說尤為重要。即使在缺乏深厚數學知識的情況下也可以操作數據，但了解一些基本概念仍然是有益的。在這裡，我們將提供一個簡短的介紹，幫助您入門。 -[![介紹影片](../../../../translated_images/tw/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) +[![介紹影片](../../../../translated_images/zh-TW/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ CO_OP_TRANSLATOR_METADATA: 我們只能討論變數落在某個值區間內的機率，例如 P(t1≤X2)。在這種情況下，機率分佈由 **機率密度函數** p(x) 描述，其滿足以下公式： -![P(t_1\le X 更多關於相關性和協方差的示例可以在 [附帶的筆記本](notebook.ipynb) 中找到。 diff --git a/translations/tw/1-Introduction/README.md b/translations/tw/1-Introduction/README.md index 63c5315e..82be0add 100644 --- a/translations/tw/1-Introduction/README.md +++ b/translations/tw/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 資料科學入門 -![數據運作](../../../translated_images/tw/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) +![數據運作](../../../translated_images/zh-TW/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > 照片由 Stephen Dawson 提供，來自 Unsplash 在這些課程中，您將了解資料科學的定義，並學習作為資料科學家必須考慮的倫理問題。您還將學習資料的定義，並簡單了解統計與機率，這些是資料科學的核心學術領域。 diff --git a/translations/tw/2-Working-With-Data/07-python/README.md b/translations/tw/2-Working-With-Data/07-python/README.md index 029750d2..7837aa33 100644 --- a/translations/tw/2-Working-With-Data/07-python/README.md +++ b/translations/tw/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | 使用 Python - _由 [@nitya](https://twitter.com/nitya) 繪製的速記圖_ | -[![介紹影片](../../../../translated_images/tw/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) +[![介紹影片](../../../../translated_images/zh-TW/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) 雖然資料庫提供了非常高效的方式來存儲數據並使用查詢語言進行查詢，但最靈活的數據處理方式是編寫自己的程式來操作數據。在許多情況下，使用資料庫查詢可能更有效。然而，在某些需要更複雜數據處理的情況下，使用 SQL 可能不容易完成。 @@ -73,7 +73,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![時間序列圖](../../../../translated_images/tw/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) +![時間序列圖](../../../../translated_images/zh-TW/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) 假設每週我們都會為朋友舉辦派對，並額外拿出 10 盒冰淇淋。我們可以創建另一個以週為索引的 Series 來展示這一點： ```python @@ -84,7 +84,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W" total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![時間序列圖](../../../../translated_images/tw/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) +![時間序列圖](../../../../translated_images/zh-TW/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **注意**：我們並未使用簡單語法 `total_items+additional_items`。如果使用該語法，我們會在結果 Series 中得到許多 `NaN`（*非數值*）值。這是因為在 `additional_items` Series 的某些索引點缺少值，而將 `NaN` 與任何值相加會得到 `NaN`。因此，我們需要在相加時指定 `fill_value` 參數。 @@ -93,7 +93,7 @@ total_items.plot() monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![每月時間序列平均值](../../../../translated_images/tw/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) +![每月時間序列平均值](../../../../translated_images/zh-TW/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -219,7 +219,7 @@ df = pd.read_csv('file.csv') 由於我們想展示如何處理數據，我們邀請你打開 [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) 並從頭到尾閱讀它。你還可以執行單元格，並完成我們在最後為你留下的一些挑戰。 -![COVID 傳播](../../../../translated_images/tw/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) +![COVID 傳播](../../../../translated_images/zh-TW/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > 如果你不知道如何在 Jupyter Notebook 中運行代碼，請查看 [這篇文章](https://soshnikov.com/education/how-to-execute-notebooks-from-github/)。 @@ -241,7 +241,7 @@ df = pd.read_csv('file.csv') 打開 [`notebook-papers.ipynb`](notebook-papers.ipynb) 並從頭到尾閱讀它。你還可以執行單元格，並完成我們在最後為你留下的一些挑戰。 -![COVID 醫療處理](../../../../translated_images/tw/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) +![COVID 醫療處理](../../../../translated_images/zh-TW/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## 處理圖像數據 diff --git a/translations/tw/2-Working-With-Data/README.md b/translations/tw/2-Working-With-Data/README.md index 29f20550..882271ad 100644 --- a/translations/tw/2-Working-With-Data/README.md +++ b/translations/tw/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 使用數據 -![data love](../../../translated_images/tw/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) +![data love](../../../translated_images/zh-TW/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > 圖片由 Alexander Sinn 提供，來自 Unsplash 在這些課程中，您將學習一些管理、操作和應用數據的方法。您將了解關聯式和非關聯式數據庫，以及數據如何存儲在其中。您還將學習使用 Python 管理數據的基礎知識，並探索使用 Python 管理和挖掘數據的多種方式。 diff --git a/translations/tw/3-Data-Visualization/12-visualization-relationships/README.md b/translations/tw/3-Data-Visualization/12-visualization-relationships/README.md index 0ae172b0..a55fd6e7 100644 --- a/translations/tw/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/tw/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![散點圖 1](../../../../translated_images/tw/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) +![散點圖 1](../../../../translated_images/zh-TW/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) 接下來，使用蜂蜜色調展示價格如何隨年份演變。您可以通過添加 'hue' 參數來顯示年份的變化： @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![散點圖 2](../../../../translated_images/tw/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) +![散點圖 2](../../../../translated_images/zh-TW/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) 通過這種色彩方案的改變，您可以清楚地看到蜂蜜每磅價格在多年來的明顯增長趨勢。事實上，如果您查看數據中的樣本集（例如選擇亞利桑那州），您可以看到價格逐年上漲的模式，僅有少數例外： @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` 您可以看到點的大小逐漸增大。 -![散點圖 3](../../../../translated_images/tw/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) +![散點圖 3](../../../../translated_images/zh-TW/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) 這是否是一個簡單的供需問題？由於氣候變化和蜂群崩潰等因素，是否每年可供購買的蜂蜜減少，因此價格上漲？ @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` 答案：是的，但在 2003 年左右有一些例外： -![折線圖 1](../../../../translated_images/tw/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) +![折線圖 1](../../../../translated_images/zh-TW/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ 由於 Seaborn 將數據聚合到一條線上，它通過繪製均值和均值周圍的 95% 置信區間來顯示「每個 x 值的多個測量值」。[來源](https://seaborn.pydata.org/tutorial/relational.html)。這種耗時的行為可以通過添加 `ci=None` 禁用。 @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![折線圖 2](../../../../translated_images/tw/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) +![折線圖 2](../../../../translated_images/zh-TW/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) 答案：並不完全。如果您查看總產量，實際上在那一年似乎有所增加，儘管總體而言蜂蜜的生產量在這些年中呈下降趨勢。 @@ -139,7 +139,7 @@ sns.relplot( ``` 在此視覺化中，您可以比較逐年每群蜂的產量和蜂群數量，並將列的包裹設置為 3： -![Facet Grid](../../../../translated_images/tw/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) +![Facet Grid](../../../../translated_images/zh-TW/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) 對於這個數據集，逐年和逐州的蜂群數量及其產量並沒有特別突出的地方。是否有其他方式來尋找這兩個變數之間的相關性？ @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![疊加折線圖](../../../../translated_images/tw/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) +![疊加折線圖](../../../../translated_images/zh-TW/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) 雖然在 2003 年左右沒有明顯的異常，但這讓我們可以以一個稍微樂觀的結論結束本課：儘管蜂群數量總體上在下降，但蜂群數量正在穩定，即使每群蜂的產量在減少。 diff --git a/translations/tw/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/tw/3-Data-Visualization/R/09-visualization-quantities/README.md index 743279e5..12fd113c 100644 --- a/translations/tw/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/tw/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -67,7 +67,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` 在這裡，您安裝了 `ggplot2` 套件，然後使用 `library("ggplot2")` 命令將其導入工作空間。要在 ggplot 中繪製任何圖表，使用 `ggplot()` 函數並指定數據集、x 和 y 變量作為屬性。在此情況下，我們使用 `geom_line()` 函數，因為我們的目標是繪製折線圖。 -![MaxWingspan-lineplot](../../../../../translated_images/tw/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) +![MaxWingspan-lineplot](../../../../../translated_images/zh-TW/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) 您立即注意到什麼？似乎至少有一個異常值——那是一個相當大的翼展！2000+ 公分的翼展超過 20 公尺——明尼蘇達州有翼龍在漫遊嗎？讓我們調查一下。 @@ -85,7 +85,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` 我們在 `theme` 中指定角度，並分別在 `xlab()` 和 `ylab()` 中指定 x 和 y 軸標籤。`ggtitle()` 為圖表/圖形命名。 -![MaxWingspan-lineplot-improved](../../../../../translated_images/tw/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/zh-TW/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) 即使將標籤旋轉設置為 45 度，仍然有太多標籤無法閱讀。讓我們嘗試另一種策略：僅標記那些異常值並在圖表內設置標籤。您可以使用散點圖來為標籤留出更多空間： @@ -101,7 +101,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + 您發現了什麼？ -![MaxWingspan-scatterplot](../../../../../translated_images/tw/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) +![MaxWingspan-scatterplot](../../../../../translated_images/zh-TW/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## 篩選數據 @@ -120,7 +120,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` 我們創建了一個新的數據框 `birds_filtered`，然後繪製了一個散點圖。通過篩選掉異常值，您的數據現在更加一致且易於理解。 -![MaxWingspan-scatterplot-improved](../../../../../translated_images/tw/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/zh-TW/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) 現在我們至少在翼展方面有了一個更乾淨的數據集，讓我們進一步了解這些鳥類。 @@ -163,7 +163,7 @@ birds_filtered %>% group_by(Category) %>% ``` 在以下代碼片段中，我們安裝了 [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) 和 [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) 套件，以幫助操作和分組數據以繪製堆疊條形圖。首先，您按鳥類的 `Category` 分組數據，然後總結 `MinLength`、`MaxLength`、`MinBodyMass`、`MaxBodyMass`、`MinWingspan`、`MaxWingspan` 列。接著，使用 `ggplot2` 套件繪製條形圖並指定不同類別的顏色和標籤。 -![Stacked bar chart](../../../../../translated_images/tw/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) +![Stacked bar chart](../../../../../translated_images/zh-TW/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) 然而，這個條形圖因為有太多未分組的數據而難以閱讀。您需要選擇要繪製的數據，因此讓我們看看基於鳥類類別的鳥類長度。 @@ -178,7 +178,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` 您首先計算 `Category` 列中的唯一值，然後將它們排序到新的數據框 `birds_count` 中。這些排序後的數據在相同層次中進行分級，以便按排序方式繪製。使用 `ggplot2`，您接著繪製條形圖。`coord_flip()` 繪製水平條形圖。 -![category-length](../../../../../translated_images/tw/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) +![category-length](../../../../../translated_images/zh-TW/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) 此條形圖清楚地顯示了每個類別中鳥類的數量。一眼就能看出，在這個地區，鴨/鵝/水禽類別的鳥類數量最多。明尼蘇達州是“萬湖之地”，所以這並不令人驚訝！ @@ -201,7 +201,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` 我們按 `Category` 分組 `birds_filtered` 數據，然後繪製條形圖。 -![comparing data](../../../../../translated_images/tw/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) +![comparing data](../../../../../translated_images/zh-TW/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) 這裡沒有什麼令人驚訝的：蜂鳥的最大長度比鵜鶘或鵝要小得多。當數據符合邏輯時，這是件好事！ @@ -213,7 +213,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/tw/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) +![super-imposed values](../../../../../translated_images/zh-TW/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 挑戰 diff --git a/translations/tw/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/tw/3-Data-Visualization/R/10-visualization-distributions/README.md index db080055..bf1d1cd8 100644 --- a/translations/tw/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/tw/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![每目最大長度](../../../../../translated_images/tw/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) +![每目最大長度](../../../../../translated_images/zh-TW/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) 這提供了每個鳥類目身體長度的一般分佈概覽，但這並不是顯示真實分佈的最佳方式。這項任務通常通過創建直方圖來完成。 ## 使用直方圖 @@ -56,7 +56,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![整個數據集的分佈](../../../../../translated_images/tw/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) +![整個數據集的分佈](../../../../../translated_images/zh-TW/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) 如你所見，這個數據集中大多數的 400 多種鳥類的最大體重都在 2000 以下。通過將 `bins` 參數更改為更高的數字，例如 30，可以獲得更多的數據洞察： @@ -64,7 +64,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![30 個區間的分佈](../../../../../translated_images/tw/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) +![30 個區間的分佈](../../../../../translated_images/zh-TW/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) 此圖表以更細緻的方式顯示了分佈。通過確保僅選擇特定範圍內的數據，可以創建一個不那麼偏向左側的圖表： @@ -76,7 +76,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![篩選後的直方圖](../../../../../translated_images/tw/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) +![篩選後的直方圖](../../../../../translated_images/zh-TW/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ 嘗試其他篩選條件和數據點。要查看數據的完整分佈，移除 `['MaxBodyMass']` 篩選條件以顯示帶標籤的分佈。 @@ -90,7 +90,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` 看起來這兩個元素沿著預期的軸有一個預期的相關性，其中有一個特別強的匯聚點： -![2D 圖](../../../../../translated_images/tw/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) +![2D 圖](../../../../../translated_images/zh-TW/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) 直方圖對於數值數據默認效果很好。如果你需要根據文本數據查看分佈該怎麼辦？ ## 使用文本數據探索數據集的分佈 @@ -121,7 +121,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![翼展與保育狀況的對比](../../../../../translated_images/tw/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) +![翼展與保育狀況的對比](../../../../../translated_images/zh-TW/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) 最小翼展與保育狀況之間似乎沒有明顯的相關性。使用此方法測試數據集的其他元素。你也可以嘗試不同的篩選條件。你發現了任何相關性嗎？ @@ -135,7 +135,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![密度圖](../../../../../translated_images/tw/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) +![密度圖](../../../../../translated_images/zh-TW/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) 你可以看到，這個圖表反映了之前的最小翼展數據，只是稍微平滑了一些。如果你想重新訪問第二個圖表中那條鋸齒狀的 MaxBodyMass 線，可以通過這種方法非常好地將其平滑化： @@ -143,7 +143,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![體重密度](../../../../../translated_images/tw/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) +![體重密度](../../../../../translated_images/zh-TW/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) 如果你想要一條平滑但不過於平滑的線，可以編輯 `adjust` 參數： @@ -151,7 +151,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![較不平滑的體重](../../../../../translated_images/tw/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) +![較不平滑的體重](../../../../../translated_images/zh-TW/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ 閱讀此類圖表可用的參數並進行實驗！ @@ -161,7 +161,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![每目體重](../../../../../translated_images/tw/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) +![每目體重](../../../../../translated_images/zh-TW/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 挑戰 diff --git a/translations/tw/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/tw/3-Data-Visualization/R/11-visualization-proportions/README.md index cfc7dddd..472712de 100644 --- a/translations/tw/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/tw/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -93,7 +93,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` 瞧，一個圓餅圖展示了根據這兩類蘑菇的比例數據。在這裡，正確的標籤順序非常重要，因此請務必確認標籤數組的構建順序！ -![圓餅圖](../../../../../translated_images/tw/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) +![圓餅圖](../../../../../translated_images/zh-TW/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## 甜甜圈圖！ @@ -128,7 +128,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![甜甜圈圖](../../../../../translated_images/tw/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) +![甜甜圈圖](../../../../../translated_images/zh-TW/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) 此代碼使用了兩個庫 - ggplot2 和 webr。使用 webr 庫的 PieDonut 函數，我們可以輕鬆創建甜甜圈圖！ @@ -166,7 +166,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual 使用華夫圖，你可以清楚地看到此蘑菇數據集中菌蓋顏色的比例。有趣的是，有許多綠色菌蓋的蘑菇！ -![華夫圖](../../../../../translated_images/tw/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) +![華夫圖](../../../../../translated_images/zh-TW/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) 在本課程中，你學到了三種視覺化比例的方法。首先，你需要將數據分組到分類中，然後決定哪種方式最適合顯示數據 - 圓餅圖、甜甜圈圖或華夫圖。這些方法都很有趣，並能讓用戶快速了解數據集。 diff --git a/translations/tw/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/tw/3-Data-Visualization/R/12-visualization-relationships/README.md index 71f79f03..2e840e4e 100644 --- a/translations/tw/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/tw/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![散點圖 1](../../../../../translated_images/tw/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) +![散點圖 1](../../../../../translated_images/zh-TW/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) 接下來，使用蜂蜜色彩方案展示價格如何隨年份演變。您可以通過添加 `scale_color_gradientn` 參數來展示年份的變化： @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![散點圖 2](../../../../../translated_images/tw/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) +![散點圖 2](../../../../../translated_images/zh-TW/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) 使用這種色彩方案，您可以清楚地看到蜂蜜每磅價格在多年來的明顯增長趨勢。事實上，如果您查看數據中的樣本集（例如選擇亞利桑那州），您可以看到價格逐年上漲的模式，只有少數例外： @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` 您可以看到點的大小逐漸增大。 -![散點圖 3](../../../../../translated_images/tw/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) +![散點圖 3](../../../../../translated_images/zh-TW/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) 這是否是一個簡單的供需問題？由於氣候變化和蜂群崩壞等因素，是否每年可供購買的蜂蜜減少，導致價格上漲？ @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` 答案：是的，但在2003年左右有一些例外： -![折線圖 1](../../../../../translated_images/tw/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) +![折線圖 1](../../../../../translated_images/zh-TW/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) 問題：那麼在2003年，我們是否也能看到蜂蜜供應的激增？如果您查看每年的總產量呢？ @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![折線圖 2](../../../../../translated_images/tw/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) +![折線圖 2](../../../../../translated_images/zh-TW/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) 答案：並不完全。如果您查看總產量，實際上在那一年似乎有所增加，儘管總的來說蜂蜜的生產量在這些年中呈下降趨勢。 @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` 在這個視覺化中，您可以比較每年的每群產量和蜂群數量，並將列的分面設置為3： -![分面網格](../../../../../translated_images/tw/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) +![分面網格](../../../../../translated_images/zh-TW/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) 對於這個數據集，關於蜂群數量和每群產量，年份與州之間並沒有特別突出的地方。是否有其他方式可以找到這兩個變數之間的相關性？ @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![疊加折線圖](../../../../../translated_images/tw/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) +![疊加折線圖](../../../../../translated_images/zh-TW/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) 雖然在2003年沒有明顯的異常，但這讓我們可以以一個稍微樂觀的結論結束這節課：儘管蜂群數量總體上在下降，但蜂群數量正在穩定，即使每群產量在減少。 diff --git a/translations/tw/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/tw/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 169fdfdf..9ca0fe2b 100644 --- a/translations/tw/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/tw/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: 即使數據科學家謹慎地為數據選擇了正確的圖表類型，數據仍然可能以某種方式被展示來證明某個觀點，往往以犧牲數據本身為代價。有許多關於誤導性圖表和信息圖的例子！ -[![Alberto Cairo 的《圖表如何說謊》](../../../../../translated_images/tw/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "圖表如何說謊") +[![Alberto Cairo 的《圖表如何說謊》](../../../../../translated_images/zh-TW/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "圖表如何說謊") > 🎥 點擊上方圖片觀看關於誤導性圖表的會議演講 這張圖表反轉了 X 軸，根據日期顯示了與事實相反的內容： -![壞圖表 1](../../../../../translated_images/tw/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) +![壞圖表 1](../../../../../translated_images/zh-TW/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [這張圖表](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) 更加誤導，因為視線被吸引到右側，讓人得出結論：隨著時間推移，各縣的 COVID 病例數量下降了。事實上，如果仔細查看日期，你會發現它們被重新排列以製造這種誤導性的下降趨勢。 -![壞圖表 2](../../../../../translated_images/tw/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) +![壞圖表 2](../../../../../translated_images/zh-TW/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) 這個臭名昭著的例子使用了顏色和反轉的 Y 軸來誤導：與其得出槍支友好立法通過後槍支死亡人數激增的結論，事實上視線被誤導以為情況正好相反： -![壞圖表 3](../../../../../translated_images/tw/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) +![壞圖表 3](../../../../../translated_images/zh-TW/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) 這張奇怪的圖表展示了比例如何被操縱，效果令人啼笑皆非： -![壞圖表 4](../../../../../translated_images/tw/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) +![壞圖表 4](../../../../../translated_images/zh-TW/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) 比較無法比較的事物是另一種陰險的手段。有一個[精彩的網站](https://tylervigen.com/spurious-correlations)專門展示「虛假的相關性」，顯示像緬因州離婚率與人造黃油消耗量這樣的「事實」。Reddit 上也有一個群組收集了[數據的醜陋用法](https://www.reddit.com/r/dataisugly/top/?t=all)。 @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: 如果你的數據在 X 軸上是文本且冗長，可以將文本傾斜以提高可讀性。[plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) 提供了 3D 繪圖功能，如果你的數據支持的話，可以使用它來製作更高級的數據視覺化。 -![3D 圖表](../../../../../translated_images/tw/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) +![3D 圖表](../../../../../translated_images/zh-TW/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## 動畫和 3D 圖表展示 當今一些最好的數據視覺化是動畫化的。Shirley Wu 使用 D3 創作了許多令人驚嘆的作品，例如「[電影之花](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)」，每朵花都是一部電影的視覺化。另一個例子是《衛報》的「Bussed Out」，這是一個結合了 Greensock 和 D3 的互動體驗，並採用滾動敘事的文章格式，展示了紐約市如何通過將無家可歸者送出城市來處理這一問題。 -![Bussed Out](../../../../../translated_images/tw/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) +![Bussed Out](../../../../../translated_images/zh-TW/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > 「Bussed Out: How America Moves its Homeless」來自[衛報](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study)。視覺化由 Nadieh Bremer 和 Shirley Wu 創作。 @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: 你將完成一個網頁應用，展示這個社交網絡的動畫化視圖。它使用了一個基於 Vue.js 和 D3 的庫來創建[網絡視覺化](https://github.com/emiliorizzo/vue-d3-network)。應用運行時，你可以在屏幕上拖動節點來重新排列數據。 -![危險關係](../../../../../translated_images/tw/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) +![危險關係](../../../../../translated_images/zh-TW/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## 專案：使用 D3.js 構建一個展示網絡的圖表 diff --git a/translations/tw/3-Data-Visualization/README.md b/translations/tw/3-Data-Visualization/README.md index a145811d..ddf384b8 100644 --- a/translations/tw/3-Data-Visualization/README.md +++ b/translations/tw/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 視覺化 -![一隻蜜蜂停在薰衣草花上](../../../translated_images/tw/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) +![一隻蜜蜂停在薰衣草花上](../../../translated_images/zh-TW/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > 照片由 Jenna Lee 提供，來自 Unsplash 視覺化數據是數據科學家最重要的任務之一。一張圖片勝過千言萬語，視覺化可以幫助你識別數據中的各種有趣部分，例如尖峰、異常值、分組、趨勢等等，這些都能幫助你理解數據背後的故事。 diff --git a/translations/tw/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/tw/4-Data-Science-Lifecycle/14-Introduction/README.md index 8544fb2d..fdb28b87 100644 --- a/translations/tw/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/tw/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 本課程將重點放在生命週期的三個部分：資料捕捉、資料處理和資料維護。 -![資料科學生命週期圖示](../../../../translated_images/tw/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) +![資料科學生命週期圖示](../../../../translated_images/zh-TW/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > 圖片來源：[Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## 資料捕捉 @@ -98,7 +98,7 @@ CO_OP_TRANSLATOR_METADATA: |團隊資料科學過程 (TDSP)|跨行業標準資料挖掘過程 (CRISP-DM)| |--|--| -|![團隊資料科學生命週期](../../../../translated_images/tw/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![資料科學過程聯盟圖示](../../../../translated_images/tw/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | +|![團隊資料科學生命週期](../../../../translated_images/zh-TW/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![資料科學過程聯盟圖示](../../../../translated_images/zh-TW/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | 圖片來源：[Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | 圖片來源：[Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [課後測驗](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/tw/4-Data-Science-Lifecycle/README.md b/translations/tw/4-Data-Science-Lifecycle/README.md index 5bdd1c25..63f2a4a4 100644 --- a/translations/tw/4-Data-Science-Lifecycle/README.md +++ b/translations/tw/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 數據科學生命週期 -![communication](../../../translated_images/tw/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) +![communication](../../../translated_images/zh-TW/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > 圖片由 Headway 提供，來自 Unsplash 在這些課程中，您將探索數據科學生命週期的一些方面，包括數據的分析和溝通。 diff --git a/translations/tw/5-Data-Science-In-Cloud/README.md b/translations/tw/5-Data-Science-In-Cloud/README.md index 91270b72..b42fda68 100644 --- a/translations/tw/5-Data-Science-In-Cloud/README.md +++ b/translations/tw/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # 雲端中的數據科學 -![cloud-picture](../../../translated_images/tw/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) +![cloud-picture](../../../translated_images/zh-TW/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > 照片由 [Jelleke Vanooteghem](https://unsplash.com/@ilumire) 提供，來自 [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) 在處理大數據的數據科學時，雲端可以帶來革命性的改變。在接下來的三節課中，我們將了解什麼是雲端以及它為什麼如此有用。我們還將探索一個心臟衰竭數據集，並建立一個模型來幫助評估某人患心臟衰竭的可能性。我們將利用雲端的強大功能來訓練、部署並以兩種不同的方式使用模型。一種方式是僅使用用戶界面，以低代碼/無代碼的方式進行；另一種方式是使用 Azure 機器學習軟件開發工具包 (Azure ML SDK)。 -![project-schema](../../../translated_images/tw/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) +![project-schema](../../../translated_images/zh-TW/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### 主題 diff --git a/translations/tw/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/tw/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 95df6996..ccb07ec6 100644 --- a/translations/tw/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/tw/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: * [醫療保健中的數據科學](https://data-flair.training/blogs/data-science-in-healthcare/) - 強調應用如醫學影像（例如 MRI、X光、CT掃描）、基因組學（DNA測序）、藥物開發（風險評估、成功預測）、預測分析（患者護理和供應物流）、疾病追蹤和預防等。 -![數據科學在現實世界中的應用](../../../../translated_images/tw/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) 圖片來源：[Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![數據科學在現實世界中的應用](../../../../translated_images/zh-TW/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) 圖片來源：[Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) 該圖展示了其他領域和應用數據科學技術的例子。想探索其他應用嗎？查看下面的[回顧與自學](../../../../6-Data-Science-In-Wild/20-Real-World-Examples)部分。 diff --git a/translations/tw/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/tw/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 8f99bc17..7c98392d 100644 --- a/translations/tw/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/tw/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Explorer界面（如下圖所示）允許您選擇數據集（從提供的選項 2. 探索數據集[目錄](https://planetarycomputer.microsoft.com/catalog)——了解每個數據集的用途。 3. 使用Explorer——選擇一個感興趣的數據集，選擇相關的查詢和渲染選項。 -![行星電腦Explorer](../../../../translated_images/tw/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) +![行星電腦Explorer](../../../../translated_images/zh-TW/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `您的任務：` 現在研究瀏覽器中渲染的可視化，並回答以下問題： diff --git a/translations/tw/CONTRIBUTING.md b/translations/tw/CONTRIBUTING.md index 89e653a0..8aba7d6a 100644 --- a/translations/tw/CONTRIBUTING.md +++ b/translations/tw/CONTRIBUTING.md @@ -311,7 +311,7 @@ def calculate_mean(data): import pandas as pd ``` ```` -- 為圖片添加替代文字：`![替代文字](../../translated_images/tw/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` +- 為圖片添加替代文字：`![替代文字](../../translated_images/zh-TW/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - 保持合理的行長度（約 80-100 字元） ### Python diff --git a/translations/tw/README.md b/translations/tw/README.md index 4d42164e..06eddfa4 100644 --- a/translations/tw/README.md +++ b/translations/tw/README.md @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: **🙏 特別感謝 🙏 我們的 [Microsoft 學生大使](https://studentambassadors.microsoft.com/) 作者、審稿人及內容貢獻者，** 特別是 Aaryan Arora、[Aditya Garg](https://github.com/AdityaGarg00)、[Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/)、[Ankita Singh](https://www.linkedin.com/in/ankitasingh007)、[Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/)、[Arpita Das](https://www.linkedin.com/in/arpitadas01/)、ChhailBihari Dubey、[Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor)、[Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb)、[Majd Safi](https://www.linkedin.com/in/majd-s/)、[Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/)、[Miguel Correa](https://www.linkedin.com/in/miguelmque/)、[Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119)、[Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum)、[Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/)、[Rohit Yadav](https://www.linkedin.com/in/rty2423)、Samridhi Sharma、[Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200)、[Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/)、[Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/)、Yogendrasingh Pawar、[Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/)、[Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/tw/00-Title.8af36cd35da1ac55.webp)| +|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/zh-TW/00-Title.8af36cd35da1ac55.webp)| |:---:| | 初學者資料科學 - _筆記由 [@nitya](https://twitter.com/nitya) 繪製_ | @@ -61,7 +61,7 @@ CO_OP_TRANSLATOR_METADATA: 我們目前有一個 Discord 上的 AI 學習系列活動，詳情請參閱並加入我們的 [學習 AI 系列](https://aka.ms/learnwithai/discord)，時間從 2025 年 9 月 18 日到 30 日。你將獲得使用 GitHub Copilot 進行資料科學的各種技巧和小秘訣。 -![Learn with AI series](../../../../translated_images/tw/1.2b28cdc6205e26fe.webp) +![Learn with AI series](../../../../translated_images/zh-TW/1.2b28cdc6205e26fe.webp) # 你是學生嗎？ @@ -140,7 +140,7 @@ CO_OP_TRANSLATOR_METADATA: ## 課程內容 -|![ Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/tw/00-Roadmap.4905d6567dff4753.webp)| +|![ Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/zh-TW/00-Roadmap.4905d6567dff4753.webp)| |:---:| | 資料科學初學者路線圖 - _手繪筆記作者 [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/tw/sketchnotes/README.md b/translations/tw/sketchnotes/README.md index 59dd21fe..173564c3 100644 --- a/translations/tw/sketchnotes/README.md +++ b/translations/tw/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: Nitya Narasimhan，藝術家 -![路線圖手繪筆記](../../../translated_images/tw/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) +![路線圖手繪筆記](../../../translated_images/zh-TW/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) **免責聲明**： 本文件使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於關鍵資訊，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋不承擔責任。 \ No newline at end of file diff --git a/translations/zh/1-Introduction/01-defining-data-science/README.md b/translations/zh/1-Introduction/01-defining-data-science/README.md index b7cc28a7..8436f7c1 100644 --- a/translations/zh/1-Introduction/01-defining-data-science/README.md +++ b/translations/zh/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![数据科学定义视频](../../../../translated_images/zh/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) +[![数据科学定义视频](../../../../translated_images/zh-CN/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ CO_OP_TRANSLATOR_METADATA: 在这个挑战中，我们将尝试通过分析文本来找到与数据科学领域相关的概念。我们将选取一篇关于数据科学的维基百科文章，下载并处理文本，然后构建一个像这样的词云： -![数据科学词云](../../../../translated_images/zh/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) +![数据科学词云](../../../../translated_images/zh-CN/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) 访问 [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') 阅读代码。你也可以运行代码，实时查看它如何执行所有数据转换。 diff --git a/translations/zh/1-Introduction/04-stats-and-probability/README.md b/translations/zh/1-Introduction/04-stats-and-probability/README.md index 47845fbd..60dc6d70 100644 --- a/translations/zh/1-Introduction/04-stats-and-probability/README.md +++ b/translations/zh/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 统计学和概率论是数学中两个密切相关的领域，与数据科学高度相关。虽然在没有深厚数学知识的情况下也可以处理数据，但了解一些基本概念仍然是有益的。在这里，我们将提供一个简短的介绍，帮助您入门。 -[![介绍视频](../../../../translated_images/zh/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) +[![介绍视频](../../../../translated_images/zh-CN/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ CO_OP_TRANSLATOR_METADATA: 我们只能讨论变量落入某个值区间的概率，例如 P(t1≤X2)。在这种情况下，概率分布由 **概率密度函数** p(x) 描述，其满足： -![P(t_1\le X 更多关于相关性和协方差的示例可以在 [配套笔记本](notebook.ipynb) 中找到。 diff --git a/translations/zh/1-Introduction/README.md b/translations/zh/1-Introduction/README.md index 4e569a4a..759d02ef 100644 --- a/translations/zh/1-Introduction/README.md +++ b/translations/zh/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 数据科学简介 -![数据的实际应用](../../../translated_images/zh/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) +![数据的实际应用](../../../translated_images/zh-CN/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > 图片由 Stephen Dawson 提供，来自 Unsplash 在这些课程中，您将了解数据科学的定义，并学习数据科学家必须考虑的伦理问题。您还将学习数据的定义，并对统计学和概率论有一些初步了解，这些是数据科学的核心学术领域。 diff --git a/translations/zh/2-Working-With-Data/07-python/README.md b/translations/zh/2-Working-With-Data/07-python/README.md index 02b6a403..30c41ee6 100644 --- a/translations/zh/2-Working-With-Data/07-python/README.md +++ b/translations/zh/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | 使用Python - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | -[![介绍视频](../../../../translated_images/zh/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) +[![介绍视频](../../../../translated_images/zh-CN/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) 虽然数据库提供了非常高效的方式来存储数据并通过查询语言进行查询，但最灵活的数据处理方式是编写自己的程序来操作数据。在许多情况下，使用数据库查询可能更有效。然而，当需要更复杂的数据处理时，SQL可能无法轻松完成。 数据处理可以用任何编程语言编写，但有些语言在处理数据方面更高级。数据科学家通常偏好以下语言之一： @@ -72,7 +72,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![时间序列图](../../../../translated_images/zh/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) +![时间序列图](../../../../translated_images/zh-CN/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) 假设每周我们都会举办一个朋友聚会，并额外拿出10盒冰淇淋用于聚会。我们可以创建另一个以周为索引的Series来展示这一点： ```python @@ -83,7 +83,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W" total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![时间序列图](../../../../translated_images/zh/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) +![时间序列图](../../../../translated_images/zh-CN/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **注意** 我们没有使用简单的语法 `total_items+additional_items`。如果使用这种方法，我们会在结果Series中得到许多`NaN`（*Not a Number*）值。这是因为在`additional_items`的某些索引点上缺少值，而将`NaN`与任何值相加都会得到`NaN`。因此，我们需要在相加时指定`fill_value`参数。 @@ -92,7 +92,7 @@ total_items.plot() monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![每月时间序列平均值](../../../../translated_images/zh/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) +![每月时间序列平均值](../../../../translated_images/zh-CN/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame（数据框） @@ -218,7 +218,7 @@ df = pd.read_csv('file.csv') 由于我们想演示如何处理数据，我们邀请你打开 [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) 并从头到尾阅读。你也可以执行单元格，并完成我们在最后留下的一些挑战。 -![COVID 传播](../../../../translated_images/zh/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) +![COVID 传播](../../../../translated_images/zh-CN/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > 如果你不知道如何在 Jupyter Notebook 中运行代码，可以查看 [这篇文章](https://soshnikov.com/education/how-to-execute-notebooks-from-github/)。 @@ -240,7 +240,7 @@ df = pd.read_csv('file.csv') 打开 [`notebook-papers.ipynb`](notebook-papers.ipynb) 并从头到尾阅读。你也可以执行单元格，并完成我们在最后留下的一些挑战。 -![COVID 医疗处理](../../../../translated_images/zh/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) +![COVID 医疗处理](../../../../translated_images/zh-CN/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## 处理图像数据 diff --git a/translations/zh/2-Working-With-Data/README.md b/translations/zh/2-Working-With-Data/README.md index b59bce68..b9c77df4 100644 --- a/translations/zh/2-Working-With-Data/README.md +++ b/translations/zh/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 数据处理 -![data love](../../../translated_images/zh/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) +![data love](../../../translated_images/zh-CN/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > 图片由 Alexander Sinn 提供，来自 Unsplash 在这些课程中，您将学习一些管理、操作和在应用程序中使用数据的方法。您将了解关系型和非关系型数据库，以及数据如何存储在其中。您将学习使用 Python 管理数据的基础知识，并探索多种使用 Python 管理和挖掘数据的方法。 diff --git a/translations/zh/3-Data-Visualization/12-visualization-relationships/README.md b/translations/zh/3-Data-Visualization/12-visualization-relationships/README.md index f30c8a1b..a12f1924 100644 --- a/translations/zh/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/zh/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/zh/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) +![scatterplot 1](../../../../translated_images/zh-CN/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) 现在，用蜂蜜色调展示同样的数据，显示价格如何逐年变化。你可以通过添加一个“hue”参数来展示逐年的变化： @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/zh/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) +![scatterplot 2](../../../../translated_images/zh-CN/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) 通过这个颜色方案的变化，你可以明显看到蜂蜜每磅价格在逐年强劲增长。如果你查看数据中的一个样本集（例如选择亚利桑那州），你会发现价格逐年上涨的模式，虽然有少数例外： @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` 你可以看到点的大小逐渐增加。 -![scatterplot 3](../../../../translated_images/zh/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) +![scatterplot 3](../../../../translated_images/zh-CN/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) 这是否是一个简单的供需问题？由于气候变化和蜂群崩溃等因素，蜂蜜的供应逐年减少，因此价格上涨？ @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` 答案：是的，除了2003年左右的一些例外： -![line chart 1](../../../../translated_images/zh/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) +![line chart 1](../../../../translated_images/zh-CN/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ 由于Seaborn对数据进行聚合，它通过绘制均值和均值周围的95%置信区间来显示“每个x值的多个测量值”。[来源](https://seaborn.pydata.org/tutorial/relational.html)。这种耗时的行为可以通过添加`ci=None`来禁用。 @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/zh/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) +![line chart 2](../../../../translated_images/zh-CN/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) 答案：并不完全。如果你查看总产量，实际上在那一年似乎有所增加，尽管总体而言蜂蜜的产量在这些年间呈下降趋势。 @@ -139,7 +139,7 @@ sns.relplot( ``` 在这个可视化中，你可以比较逐年的每群产量和蜂群数量，并将列的wrap设置为3： -![facet grid](../../../../translated_images/zh/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) +![facet grid](../../../../translated_images/zh-CN/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) 对于这个数据集，逐年和各州之间的蜂群数量及其产量并没有特别显著的变化。是否有其他方法可以找到这两个变量之间的相关性？ @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/zh/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) +![superimposed plots](../../../../translated_images/zh-CN/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) 虽然2003年没有明显的异常，但这确实让我们以一个稍微乐观的结论结束这节课：尽管蜂群数量总体上在下降，但蜂群数量正在趋于稳定，尽管每群产量在减少。 diff --git a/translations/zh/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/zh/3-Data-Visualization/R/09-visualization-quantities/README.md index df1f85f0..c7c59130 100644 --- a/translations/zh/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/zh/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -67,7 +67,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` 在这里，你安装了 `ggplot2` 包并通过 `library("ggplot2")` 命令将其导入工作区。要在 ggplot 中绘制任何图表，使用 `ggplot()` 函数，并将数据集、x 和 y 变量作为属性指定。在这种情况下，我们使用 `geom_line()` 函数，因为我们要绘制折线图。 -![最大翼展折线图](../../../../../translated_images/zh/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) +![最大翼展折线图](../../../../../translated_images/zh-CN/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) 你立即注意到了什么？似乎至少有一个异常值——那是一个相当惊人的翼展！2000+ 厘米的翼展超过了 20 米——难道明尼苏达州有翼龙在飞翔？让我们调查一下。 @@ -85,7 +85,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` 我们在 `theme` 中指定了角度，并在 `xlab()` 和 `ylab()` 中分别指定了 x 和 y 轴的标签。`ggtitle()` 为图表命名。 -![改进后的最大翼展折线图](../../../../../translated_images/zh/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) +![改进后的最大翼展折线图](../../../../../translated_images/zh-CN/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) 即使将标签旋转到 45 度，仍然太多了，难以阅读。让我们尝试另一种策略：仅标记那些异常值，并在图表内设置标签。你可以使用散点图来腾出更多空间进行标记： @@ -101,7 +101,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + 你发现了什么？ -![最大翼展散点图](../../../../../translated_images/zh/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) +![最大翼展散点图](../../../../../translated_images/zh-CN/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## 筛选数据 @@ -120,7 +120,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` 我们创建了一个新的数据框 `birds_filtered`，然后绘制了一个散点图。通过筛选掉异常值，你的数据现在更加连贯且易于理解。 -![改进后的最大翼展散点图](../../../../../translated_images/zh/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) +![改进后的最大翼展散点图](../../../../../translated_images/zh-CN/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) 现在我们至少在翼展方面有了一个更干净的数据集，让我们进一步探索这些鸟类。 @@ -162,7 +162,7 @@ birds_filtered %>% group_by(Category) %>% ``` 在以下代码片段中，我们安装了 [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) 和 [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) 包，以帮助操作和分组数据，从而绘制堆叠条形图。首先，你按鸟类的 `Category` 分组数据，然后汇总 `MinLength`、`MaxLength`、`MinBodyMass`、`MaxBodyMass`、`MinWingspan`、`MaxWingspan` 列。接着，使用 `ggplot2` 包绘制条形图，并为不同类别指定颜色和标签。 -![堆叠条形图](../../../../../translated_images/zh/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) +![堆叠条形图](../../../../../translated_images/zh-CN/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) 然而，这个条形图由于数据未分组过多而难以阅读。你需要选择要绘制的数据，因此让我们根据鸟类类别查看其长度。 @@ -177,7 +177,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` 你首先统计 `Category` 列中的唯一值，然后将它们排序到一个新的数据框 `birds_count` 中。接着，将这些排序后的数据按相同顺序分级，以便按排序方式绘制。使用 `ggplot2` 绘制条形图。`coord_flip()` 将条形图水平显示。 -![类别长度](../../../../../translated_images/zh/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) +![类别长度](../../../../../translated_images/zh-CN/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) 这个条形图很好地展示了每个类别中鸟类的数量。一眼就能看出，这个地区数量最多的鸟类是鸭/鹅/水禽类别。明尼苏达州是“万湖之地”，这并不令人意外！ @@ -200,7 +200,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` 我们按 `Category` 对 `birds_filtered` 数据进行分组，然后绘制条形图。 -![比较数据](../../../../../translated_images/zh/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) +![比较数据](../../../../../translated_images/zh-CN/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) 这里没有什么令人意外的：蜂鸟的最大长度最小，而鹈鹕或鹅的最大长度较大。当数据符合逻辑时，这是好事！ @@ -212,7 +212,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![叠加值](../../../../../translated_images/zh/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) +![叠加值](../../../../../translated_images/zh-CN/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 挑战 diff --git a/translations/zh/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/zh/3-Data-Visualization/R/10-visualization-distributions/README.md index 2b19a7fa..9211d75e 100644 --- a/translations/zh/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/zh/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![每目最大长度](../../../../../translated_images/zh/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) +![每目最大长度](../../../../../translated_images/zh-CN/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) 这提供了每个鸟类目身体长度的一般分布概览，但这并不是显示真实分布的最佳方式。通常通过创建直方图来完成这一任务。 @@ -57,7 +57,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![整个数据集的分布](../../../../../translated_images/zh/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) +![整个数据集的分布](../../../../../translated_images/zh-CN/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) 如你所见，这个数据集中的 400 多种鸟类大多数最大体重都在 2000 以下。通过将 `bins` 参数更改为更高的数字，例如 30，可以获得更多数据洞察： @@ -65,7 +65,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![30个区间的分布](../../../../../translated_images/zh/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) +![30个区间的分布](../../../../../translated_images/zh-CN/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) 此图表以更细致的方式显示分布。通过确保仅选择特定范围内的数据，可以创建一个偏向左侧较少的图表： @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![过滤后的直方图](../../../../../translated_images/zh/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) +![过滤后的直方图](../../../../../translated_images/zh-CN/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ 尝试其他过滤器和数据点。要查看数据的完整分布，请移除 `['MaxBodyMass']` 过滤器以显示带标签的分布。 @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` 可以看到这两个元素沿预期轴存在预期的相关性，其中一个点的收敛特别强： -![二维图](../../../../../translated_images/zh/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) +![二维图](../../../../../translated_images/zh-CN/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) 直方图默认适用于数值数据。如果需要根据文本数据查看分布该怎么办？ @@ -123,7 +123,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![翼展与保护状态的关联](../../../../../translated_images/zh/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) +![翼展与保护状态的关联](../../../../../translated_images/zh-CN/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) 最小翼展与保护状态之间似乎没有明显的相关性。使用此方法测试数据集中的其他元素。你可以尝试不同的过滤器。是否发现任何相关性？ @@ -137,7 +137,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![密度图](../../../../../translated_images/zh/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) +![密度图](../../../../../translated_images/zh-CN/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) 你可以看到此图与之前的最小翼展数据图相呼应；它只是稍微平滑了一些。如果你想重新创建第二个图表中那个不平滑的最大体重线，可以通过这种方法很好地将其平滑化： @@ -145,7 +145,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![体重密度](../../../../../translated_images/zh/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) +![体重密度](../../../../../translated_images/zh-CN/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) 如果你想要一个平滑但不过于平滑的线条，可以编辑 `adjust` 参数： @@ -153,7 +153,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![较少平滑的体重](../../../../../translated_images/zh/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) +![较少平滑的体重](../../../../../translated_images/zh-CN/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ 阅读有关此类图表可用参数的内容并进行实验！ @@ -163,7 +163,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![每目体重](../../../../../translated_images/zh/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) +![每目体重](../../../../../translated_images/zh-CN/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 挑战 diff --git a/translations/zh/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/zh/3-Data-Visualization/R/11-visualization-proportions/README.md index 0be6b02a..7d635a11 100644 --- a/translations/zh/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/zh/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -93,7 +93,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` 瞧，一个饼图展示了根据蘑菇的两种类别的数据比例。在这里，确保标签数组的顺序正确非常重要，因此务必验证标签的构建顺序！ -![饼图](../../../../../translated_images/zh/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) +![饼图](../../../../../translated_images/zh-CN/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## 环形图！ @@ -128,7 +128,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![环形图](../../../../../translated_images/zh/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) +![环形图](../../../../../translated_images/zh-CN/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) 此代码使用了两个库——ggplot2 和 webr。通过 webr 库的 PieDonut 函数，我们可以轻松创建环形图！ @@ -166,7 +166,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual 使用华夫图，你可以清楚地看到蘑菇数据集中帽颜色的比例。有趣的是，有许多绿色帽子的蘑菇！ -![华夫图](../../../../../translated_images/zh/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) +![华夫图](../../../../../translated_images/zh-CN/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) 在本课中，你学习了三种可视化比例的方法。首先，你需要将数据分组为类别，然后决定哪种方式最适合显示数据——饼图、环形图或华夫图。所有这些都很有趣，并能让用户快速了解数据集。 diff --git a/translations/zh/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/zh/3-Data-Visualization/R/12-visualization-relationships/README.md index bddf735a..194170e1 100644 --- a/translations/zh/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/zh/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/zh/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) +![scatterplot 1](../../../../../translated_images/zh-CN/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) 现在，用蜂蜜色调展示同样的数据，显示价格随年份的变化。你可以通过添加`scale_color_gradientn`参数来实现逐年变化的可视化： @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/zh/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) +![scatterplot 2](../../../../../translated_images/zh-CN/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) 通过这个颜色方案的变化，你可以明显看到蜂蜜每磅价格在这些年间逐年上涨。如果你查看数据中的一个样本集（例如亚利桑那州），你会发现价格逐年上涨的模式，虽然有少数例外： @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` 你可以看到点的大小逐渐增大。 -![scatterplot 3](../../../../../translated_images/zh/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) +![scatterplot 3](../../../../../translated_images/zh-CN/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) 这是否是一个简单的供需关系？由于气候变化和蜂群崩溃等因素，是否导致蜂蜜的供应逐年减少，从而价格上涨？ @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` 答案：是的，除了2003年左右的一些例外： -![line chart 1](../../../../../translated_images/zh/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) +![line chart 1](../../../../../translated_images/zh-CN/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) 问题：那么在2003年，我们是否也能看到蜂蜜供应的激增？如果你查看逐年的总产量呢？ @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/zh/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) +![line chart 2](../../../../../translated_images/zh-CN/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) 答案：并不明显。如果你查看总产量，实际上在那一年似乎有所增加，尽管总体而言蜂蜜的产量在这些年间是下降的。 @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` 在这个可视化中，你可以比较逐年蜂群产量和蜂群数量，并将列数设置为3： -![facet grid](../../../../../translated_images/zh/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) +![facet grid](../../../../../translated_images/zh-CN/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) 对于这个数据集，逐年和各州之间，蜂群数量和产量并没有特别突出的变化。是否有其他方法可以发现这两个变量之间的相关性？ @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/zh/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) +![superimposed plots](../../../../../translated_images/zh-CN/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) 虽然2003年没有明显的异常，但这让我们可以以一个稍微乐观的结论结束这节课：尽管蜂群数量总体上在下降，但蜂群数量正在趋于稳定，尽管每群产量在减少。 diff --git a/translations/zh/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/zh/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 88229d3d..ebae3356 100644 --- a/translations/zh/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/zh/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: 即使数据科学家小心选择了适合数据的正确图表，也有很多方法可以通过展示数据来证明某种观点，往往以牺牲数据本身为代价。有许多误导性图表和信息图的例子！ -[![Alberto Cairo 的《图表如何撒谎》](../../../../../translated_images/zh/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "图表如何撒谎") +[![Alberto Cairo 的《图表如何撒谎》](../../../../../translated_images/zh-CN/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "图表如何撒谎") > 🎥 点击上方图片观看关于误导性图表的会议演讲 这个图表颠倒了 X 轴的顺序，根据日期显示了与事实相反的内容： -![错误图表 1](../../../../../translated_images/zh/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) +![错误图表 1](../../../../../translated_images/zh-CN/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [这个图表](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) 更具误导性，因为视觉上会让人得出结论，随着时间推移，各县的 COVID 病例数在下降。实际上，如果仔细查看日期，你会发现它们被重新排列以制造这种误导性的下降趋势。 -![错误图表 2](../../../../../translated_images/zh/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) +![错误图表 2](../../../../../translated_images/zh-CN/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) 这个臭名昭著的例子同时使用了颜色和颠倒的 Y 轴来误导：本应得出枪支死亡人数在通过支持枪支的立法后激增的结论，但实际上视觉上被误导认为相反的情况是真实的： -![错误图表 3](../../../../../translated_images/zh/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) +![错误图表 3](../../../../../translated_images/zh-CN/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) 这个奇怪的图表展示了比例如何被操纵，效果令人啼笑皆非： -![错误图表 4](../../../../../translated_images/zh/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) +![错误图表 4](../../../../../translated_images/zh-CN/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) 比较不可比的事物是另一种阴险的技巧。有一个[精彩的网站](https://tylervigen.com/spurious-correlations) 专门展示“虚假的相关性”，比如缅因州的离婚率与人造黄油消费之间的“事实”相关性。Reddit 上还有一个小组收集了[数据的丑陋用法](https://www.reddit.com/r/dataisugly/top/?t=all)。 @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: 如果你的数据在 X 轴上是文本且较长，可以将文本倾斜以提高可读性。[plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) 提供了 3D 绘图功能，如果你的数据支持的话，可以用它制作复杂的数据可视化。 -![3D 图表](../../../../../translated_images/zh/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) +![3D 图表](../../../../../translated_images/zh-CN/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## 动画和 3D 图表展示 如今一些最佳的数据可视化是动画的。Shirley Wu 使用 D3 制作了许多惊艳的作品，例如“[电影之花](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)”，每朵花都是一部电影的可视化。另一个为《卫报》制作的例子是“Bussed Out”，一个结合了 Greensock 和 D3 的交互式体验，通过滚动叙事文章格式展示纽约市如何通过将无家可归者送出城市来处理其无家可归问题。 -![Bussed Out](../../../../../translated_images/zh/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) +![Bussed Out](../../../../../translated_images/zh-CN/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > “Bussed Out: 美国如何转移无家可归者” 来自 [卫报](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study)。可视化由 Nadieh Bremer 和 Shirley Wu 制作 @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: 你将完成一个网络应用，展示这个社交网络的动画视图。它使用了一个库来创建[网络可视化](https://github.com/emiliorizzo/vue-d3-network)，基于 Vue.js 和 D3。当应用运行时，你可以在屏幕上拖动节点以重新排列数据。 -![危险关系](../../../../../translated_images/zh/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) +![危险关系](../../../../../translated_images/zh-CN/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## 项目：使用 D3.js 构建一个展示网络的图表 diff --git a/translations/zh/3-Data-Visualization/README.md b/translations/zh/3-Data-Visualization/README.md index 91223cf0..08627d27 100644 --- a/translations/zh/3-Data-Visualization/README.md +++ b/translations/zh/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 可视化 -![一只蜜蜂停在薰衣草花上](../../../translated_images/zh/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) +![一只蜜蜂停在薰衣草花上](../../../translated_images/zh-CN/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > 图片由 Jenna Lee 提供，来自 Unsplash 数据可视化是数据科学家最重要的任务之一。图片胜过千言万语，可视化可以帮助你识别数据中的各种有趣部分，例如峰值、异常值、分组、趋势等，从而帮助你理解数据背后的故事。 diff --git a/translations/zh/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/zh/4-Data-Science-Lifecycle/14-Introduction/README.md index 4725dcda..12c8c626 100644 --- a/translations/zh/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/zh/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 本课程重点讲解生命周期中的三个部分：数据捕获、数据处理和数据维护。 -![数据科学生命周期图示](../../../../translated_images/zh/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) +![数据科学生命周期图示](../../../../translated_images/zh-CN/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > 图片来源：[伯克利信息学院](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## 数据捕获 @@ -98,7 +98,7 @@ CO_OP_TRANSLATOR_METADATA: |团队数据科学过程 (TDSP)|跨行业数据挖掘标准过程 (CRISP-DM)| |--|--| -|![团队数据科学生命周期](../../../../translated_images/zh/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![数据科学过程联盟图片](../../../../translated_images/zh/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | +|![团队数据科学生命周期](../../../../translated_images/zh-CN/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![数据科学过程联盟图片](../../../../translated_images/zh-CN/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | 图片来源：[Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | 图片来源：[数据科学过程联盟](https://www.datascience-pm.com/crisp-dm-2/) | ## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/zh/4-Data-Science-Lifecycle/README.md b/translations/zh/4-Data-Science-Lifecycle/README.md index 0d10e8c9..d48a62bb 100644 --- a/translations/zh/4-Data-Science-Lifecycle/README.md +++ b/translations/zh/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 数据科学生命周期 -![communication](../../../translated_images/zh/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) +![communication](../../../translated_images/zh-CN/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > 图片由 Headway 提供，来自 Unsplash 在这些课程中，您将探索数据科学生命周期的一些方面，包括数据的分析和沟通。 diff --git a/translations/zh/5-Data-Science-In-Cloud/README.md b/translations/zh/5-Data-Science-In-Cloud/README.md index 1315d7c2..b1804266 100644 --- a/translations/zh/5-Data-Science-In-Cloud/README.md +++ b/translations/zh/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # 云中的数据科学 -![cloud-picture](../../../translated_images/zh/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) +![cloud-picture](../../../translated_images/zh-CN/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > 图片由 [Jelleke Vanooteghem](https://unsplash.com/@ilumire) 提供，来自 [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) 在处理大数据的数据科学时，云计算可以带来革命性的变化。在接下来的三节课中，我们将了解什么是云，以及为什么它非常有用。我们还将探索一个心力衰竭数据集，并构建一个模型来帮助评估某人发生心力衰竭的可能性。我们将利用云的强大功能，通过两种不同的方式来训练、部署和使用模型。一种方式是仅使用用户界面，以低代码/无代码的方式进行；另一种方式是使用 Azure Machine Learning 软件开发工具包 (Azure ML SDK)。 -![project-schema](../../../translated_images/zh/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) +![project-schema](../../../translated_images/zh-CN/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### 主题 diff --git a/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 76c8142c..16f4bc33 100644 --- a/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: * [医疗领域的数据科学](https://data-flair.training/blogs/data-science-in-healthcare/) - 强调应用包括医学影像（如 MRI、X光、CT扫描）、基因组学（DNA测序）、药物开发（风险评估、成功预测）、预测分析（患者护理和供应物流）、疾病追踪与预防等。 -![数据科学在现实世界中的应用](../../../../translated_images/zh/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) 图片来源：[Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![数据科学在现实世界中的应用](../../../../translated_images/zh-CN/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) 图片来源：[Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) 图中展示了其他领域和数据科学技术的应用案例。想探索更多应用？查看下面的[复习与自学](../../../../6-Data-Science-In-Wild/20-Real-World-Examples)部分。 diff --git a/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 48479648..adefad58 100644 --- a/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Explorer界面（如下图所示）允许你选择一个数据集（从提供的 2. 探索数据集[目录](https://planetarycomputer.microsoft.com/catalog)——了解每个数据集的用途。 3. 使用Explorer——选择一个感兴趣的数据集，选择一个相关的查询和渲染选项。 -![行星计算机Explorer](../../../../translated_images/zh/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) +![行星计算机Explorer](../../../../translated_images/zh-CN/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `你的任务：` 现在研究浏览器中渲染的可视化，并回答以下问题： diff --git a/translations/zh/CONTRIBUTING.md b/translations/zh/CONTRIBUTING.md index de93cdeb..5b8b19e5 100644 --- a/translations/zh/CONTRIBUTING.md +++ b/translations/zh/CONTRIBUTING.md @@ -311,7 +311,7 @@ def calculate_mean(data): import pandas as pd ``` ```` -- 为图片添加替代文本：`![Alt text](../../translated_images/zh/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` +- 为图片添加替代文本：`![Alt text](../../translated_images/zh-CN/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - 保持合理的行长度（约 80-100 个字符） ### Python diff --git a/translations/zh/README.md b/translations/zh/README.md index 2a1b4d7b..90b7b2e3 100644 --- a/translations/zh/README.md +++ b/translations/zh/README.md @@ -33,7 +33,7 @@ CO_OP_TRANSLATOR_METADATA: **🙏 特别感谢 🙏 我们的 [Microsoft 学生大使](https://studentambassadors.microsoft.com/) 作者、审稿人和内容贡献者，** 尤其是 Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![由 @sketchthedocs 绘制的手绘笔记 https://sketchthedocs.dev](../../../../translated_images/zh/00-Title.8af36cd35da1ac55.webp)| +|![由 @sketchthedocs 绘制的手绘笔记 https://sketchthedocs.dev](../../../../translated_images/zh-CN/00-Title.8af36cd35da1ac55.webp)| |:---:| | 面向初学者的数据科学 - _由 [@nitya](https://twitter.com/nitya) 绘制的手绘笔记_ | @@ -62,7 +62,7 @@ CO_OP_TRANSLATOR_METADATA: 我们正在进行 Discord AI 学习系列，了解详情并加入我们，[AI 学习系列](https://aka.ms/learnwithai/discord)，时间为 2025 年 9 月 18 日至 30 日。你将获得使用 GitHub Copilot 进行数据科学的技巧和窍门。 -![AI 学习系列](../../../../translated_images/zh/1.2b28cdc6205e26fe.webp) +![AI 学习系列](../../../../translated_images/zh-CN/1.2b28cdc6205e26fe.webp) # 你是学生吗？ @@ -142,7 +142,7 @@ CO_OP_TRANSLATOR_METADATA: ## 课程列表 -|![ Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/zh/00-Roadmap.4905d6567dff4753.webp)| +|![ Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/zh-CN/00-Roadmap.4905d6567dff4753.webp)| |:---:| | 初学者数据科学路线图 - _手绘笔记来自 [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/zh/sketchnotes/README.md b/translations/zh/sketchnotes/README.md index 1801b11f..aa0f6a22 100644 --- a/translations/zh/sketchnotes/README.md +++ b/translations/zh/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: Nitya Narasimhan，艺术家 -![路线图手绘笔记](../../../translated_images/zh/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) +![路线图手绘笔记](../../../translated_images/zh-CN/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) **免责声明**： 本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于重要信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。 \ No newline at end of file