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README.md
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@ -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)
<!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
[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?**

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translations/br/1-Introduction/01-defining-data-science/README.md
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@ -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.

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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(t<sub>1</sub>≤X<t<sub>2</sub>). Nesse caso, a distribuição de probabilidade é descrita por uma **função densidade de probabilidade** p(x), tal que
![P(t_1\le X<t_2)=\int_{t_1}^{t_2}p(x)dx](../../../../translated_images/br/probability-density.a8aad29f17a14afb519b407c7b6edeb9f3f9aa5f69c9e6d9445f604e5f8a2bf7.png)
![P(t_1\le X<t_2)=\int_{t_1}^{t_2}p(x)dx](../../../../translated_images/pt-BR/probability-density.a8aad29f17a14afb519b407c7b6edeb9f3f9aa5f69c9e6d9445f604e5f8a2bf7.png)
Um análogo contínuo da distribuição uniforme é chamado de **uniforme contínua**, que é definido em um intervalo finito. A probabilidade de o valor X estar em um intervalo de comprimento l é proporcional a l, e aumenta até 1.
@ -82,11 +82,11 @@ Quando analisamos dados do mundo real, eles frequentemente não são variáveis
Aqui está o box plot mostrando média, mediana e quartis para nossos dados:
![Box Plot de Peso](../../../../translated_images/br/weight-boxplot.1dbab1c03af26f8a008fff4e17680082c8ab147d6df646cbac440bbf8f5b9c42.png)
![Box Plot de Peso](../../../../translated_images/pt-BR/weight-boxplot.1dbab1c03af26f8a008fff4e17680082c8ab147d6df646cbac440bbf8f5b9c42.png)
Como nossos dados contêm informações sobre diferentes **funções** de jogadores, também podemos fazer o box plot por função - isso nos permitirá ter uma ideia de como os valores dos parâmetros diferem entre as funções. Desta vez, consideraremos a altura:
![Box plot por função](../../../../translated_images/br/boxplot_byrole.036b27a1c3f52d42f66fba2324ec5cde0a1bca6a01a619eeb0ce7cd054b2527b.png)
![Box plot por função](../../../../translated_images/pt-BR/boxplot_byrole.036b27a1c3f52d42f66fba2324ec5cde0a1bca6a01a619eeb0ce7cd054b2527b.png)
Este diagrama sugere que, em média, a altura dos jogadores de primeira base é maior que a altura dos jogadores de segunda base. Mais tarde nesta lição, aprenderemos como podemos testar essa hipótese de forma mais formal e como demonstrar que nossos dados são estatisticamente significativos para mostrar isso.
@ -94,7 +94,7 @@ Este diagrama sugere que, em média, a altura dos jogadores de primeira base é
Para ver qual é a distribuição de nossos dados, podemos plotar um gráfico chamado **histograma**. O eixo X conteria um número de diferentes intervalos de peso (os chamados **bins**), e o eixo vertical mostraria o número de vezes que nossa amostra de variável aleatória esteve dentro de um determinado intervalo.
![Histograma de dados do mundo real](../../../../translated_images/br/weight-histogram.bfd00caf7fc30b145b21e862dba7def41c75635d5280de25d840dd7f0b00545e.png)
![Histograma de dados do mundo real](../../../../translated_images/pt-BR/weight-histogram.bfd00caf7fc30b145b21e862dba7def41c75635d5280de25d840dd7f0b00545e.png)
A partir deste histograma, você pode ver que todos os valores estão centrados em torno de um certo peso médio, e quanto mais nos afastamos desse peso - menos pesos desse valor são encontrados. Ou seja, é muito improvável que o peso de um jogador de beisebol seja muito diferente do peso médio. A variância dos pesos mostra a extensão em que os pesos provavelmente diferem da média.
@ -111,7 +111,7 @@ samples = np.random.normal(mean,std,1000)
Se plotarmos o histograma das amostras geradas, veremos uma imagem muito semelhante à mostrada acima. E se aumentarmos o número de amostras e o número de bins, podemos gerar uma imagem de uma distribuição normal mais próxima do ideal:
![Distribuição Normal com média=0 e desvio padrão=1](../../../../translated_images/br/normal-histogram.dfae0d67c202137d552d0015fb87581eca263925e512404f3c12d8885315432e.png)
![Distribuição Normal com média=0 e desvio padrão=1](../../../../translated_images/pt-BR/normal-histogram.dfae0d67c202137d552d0015fb87581eca263925e512404f3c12d8885315432e.png)
*Distribuição Normal com média=0 e desvio padrão=1*
@ -233,7 +233,7 @@ array([[1. , 0.52959196],
No nosso caso, o valor 0.53 indica que há alguma correlação entre o peso e a altura de uma pessoa. Também podemos fazer o gráfico de dispersão de um valor contra o outro para ver a relação visualmente:
![Relação entre peso e altura](../../../../translated_images/br/weight-height-relationship.3f06bde4ca2aba9974182c4ef037ed602acd0fbbbbe2ca91cefd838a9e66bcf9.png)
![Relação entre peso e altura](../../../../translated_images/pt-BR/weight-height-relationship.3f06bde4ca2aba9974182c4ef037ed602acd0fbbbbe2ca91cefd838a9e66bcf9.png)
> Mais exemplos de correlação e covariância podem ser encontrados no [notebook complementar](notebook.ipynb).

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# 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 <a href="https://unsplash.com/@dawson2406?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Stephen Dawson</a> no <a href="https://unsplash.com/s/photos/data?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
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.

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@ -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

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# 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 <a href="https://unsplash.com/@swimstaralex?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Alexander Sinn</a> no <a href="https://unsplash.com/s/photos/data?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
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.

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@ -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.

16
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@ -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

20
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@ -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

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@ -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.

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@ -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.

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@ -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

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@ -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 <a href="https://unsplash.com/@jenna2980?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Jenna Lee</a> no <a href="https://unsplash.com/s/photos/bees-in-a-meadow?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
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.

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@ -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)

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-->
# 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 <a href="https://unsplash.com/@headwayio?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Headway</a> no <a href="https://unsplash.com/s/photos/communication?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
Nestes módulos, você explorará alguns aspectos do ciclo de vida da Ciência de Dados, incluindo análise e comunicação de dados.

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-->
# 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

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@ -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.

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@ -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:

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@ -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

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@ -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)_ |

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@ -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)
---

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# Defining Data Science
| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/01-Definitions.png) |

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# 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:

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# 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:

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# Introduction to Data Ethics
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/02-Ethics.png)|

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## Write A Data Ethics Case Study
## Instructions

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# Defining Data
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/03-DefiningData.png)|

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# Classifying Datasets
## Instructions

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# A Brief Introduction to Statistics and Probability
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/04-Statistics-Probability.png)|
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The relationship between the median and quartiles can be visualized using a **box plot**:
<img src="images/boxplot_explanation.png" alt="Box Plot Explanation" width="50%">
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We also calculate the **interquartile range** (IQR=Q3-Q1) and identify **outliers**—values outside the range [Q1-1.5*IQR, Q3+1.5*IQR].

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# 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).

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# Introduction to Data Science
![data in action](../../../1-Introduction/images/data.jpg)

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# Working with Data: Relational Databases
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/05-RelationalData.png)|

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# 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.

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# Working with Data: Non-Relational Data
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/06-NoSQL.png)|

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# Soda Profits
## Instructions

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# Working with Data: Python and the Pandas Library
| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/07-WorkWithPython.png) |

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# 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:

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# Working with Data: Data Preparation
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/08-DataPreparation.png)|

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# 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.

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# Working with Data
![data love](../../../2-Working-With-Data/images/data-love.jpg)

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# Visualizing Quantities
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# Lines, Scatters and Bars
## Instructions

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# Visualizing Distributions
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/10-Visualizing-Distributions.png)|

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# Apply your skills
## Instructions

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# Visualizing Proportions
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# Try it in Excel
## Instructions

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# Visualizing Relationships: All About Honey 🍯
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# Explore the Beehive
## Instructions

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# Making Meaningful Visualizations
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# Build your own custom vis
## Instructions

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# 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):

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# 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):

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# Visualizing Quantities
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/sketchnotes/09-Visualizing-Quantities.png)|
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# Lines, Scatters and Bars
## Instructions

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# Visualizing Distributions
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# Apply your skills
## Instructions

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# Visualizing Proportions
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# Visualizing Relationships: All About Honey 🍯
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# Creating Meaningful Visualizations
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# Visualizations
![a bee on a lavender flower](../../../3-Data-Visualization/images/bee.jpg)

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# Introduction to the Data Science Lifecycle
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# 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.

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# The Data Science Lifecycle: Analyzing
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/15-Analyzing.png)|

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# 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.

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# The Data Science Lifecycle: Communication
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/16-Communicating.png)|

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# Tell a story
## Instructions

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# The Data Science Lifecycle
![communication](../../../4-Data-Science-Lifecycle/images/communication.jpg)

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# Introduction to Data Science in the Cloud
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/17-DataScience-Cloud.png)|

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# Market Research
## Instructions

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# Data Science in the Cloud: The "Low code/No code" way
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/18-DataScience-Cloud.png)|

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# Low code/No code Data Science project on Azure ML
## Instructions

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# Data Science in the Cloud: The "Azure ML SDK" way
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/19-DataScience-Cloud.png)|

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# Data Science project using Azure ML SDK
## Instructions

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# Data Science in the Cloud
![cloud-picture](../../../5-Data-Science-In-Cloud/images/cloud-picture.jpg)

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# Data Science in the Real World
| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-RealWorld.png) |

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# Explore a Planetary Computer Dataset
## Instructions

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# Data Science in the Wild
Practical applications of data science across various industries.

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# AGENTS.md
## Project Overview

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# Microsoft Open Source Code of Conduct
This project follows the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).

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# 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.

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# Installation Guide
This guide will help you set up your environment to work with the Data Science for Beginners curriculum.

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# 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)
<!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
[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)_ |

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## 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/).

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# Support
## How to report issues and seek assistance

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# Troubleshooting Guide
This guide provides solutions to common issues you might encounter while working with the Data Science for Beginners curriculum.

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# Usage Guide
This guide provides examples and common workflows for using the Data Science for Beginners curriculum.

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- Introduction
- [Defining Data Science](../1-Introduction/01-defining-data-science/README.md)
- [Ethics of Data Science](../1-Introduction/02-ethics/README.md)

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# 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.

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## For Educators
Would you like to use this curriculum in your classroom? Go ahead!

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# Quizzes
These quizzes are the pre- and post-lecture quizzes for the data science curriculum at https://aka.ms/datascience-beginners

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Find all sketchnotes here!
## Credits

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# Definiendo Ciencia de Datos
| ![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/01-Definitions.png) |

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# 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:

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# 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:

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# Introducción a la Ética de los Datos
|![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/02-Ethics.png)|

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## Escribe un Estudio de Caso sobre Ética de Datos
## Instrucciones

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# Definiendo Datos
|![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/03-DefiningData.png)|

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# Clasificación de Conjuntos de Datos
## Instrucciones

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