From aad92567e8b4fcc308a699484614c7dad3e41773 Mon Sep 17 00:00:00 2001 From: Floor Drees Date: Sun, 31 Oct 2021 22:27:46 +0100 Subject: [PATCH 01/60] Add NL translation project README Loving this curriculum! --- translations/README.nl.md | 115 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 115 insertions(+) create mode 100644 translations/README.nl.md diff --git a/translations/README.nl.md b/translations/README.nl.md new file mode 100644 index 00000000..e5220328 --- /dev/null +++ b/translations/README.nl.md @@ -0,0 +1,115 @@ +# Data Science voor Beginners - Een curriculum + +[![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/) + + +Met groot genoegen bieden Azure Cloud Advocates bij Microsoft dit curriculum van 10 weken en 20 lessen aan over data science (datawetenschap). Elke les bevat quizzen voor en na de les, schriftelijke instructies om de les te voltooien, een oplossing en een opdracht. Onze projectmatige pedagogiek stelt je in staat om te leren tijdens het bouwen, een bewezen manier om nieuwe vaardigheden te laten 'plakken'. + + +**Met dank aan de 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). + +**🙏 Speciale dank 🙏 gaat uit naar onze [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) auteurs, proeflezers en "meedenkers",** 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, Yogendrasingh Pawar + +|![ Sketchnote door [(@sketchthedocs)](https://sketchthedocs.dev) ](./sketchnotes/00-Title.png)| +|:---:| +| Data Science voor Beginners - _Sketchnote door [@nitya](https://twitter.com/nitya)_ | + + +# Start + +> **Leerkrachten**: we hebben [suggesties bijgevoegd](for-teachers.md) over het gebruik van dit curriculum. We staan open voor uw feedback [in ons discussie forum](https://github.com/microsoft/Data-Science-For-Beginners/discussions)! + +> **Studenten, leerlingen**: "fork" om dit lesmateriaal te gebruiken de gehele folder, en werk op eigen kracht door de opdrachten. Start steeds met de quiz vooraf. Lees dan de lezing en volg de rest van de opdrachten. Probeer de projecten te voltooien zonder de oplossing een-op-een te kopiëren; maar weet dat de oplossing in de /solutions folder te vinden is. Overweeg een studie groep te vormen en samen door het lesmateriaal te gaan. Wil je nog meer leren? Ga dan naar [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-40229-cxa). + +## Het team achter Data Science voor Beginners + +[![Promo video](ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "Promo video") + +**Gif door** [Mohit Jaisal](https://www.linkedin.com/in/mohitjaisal) + +> 🎥 Klik op de afbeelding hierboven om een video over de makers van dit project te bekijken! + +## Pedagogie + +We hebben twee pedagogische uitgangspunten gekozen bij het bouwen van dit curriculum: we wilden ervoor zorgen dat het projectmatig is en dat het frequente quizzen bevat. Aan het einde van deze serie hebben studenten de basisprincipes van datawetenschap geleerd, waaronder ethische concepten, "data preparation", verschillende manieren van werken met gegevens, gegevensvisualisatie, gegevensanalyse, praktijkgevallen van data wetenschap en meer. + +Bovendien zet een laagdrempelige quiz voor een les de intentie van de student om een ​​onderwerp te leren, terwijl een tweede quiz na de les zorgt voor verdere retentie. Dit curriculum is ontworpen om flexibel en leuk te zijn en kan geheel of gedeeltelijk worden gevolgd. De projecten beginnen klein en worden steeds complexer tegen het einde van de cyclus van 10 weken. + +> Vind onze richtlijnen hierL [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), [Translation](TRANSLATIONS.md). Ook hier verwelkomen wij feedback. + +## Elke les omvat: + +- (Optioneel) sketchnote +- (Optioneel) video +- Een warmup quiz voor de les +- Uitgeschreven lezing +- Voor projectgebaseerde lessen: stapsgewijze handleidingen voor het bouwen van het project +- Kennischecks +- Een uitdaging +- Aanvullende lectuur +- Opdracht +- Quiz na de les + + +> **Een opmerking over de quizzen**: Alle quizzen zijn opgenomen [in deze app](https://red-water-0103e7a0f.azurestaticapps.net/), voor in totaal 40 quizzen van elk drie vragen. Ze zijn gekoppeld vanuit de lessen, maar de quiz-app kan lokaal worden uitgevoerd; volg de instructies in de `quiz-app` map. Ze worden stilaan gelokaliseerd. + +## Lessen + + +|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](./sketchnotes/00-Roadmap.png)| +|:---:| +| Data Science voor Beginners: Roadmap - _Sketchnote door [@nitya](https://twitter.com/nitya)_ | + + +| Les Nummer | Onderwerp | Lesgroepering | Leerdoelen | Link | Auteur | +| :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: | +| 01 | Datawetenschap definiëren | [Introductie](1-Introduction/README.md) | Leer de basisconcepten achter datawetenschap en hoe deze verband houdt met kunstmatige intelligentie, machine learning en big data. | [les](1-Introduction/01-defining-data-science/README.md) [video](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) | +| 02 | Ethiek | [Introductie](1-Introduction/README.md) | Data-ethiekconcepten, uitdagingen en kaders. | [lesson](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | +| 03 | Data definiëren | [Introductie](1-Introduction/README.md) | Hoe gegevens worden geclassificeerd en de gemeenschappelijke bronnen. | [les](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) | +| 04 | Inleiding tot statistiek en waarschijnlijkheid | [Introductie](1-Introduction/README.md) | De wiskundige techniek van waarschijnlijkheid en statistiek om gegevens te begrijpen. | [les](1-Introduction/04-stats-and-probability/README.md) [video](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) | +| 05 | Werken met relationele gegevens | [Werken met Data](2-Working-With-Data/README.md) | Inleiding tot relationele gegevens en de basisprincipes van het verkennen en analyseren van relationele gegevens met de Structured Query Language, ook bekend als SQL (uitgesproken als "see-quell"). | [les](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | | +| 06 | Werken met NoSQL Data | [Werken met Data](2-Working-With-Data/README.md) | Inleiding tot niet-relationele gegevens, de verschillende soorten en de basisprincipes van het verkennen en analyseren van documentdatabases. | [les](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)| +| 07 | Aan de slag met Python | [Werken met Data](2-Working-With-Data/README.md) |Basisprincipes van het gebruik van Python voor gegevensverkenning met bibliotheken zoals Panda's. Fundamenteel begrip van Python-programmering wordt aanbevolen. | [les](2-Working-With-Data/07-python/README.md) [video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) | +| 08 | Data Preparation | [Werken met Data](2-Working-With-Data/README.md) | Onderwerpen over gegevenstechnieken voor het opschonen en transformeren van gegevens om uitdagingen als ontbrekende, onnauwkeurige of onvolledige gegevens aan te pakken. | [les](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) | +| 09 | Hoeveelheden visualiseren | [Data Visualisatie](3-Data-Visualization/README.md) | Leer Matplotlib te gebruiken om vogelgegevens te visualiseren 🦆 | [les](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) | +| 10 | Distributies van gegevens visualiseren | [Data Visualisatie](3-Data-Visualization/README.md) | Visualiseren van waarnemingen en trends binnen een interval. | [les](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) | +| 11 | Verhoudingen visualiseren | [Data Visualisatie](3-Data-Visualization/README.md) | Het visualiseren van discrete en gegroepeerde percentages. | [les](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) | +| 12 | Relaties visualiseren | [Data Visualisatie](3-Data-Visualization/README.md) | Het visualiseren van verbanden en correlaties tussen gegevenssets en hun variabelen. | [les](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) | +| 13 | Betekenisvolle visualisaties | [Data Visualisatie](3-Data-Visualization/README.md) | Technieken en begeleiding om uw visualisaties waardevol te maken voor effectieve probleemoplossing en inzichten. | [les](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) | +| 14 | Inleiding tot de Data Science-levenscyclus | [Levenscyclus](4-Data-Science-Lifecycle/README.md) | Inleiding tot de data science-levenscyclus en de eerste stap van het verwerven en extraheren van gegevens. | [les](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) | +| 15 | Analyse | [Levenscyclus](4-Data-Science-Lifecycle/README.md) | Deze fase van de data science-levenscyclus richt zich op technieken om data te analyseren. | [les](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | | +| 16 | Communicatie | [Levenscyclus](4-Data-Science-Lifecycle/README.md) | Deze fase van de data science-levenscyclus richt zich op het presenteren van de inzichten uit de data op een manier die het voor besluitvormers gemakkelijker maakt om te begrijpen. | [les](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | | +| 17 | Data Science in de Cloud | [Levenscyclus](5-Data-Science-In-Cloud/README.md) | Deze lessenreeks introduceert datawetenschap in de cloud en de voordelen ervan. | [les](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) en [Maud](https://twitter.com/maudstweets) | +| 18 | Data Science in de Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Modellen trainen met behulp van low code-tools. |[les](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) en [Maud](https://twitter.com/maudstweets) | +| 19 | Data Science in de Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Modellen implementeren met Azure Machine Learning Studio. | [les](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) en [Maud](https://twitter.com/maudstweets) | +| 20 | Data Science in het Wild | [In het Wild](6-Data-Science-In-Wild/README.md) | Data science projecten in de echte wereld. | [les](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) | + +## Offline toegang + +Deze documentatie kan offline geconsumeerd worden door [Docsify](https://docsify.js.org/#/) te gebruiken. Fork deze foldeer, [installeer Docsify](https://docsify.js.org/#/quickstart) op uw computer en typ vervolgens in de hoofdmap van deze opslagplaats `docsify serve`. De website wordt bediend op poort 3000: `localhost:3000`. + +> Let op, notebooks worden niet weergegeven via Docsify, dus als je een notebook moet uitvoeren, doe dat dan apart in VS Code met een Python-kernel. + +## PDF + +Een PDF van alle lessen is [hier](https://microsoft.github.io/Data-Science-For-Beginners/pdf/readme.pdf) te vinden. + +## Hulp gewenst! + +Als je het hele curriculum of een deel ervan wilt vertalen, volg dan onze gids [Vertalingen](TRANSLATIONS.md). + +## Ander Curricula + +Ons team maakt andere curricula: +- [Machine Learning voor Beginners](https://aka.ms/ml-beginners) +- [IoT voor Beginners](https://aka.ms/iot-beginners) +- [Web Dev voor Beginners](https://aka.ms/webdev-beginners) From 7628aa644ff21f0b9b8b5b59e4cebcfe239c06fc Mon Sep 17 00:00:00 2001 From: Floor Drees Date: Sun, 31 Oct 2021 22:33:29 +0100 Subject: [PATCH 02/60] NL README for 1-Introductions --- 1-Introduction/translations/README.nl.md | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 1-Introduction/translations/README.nl.md diff --git a/1-Introduction/translations/README.nl.md b/1-Introduction/translations/README.nl.md new file mode 100644 index 00000000..04682436 --- /dev/null +++ b/1-Introduction/translations/README.nl.md @@ -0,0 +1,17 @@ +# Inleiding tot datawetenschap + +![data in actie](images/data.jpg) +> Beeld door Stephen Dawson op Unsplash + +In deze lessen ontdek je hoe Data Science wordt gedefinieerd en leer je over ethische overwegingen waarmee een datawetenschapper rekening moet houden. Je leert ook hoe gegevens worden gedefinieerd en leert over statistiek en waarschijnlijkheid, de academische kerndomeinen van Data Science. + +### Onderwerpen + +1. [Data Science definiëren](01-defining-data-science/README.md) +2. [Ethiek in Data Science](02-ethics/README.md) +3. [Data definiëren](03-defining-data/README.md) +4. [Inleiding tot statistiek en kansrekening](04-stats-and-probability/README.md) + +### Credits + +Dit lesmateriaal is met liefde ❤️ geschreven door [Nitya Narasimhan](https://twitter.com/nitya) en [Dmitry Soshnikov](https://twitter.com/shwars). From 00567ead7a2896e23d4479b23966725ecea40644 Mon Sep 17 00:00:00 2001 From: Floor Drees Date: Sun, 31 Oct 2021 23:17:53 +0100 Subject: [PATCH 03/60] NL README 2-Working-With-Data project --- 2-Working-With-Data/translations/README.nl.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 2-Working-With-Data/translations/README.nl.md diff --git a/2-Working-With-Data/translations/README.nl.md b/2-Working-With-Data/translations/README.nl.md new file mode 100644 index 00000000..3ce3b957 --- /dev/null +++ b/2-Working-With-Data/translations/README.nl.md @@ -0,0 +1,16 @@ +# Werken met gegevens + +![data love](images/data-love.jpg) +> Beeld door Alexander Sinn op Unsplash + +Leer over de manieren waarop gegevens kunnen worden beheerd, gemanipuleerd en gebruikt in applicaties. Leer meer over relationele en niet-relationele databases en hoe gegevens daarin kunnen worden opgeslagen. Lees over de basisprincipes van het werken met Python om gegevens te beheren, en ontdek enkele van de vele manieren waarop je met Python kunt werken om gegevens te beheren en te ontginnen. +### Onderwerpen + +1. [Relationele databases](05-relational-databases/README.md) +2. [Niet-relationale databases](06-non-relational/README.md) +3. [Aan de slag met Python](07-python/README.md) +4. [Data voorbereiden](08-data-preparation/README.md) + +### Credits + +Dit materiaal is met ❤️ geschreven door [Christopher Harrison](https://twitter.com/geektrainer), [Dmitry Soshnikov](https://twitter.com/shwars) en [Jasmine Greenaway](https://twitter.com/paladique) From 86e9fae19c36717d47218515df679bace65d99bc Mon Sep 17 00:00:00 2001 From: Floor Drees Date: Sun, 31 Oct 2021 23:30:37 +0100 Subject: [PATCH 04/60] NL README project 3-Data-Vizualisation --- .../translations/README.nl.md | 27 +++++++++++++++++++ 1 file changed, 27 insertions(+) create mode 100644 3-Data-Visualization/translations/README.nl.md diff --git a/3-Data-Visualization/translations/README.nl.md b/3-Data-Visualization/translations/README.nl.md new file mode 100644 index 00000000..eb04fff2 --- /dev/null +++ b/3-Data-Visualization/translations/README.nl.md @@ -0,0 +1,27 @@ +# Visualisaties + +![Een bij op lavendel](./images/bee.jpg) +> Beeld door Jenna Lee op Unsplash + +Het visualiseren van data is een van de belangrijkste taken van een data scientist. Afbeeldingen zeggen meer dan 1000 woorden, en een visualisatie kan helpen allerlei interessante delen van uw gegevens te identificeren, zoals pieken, uitbijters, groeperingen, tendensen en meer, die kunnen helpen het verhaal te begrijpen dat de data probeert te vertellen. + +In deze vijf lessen verkennen we gegevens uit de natuur en maken we interessante en mooie visualisaties met behulp van verschillende technieken. +### Onderwerpen + +1. [Hoeveelheden visualiseren](09-visualization-quantities/README.md) +1. [Distributie visualiseren](10-visualization-distributions/README.md) +1. [Proporties visualiseren](11-visualization-proportions/README.md) +1. [Relaties visualiseren](12-visualization-relationships/README.md) +1. [Betekenisvolle visualisaties maken](13-meaningful-visualizations/README.md) + +### Credits + +🌸 Deze lessen in visualisatie zijn geschreven door [Jen Looper](https://twitter.com/jenlooper) + +🍯 De US Honey Production data is gebruikt uit Jessica Li's project op [Kaggle](https://www.kaggle.com/jessicali9530/honey-production). De [data](https://usda.library.cornell.edu/concern/publications/rn301137d) is afgeleid van de [United States Department of Agriculture](https://www.nass.usda.gov/About_NASS/index.php). + +🍄 De gegevens voor paddenstoelen zijn ook afkomstig van [Kaggle](https://www.kaggle.com/hatterasdunton/mushroom-classification-updated-dataset), herzien door Hatteras Dunton. Deze dataset bevat beschrijvingen van hypothetische monsters die overeenkomen met 23 soorten kieuwen van paddenstoelen in de Agaricus- en Lepiota-familie. Paddestoel getekend uit The Audubon Society Field Guide to North American Mushrooms (1981). Deze dataset werd in 1987 geschonken aan UCI ML 27. + +🦆 Gegevens voor Minnesota Birds komen eveneens van [Kaggle](https://www.kaggle.com/hannahcollins/minnesota-birds) gescraped van [Wikipedia](https://en.wikipedia.org/wiki/List_of_birds_of_Minnesota) door Hannah Collins. + +Al deze datasets zijn gelicentieerd als [CC0: Creative Commons](https://creativecommons.org/publicdomain/zero/1.0/). From 54e955a7d0a4a09efccc005fa9a0ad91e978eef0 Mon Sep 17 00:00:00 2001 From: vahid baghi Date: Mon, 1 Nov 2021 11:16:53 +0330 Subject: [PATCH 05/60] Create README.fa.md adding persian translation --- 1-Introduction/translations/README.fa.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 1-Introduction/translations/README.fa.md diff --git a/1-Introduction/translations/README.fa.md b/1-Introduction/translations/README.fa.md new file mode 100644 index 00000000..46baec88 --- /dev/null +++ b/1-Introduction/translations/README.fa.md @@ -0,0 +1,21 @@ +
+ +# مقدمه‌ای بر علم داده + + +![data in action](images/data.jpg) +> تصویر از Stephen Dawson در Unsplash + +شما در این بخش با تعریف علم داده و ملاحظات اخلاقی که یک دانشمند علوم داده باید در نظر داشته باشد آشنا خواهید شد. همچنین با تعریف داده و کمی هم با آمار و احتمالات که پایه و اساس علم داده است آشنا خواهید شد. + +### سرفصل ها + +1. [تعریف علم داده](01-defining-data-science/README.md) +2. [اصول اخلاقی علم داده](02-ethics/README.md) +3. [تعریف داده](03-defining-data/README.md) +4. [مقدمه ای بر آمار و احتمال](04-stats-and-probability/README.md) + +### تهیه کنندگان + +این درس ها با ❤️ توسط [Nitya Narasimhan](https://twitter.com/nitya) و [Dmitry Soshnikov](https://twitter.com/shwars) تهیه شده است. +
From 7e1d6e3123f6ff36a4de138ab73a3815c267ecac Mon Sep 17 00:00:00 2001 From: vahid baghi Date: Fri, 5 Nov 2021 10:28:00 +0330 Subject: [PATCH 06/60] Update README.fa.md --- 1-Introduction/translations/README.fa.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/1-Introduction/translations/README.fa.md b/1-Introduction/translations/README.fa.md index 46baec88..45532340 100644 --- a/1-Introduction/translations/README.fa.md +++ b/1-Introduction/translations/README.fa.md @@ -3,17 +3,17 @@ # مقدمه‌ای بر علم داده -![data in action](images/data.jpg) +![data in action](../images/data.jpg) > تصویر از Stephen Dawson در Unsplash شما در این بخش با تعریف علم داده و ملاحظات اخلاقی که یک دانشمند علوم داده باید در نظر داشته باشد آشنا خواهید شد. همچنین با تعریف داده و کمی هم با آمار و احتمالات که پایه و اساس علم داده است آشنا خواهید شد. ### سرفصل ها -1. [تعریف علم داده](01-defining-data-science/README.md) -2. [اصول اخلاقی علم داده](02-ethics/README.md) -3. [تعریف داده](03-defining-data/README.md) -4. [مقدمه ای بر آمار و احتمال](04-stats-and-probability/README.md) +1. [تعریف علم داده](../01-defining-data-science/README.md) +2. [اصول اخلاقی علم داده](../02-ethics/README.md) +3. [تعریف داده](../03-defining-data/README.md) +4. [مقدمه ای بر آمار و احتمال](../04-stats-and-probability/README.md) ### تهیه کنندگان From f55ff50466f8f3ac256b86599a65df393f2698e1 Mon Sep 17 00:00:00 2001 From: Maud Date: Fri, 5 Nov 2021 16:22:14 +0100 Subject: [PATCH 07/60] Fixed typo --- 2-Working-With-Data/06-non-relational/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/2-Working-With-Data/06-non-relational/README.md b/2-Working-With-Data/06-non-relational/README.md index 808dc702..423b6023 100644 --- a/2-Working-With-Data/06-non-relational/README.md +++ b/2-Working-With-Data/06-non-relational/README.md @@ -49,7 +49,7 @@ NoSQL is an umbrella term for the different ways to store non-relational data an ![Graphical representation of a columnar data store showing a customer database with two column families named Identity and Contact Info](images/columnar-db.png) -[Columnar](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#columnar-data-stores) data stores organizes data into columns and rows like a relational data structure but each column is divided into groups called a column family, where the all the data under one column is related and can be retrieved and changed in one unit. +[Columnar](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#columnar-data-stores) data stores organizes data into columns and rows like a relational data structure but each column is divided into groups called a column family, where all the data under one column is related and can be retrieved and changed in one unit. ### Document Data Stores with the Azure Cosmos DB From 42d2a68bc0868ac2647990d2000752d3c27da0ac Mon Sep 17 00:00:00 2001 From: Eugene Chung Date: Fri, 5 Nov 2021 14:16:58 -0400 Subject: [PATCH 08/60] fixed paths for README.zh-cn.md --- 2-Working-With-Data/translations/README.zh-cn.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/2-Working-With-Data/translations/README.zh-cn.md b/2-Working-With-Data/translations/README.zh-cn.md index ebcb87f9..fb0e368b 100644 --- a/2-Working-With-Data/translations/README.zh-cn.md +++ b/2-Working-With-Data/translations/README.zh-cn.md @@ -7,10 +7,10 @@ ### 话题 -1. [关系数据库](05-relational-databases/README.md) -2. [非关系数据库](06-non-relational/README.md) -3. [使用Python](07-python/README.md) -4. [准备数据](08-data-preparation/README.md) +1. [关系数据库](../05-relational-databases/README.md) +2. [非关系数据库](../06-non-relational/README.md) +3. [使用Python](../07-python/README.md) +4. [准备数据](../08-data-preparation/README.md) ### 学分 From a097fda28b2c573955c7b5b0a96ccd5fd7645a66 Mon Sep 17 00:00:00 2001 From: nahyeong99 Date: Sat, 6 Nov 2021 04:11:52 +0900 Subject: [PATCH 09/60] README for translation --- 1-Introduction/translations/README.md | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 1-Introduction/translations/README.md diff --git a/1-Introduction/translations/README.md b/1-Introduction/translations/README.md new file mode 100644 index 00000000..b041eb5b --- /dev/null +++ b/1-Introduction/translations/README.md @@ -0,0 +1,17 @@ +# Introduction to Data Science + +![data in action](images/data.jpg) +> Photo by Stephen Dawson on Unsplash + +In these lessons, you will discover how Data Science is defined and learn about ethical considerations that must be considered by a data scientist. You will also learn how data is defined and learn a bit about statistics and probability, the core academic domains of Data Science. + +### Topics + +1. [Defining Data Science](01-defining-data-science/README.md) +2. [Data Science Ethics](02-ethics/README.md) +3. [Defining Data](03-defining-data/README.md) +4. [Introduction to Statistics and Probability](04-stats-and-probability/README.md) + +### Credits + +These lessons were written with ❤️ by [Nitya Narasimhan](https://twitter.com/nitya) and [Dmitry Soshnikov](https://twitter.com/shwars). From e25cf768f2af71d828e2117ed85cb5d8f6ce0480 Mon Sep 17 00:00:00 2001 From: nahyeongKim <74201593+nahyeong99@users.noreply.github.com> Date: Sat, 6 Nov 2021 04:42:10 +0900 Subject: [PATCH 10/60] Update README.md --- 1-Introduction/translations/README.md | 21 +++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/1-Introduction/translations/README.md b/1-Introduction/translations/README.md index b041eb5b..232dcd3f 100644 --- a/1-Introduction/translations/README.md +++ b/1-Introduction/translations/README.md @@ -1,17 +1,18 @@ -# Introduction to Data Science +# 데이터 과학의 입문 ![data in action](images/data.jpg) -> Photo by Stephen Dawson on Unsplash +> 이미지 출처: Stephen Dawson on Unsplash -In these lessons, you will discover how Data Science is defined and learn about ethical considerations that must be considered by a data scientist. You will also learn how data is defined and learn a bit about statistics and probability, the core academic domains of Data Science. +이 레슨에서, 당신은 어떻게 데이터 과학이 정의되었는지 발견하고 데이터 과학자에게 있어서 필히 고려해야만 하는 윤리적 사항들에 대하여 배울 것입니다. 당신은 또한 데이터가 어떻게 정의되었는지와, 데이터 과학 학습 영역에서의 중심인 약간의 통계와 확률에 대하여 배울 것입니다. -### Topics -1. [Defining Data Science](01-defining-data-science/README.md) -2. [Data Science Ethics](02-ethics/README.md) -3. [Defining Data](03-defining-data/README.md) -4. [Introduction to Statistics and Probability](04-stats-and-probability/README.md) +### 토픽 -### Credits +1. [데이터 과학 정의하기](01-defining-data-science/README.md) +2. [데이터 과학에서의 윤리](02-ethics/README.md) +3. [데이터 정의하기](03-defining-data/README.md) +4. [통계와 확률에 대한 소개](04-stats-and-probability/README.md) -These lessons were written with ❤️ by [Nitya Narasimhan](https://twitter.com/nitya) and [Dmitry Soshnikov](https://twitter.com/shwars). +### 출처 + +이 강의들은 [Nitya Narasimhan](https://twitter.com/nitya) 과 [Dmitry Soshnikov](https://twitter.com/shwars)에 의해 쓰여졌음❤️ From 1be226e0e89b17fcff500f89b2b84ca9f71f888b Mon Sep 17 00:00:00 2001 From: nahyeongKim <74201593+nahyeong99@users.noreply.github.com> Date: Sat, 6 Nov 2021 04:42:23 +0900 Subject: [PATCH 11/60] Rename README.md to README.ko.md --- 1-Introduction/translations/{README.md => README.ko.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename 1-Introduction/translations/{README.md => README.ko.md} (100%) diff --git a/1-Introduction/translations/README.md b/1-Introduction/translations/README.ko.md similarity index 100% rename from 1-Introduction/translations/README.md rename to 1-Introduction/translations/README.ko.md From 2b47221e8567cb66c41726694ff64d6ca77074e9 Mon Sep 17 00:00:00 2001 From: Hyejeong443 <82637076+Hyejeong443@users.noreply.github.com> Date: Sat, 6 Nov 2021 13:05:18 +0900 Subject: [PATCH 12/60] Update assignment.md --- .../14-Introduction/assignment.md | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/4-Data-Science-Lifecycle/14-Introduction/assignment.md b/4-Data-Science-Lifecycle/14-Introduction/assignment.md index e0ff4244..0a6db90f 100644 --- a/4-Data-Science-Lifecycle/14-Introduction/assignment.md +++ b/4-Data-Science-Lifecycle/14-Introduction/assignment.md @@ -1,23 +1,23 @@ -# Assessing a Dataset +# 데이터셋 평가 -A client has approached your team for help in investigating a taxi customer's seasonal spending habits in New York City. +한 고객이 뉴욕에서 택시 고객의 계절별 소비 습관을 조사하는 데 도움을 청하기 위해 귀하의 팀에 연락했습니다. -They want to know: **Do yellow taxi passengers in New York City tip drivers more in the winter or summer?** +그들은 알고 싶어한다: **뉴욕의 노란 택시 승객들은 겨울이나 여름에 기사들에게 팁을 더 많이 주는가?** -Your team is in the [Capturing](Readme.md#Capturing) stage of the Data Science Lifecycle and you are in charge of handling the the dataset. You have been provided a notebook and [data](../../data/taxi.csv) to explore. +귀하의 팀은 데이터과학 라이프사이클 [캡처링](Readme.md#Capturing) 단계에 있으며, 귀하는 데이터 셋을 처리하는 임무를 맡고 있습니다. 노트북과 가공할 [데이터](../../data/taxi.csv)를 제공받으셨습니다. -In this directory is a [notebook](notebook.ipynb) that uses Python to load yellow taxi trip data from the [NYC Taxi & Limousine Commission](https://docs.microsoft.com/en-us/azure/open-datasets/dataset-taxi-yellow?tabs=azureml-opendatasets). -You can also open the taxi data file in text editor or spreadsheet software like Excel. +이 디렉토리에서는 파이썬을 사용하여 [NYC택시 & 리무진 위원회](https://docs.microsoft.com/en-us/azure/open-datasets/dataset-taxi-yellow?tabs=azureml-opendatasets)로부터 노란색 택시 트립 데이터를 로드하는 [노트북](notebook.ipynb)이 있습니다. +엑셀과 같은 텍스트 편집기나 스프레드시트 소프트웨어에서 택시 데이터 파일을 열 수도 있습니다. -## Instructions +## 지시사항 -- Assess whether or not the data in this dataset can help answer the question. -- Explore the [NYC Open Data catalog](https://data.cityofnewyork.us/browse?sortBy=most_accessed&utf8=%E2%9C%93). Identify an additional dataset that could potentially be helpful in answering the client's question. -- Write 3 questions that you would ask the client for more clarification and better understanding of the problem. +- 이 데이터 세트의 데이터가 질문에 대답하는 데 도움이 될 수 있는지 여부를 평가합니다. +- [NYC Open Data 카탈로그](https://data.cityofnewyork.us/browse?sortBy=most_accessed&utf8=%E2%9C%93)를 살펴보십시오. 고객의 질문에 대답하는 데 잠재적으로 도움이 될 수 있는 추가 데이터 세트를 식별합니다. +- 고객에게 문제에 대한 보다 명확한 설명과 이해를 위해 물어볼 질문 3개를 작성합니다. -Refer to the [dataset's dictionary](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf) and [user guide](https://www1.nyc.gov/assets/tlc/downloads/pdf/trip_record_user_guide.pdf) for more information about the data. +데이터에 대한 자세한 내용은 [정보 사전](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf) 및 [사용자 가이드](https://www1.nyc.gov/assets/tlc/downloads/pdf/trip_record_user_guide.pdf)을 참조하십시오. ## Rubric -Exemplary | Adequate | Needs Improvement +모범 | 충분 | 개선 필요 --- | --- | -- | From 9a02c016f69a9ab3cbd151d9e57c396904a7487c Mon Sep 17 00:00:00 2001 From: Hyejeong443 <82637076+Hyejeong443@users.noreply.github.com> Date: Sat, 6 Nov 2021 13:06:01 +0900 Subject: [PATCH 13/60] Update assignment.md --- 4-Data-Science-Lifecycle/14-Introduction/assignment.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/4-Data-Science-Lifecycle/14-Introduction/assignment.md b/4-Data-Science-Lifecycle/14-Introduction/assignment.md index 0a6db90f..df425137 100644 --- a/4-Data-Science-Lifecycle/14-Introduction/assignment.md +++ b/4-Data-Science-Lifecycle/14-Introduction/assignment.md @@ -17,7 +17,7 @@ 데이터에 대한 자세한 내용은 [정보 사전](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf) 및 [사용자 가이드](https://www1.nyc.gov/assets/tlc/downloads/pdf/trip_record_user_guide.pdf)을 참조하십시오. -## Rubric +## 표제 모범 | 충분 | 개선 필요 --- | --- | -- | From c1802f663259e9cfaa9bfc88f1164bf6940b1ea2 Mon Sep 17 00:00:00 2001 From: ludovicobesana Date: Sat, 6 Nov 2021 19:14:38 +0100 Subject: [PATCH 14/60] fix: Update favicon img --- images/favicon.png | Bin 6188 -> 4556 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/images/favicon.png b/images/favicon.png index 9e5b4f6ac5ab75ef5467dcdb59f9db3cc7a0f405..7e33f5aedba624bb20001cc044b0fdd9a8587a46 100644 GIT binary patch delta 4214 zcmZ`+X*|?l)E{FkL)MY9Z)GC8k+DX|n#q!^Bm2%+vXuNr$UbD>$5M@oP?kYzLbkC* zb}|T+QI^sa@y!48yn0?d_szZM-upfG-0wZ-p3kXM8kWlT20;I{KL(xu4Ok*fuNkqd zvoo^FfF1{7bU+}k2vb9S+vxf2@}Lao8HavgOyl>b;<<8{AM*iPvrXm1CXdcg;hYC4 z&El-4l~2L7tP+W~_qiXNf+T1pbs8=a%Q}&#ZbdO*=M}eldX4QN+haYm)Y3NQvPU@A z)!E(e!05bI!|zYwG|YGE=+Y!w5?H#ukLj0+S;Dn%Ud=smyOsDtM(><}OMiwL^{oe4FEGwv!Hq@EGdXu_^gtGE zd7+vzoqYmtnB~;-nzEg}m`#SnS-EfVB0-B#K$rz5efdXlZBb$DTXeg%b(J|uixfaA zB`uI{FryxDQJHY(ytJ7YnPa(dSg4&)s#i;uGs_FmX8Z}`C(!X3B8YEJTtG-dk1?Di zL-8Q}OI`VWj=3S~E4ooHMQTlWCy26)Jpqq`^Em`{*<&xrto37aiG9jR0epUBUks4w z+{d$PZ9RGu%Os9QtxOwWy9JIJC@_+7sJvfT`B;Eak?r0BsSw++>gZZOwh1BWQ+O!- z@pmyg)?Aet{BYDV)0>LC{ZgdlgKrDcSeE!@mNz}mL^;>eu)ncynRKC4YE79|JRW?k z5OWpL8&-hX`_oDKy>e%fBten~NQw-uJx%y$Sg_q~;<ok|@VjG}D;KOHb?NE1g zOF_W2Qi%JD`J(dywf2+duI>~U3LP8Y_C><9i**NMd2kbpCnW`nJcPM5L<{eOzfTL` zzF_YSd0gewu%fYE(SfMs)p>pDD@!*(X9bkuWM2neVUtFaOK)ILK*tv(JT(5CXzyIu zL0C#^4PlwR^EWuxf$$;no?H1!<-x2>36ux?(TtVPUB=1aouEQ-ifU-)%~jcut7-}h zDLF2$+T}ne*?IdnrZM9IJeBc>*ky>Qx$NABo#gy6u&$qyu}Nh`g}>&7ZbNK zmV*a)i0J_5L=kKyZ*(s@do%eTl=DKCp562E){I{$M!C77o7J+eoawL679hovmr9~} zXvSp;aV?Wu9Z8;1ptraHhunJgSCZP4Oep@m`lzo+-vlYYEQdY7y<W)?Ov=1{hgT^%!0K|Aki;doIPNvgx4JP5KpQGa&Fy|H)ozRuXC7%e0{?}N6 z56((F?Azy>^%>z255_!wXkUjBcc;YrT*}=-yCU&>$ZE!C(_zzww)5s1P->5nNyrCE zo|Ac$FpXmoTmAILwbWHOY)$OLyN>lIt`;f4YU9D&W5k)9#ug{%O^LF-m)gapD=_^E z5tZ3ba~xo*bxDsVr9&-tCb9fZx154exFD_a7@rBTV8D03|9h=NOkA7SV$U-;@=+f= z26Z)Y!Il*KSR+L8MN#ecKS5$Yd5``y#{aZoQYvv$beG2-#4H<@1w{@2;v3b*B?69J zixiXBXW%-up&gywKl9{Id+Xi$Q_$vlhXc-+B5VnSu!I$ zx7V@TI@o8ng1ZsIa4`*g(P0)D%l3UPX5p7`(FB*Ym&BD`^?0&qaP=yhVz`wMY$ck- zlq)}2g>ZL zRVRE0HzuJ4F9F-@dY!oxY|aDFn_?0Tp4^+STh_?ln61ZnkZ3;cG$!u)w> zOwAhCMdyxonUOkd&HSd_I$;OTA110&&QP!U=_!lp@9nm?OyH#X%b6u2 zLqlCnMyD>Ok2&7LlG)TzbDvoXl<9$gI^a%nwdCyht%ezKNAqNentHsh=B72jIs>HE z(TBW#5aA7Z{TvgpR9b*dp5BXet{FF<-Adu`Zpx51)+x9n^YM+$&+^XfVj(<)fxmvG zo(Np6ce($JRLZ8V(>fmA5As9ii5sHU8;=p|d_r?q^IRLYYYejhOFK6^-pLO+#vhRjxo6W22cHL_>s z;hu7qf0u2E^_Ln}3&I*IeL0p{$<7F?4q)Je<8Z#@#%hb`8}WDo-H&!~BTLyj`qA6*fxs+{7tskqBD;FYTs{%(6o2rmYx|cYOmiz)Frl( zsxt@ER}~ZuE?rXu)h1qNdl(3uGob|isSqcr1<>4oX`a|#wD#xn;E#XHT*1<<$snP? z*CnKWxpAJo(YZyLo?zUzYiu@nfk2I3euHy`6_7y|lfH$QI{FwPE%Yh#Bct1??Az?G zP6bD;Z(_8rluEl7V4k;re=l$2tpBc0&VG~YK<4s4ZTipFoSR(|Mxp^@V#+m-eq zUP6r-f%{lxhd^A?kwCz>S1*|?;}$SoVPoLJpx$b=#V6VavWjZZR$)y%FisE-I*-4; zOx*RiU6~u=JrT_R!ksaLRb&M2OM!h|MegaeTT%*a8tgAJD+n5QORT>laPdx0!dlZ% z_G)qYo3iFd;r7)N8oQB#G)4;GJJ8j_dkaH7yV#Lm+*c}5urKZSuqMug$aAkdZFfy} z%#JNqP0spT*&7X(BjsRV%-&|6U=!%j!!Zh8bo}t-@i&QJv6*URVQOxo&>$Qkc=Bo; ztQetwe43&d8hR4DH4^;p-aVmX$fy`%on6or`6xCrYY3i^Dm;~W;%K}6Djz%+rG59 zm2Ge^%BDiiFB?+7eEH+&VC_rrUcgNV7e)$BmIFaywtNTvDZc%m% zz0b*LSVMgj_y-uHNO#=fe~H&A+kcc@^A}kq)?fAv&N@3z)JRU+8@QwQyyu#XmX1@L z$iLv~YwIxnpp&qV8!$nexj{kYyDf%Cqmp^XFsRy~Wk~Wu+pgO^f-%PooX?B+OT$H9HRd1jeWdW_C}j~*TO*Z7no#V6&QFg*lHhM0_+OzYB5o#CQO=v zqLSsbOzIkH8}?Te{?VOk&2+aVvCBHb3MC^PL>9}T<7-%CP6d@=-v@n5-yRSN#)lqv zWO5~F=wqUFt9H*lmhi9-jZ-}J?ZQJ_T{oTf8mr2z#nA_9Z%-#^lf9yK-BP}&wyAQ@ z`Sn(E0h0n1sIy=@$7AlbBlOumzpFj_P)5TgT0{Sh3L*EhW%eFTOMl^6 zJ0FB3odxSN-FH%!1&b%4kRg(6rwS6K*MOV-29biwW?=Ehs0-)T5;Jp@f;rn@SFeE{ z{e&{TQs(WuvG3F*0MRqUH==o`dA2hw=&}PuZz%Li6{`O=XR=lp{UYAHAjSizlRkZI z4x)Ei*DbSR+?cG`m`dj|R2($%H&MYaJruWp*Ugy~+pJq|yRS|a1n=F8NWV0OP_yLbw2GM3-iWI@c5oc@yQF!#U>nAkA7eYob&KB$Td zofJ9U63QAJZgP_s;jT78rbWFBI=Wp0hXx7(UwrdwjP3{l4}<0ekazwt@BaXTLOlJvV?_Ud1j7@AWo|G36q|+$5~-$UG$O~FTXV)j=Rf6& z?jB0&2zM`}x0eUPT?3)6>_MOLP*YX)RQLAOKq3|VJVF#O!9I1zHRJSSL8eBQhA*z% Gy8l0+P~2Yt delta 5859 zcmZu#cQD*hwEpc{U9fr(WFv^Wy2N6!%j&%(L|a7fgb+VHt4oAPQKCfDAWHP;(TQFX z5;Z~e-kvw_{qxG)IdkuvneY3~%$+&knbUWx7@1@bUIJGqt1&+r0043i+*Z4URfU2WY`ko$p03^6`bGX5wx7+SC%}~4 z7cTtuOAk~N@UjI}g5uxrRdDyF3U=q>hJ(;tiU3Tlk7`-&&@VGtfg~6G97ft6f;8AI za-y<#n+psakUsP|{6jjFqngMe_hfH%_n9hk`g31l!h38g?Dn#ZZ&eHw5r526pv!{Ms5 zaNJ6^fSsbQ{oXipK4*jbPHpSTX5yFEBselyQW!!Xmd7XocSHckRxtO6hZ>>z4Usx^ zZN4HQ1T;Vv@x_M(6_7??@CXfL6hu)|&=S$kw*Mm1`B}c{Ebg_)n?8B;;xMGG6Kog$ zDYvCa%8jjCt^E`T;R49vFxR4F=*zlRH5jeu>fh8#I^srKH>NPeh#waA&%Prd@5RN? zE!r|9my(r5=j%Q}rtfOTbhHG+897nkVd95}P&8oA1Hk?m0+4dDaPsscdI-avn@M~K zy~ca*T{^-FI@rJDmWSt9+id0 zY*qd~*r;o>BgXv*qCX&TRlEH-$O7k8y&liyzrahEzPN6(Ma-ZmKn$YETE7=)Fk8RJ z4h}MID#M6>qn)KpT$Y7%&R)sVroV0zH>)A3);~@YdeyA^bX!(|myWn4YQM;7XenE* z6aRf3*{^c9Z0C0p+UwaK(+Ur!Jmr4#vH4nOh;_OE|mO#Q%Q(Y<&7+2!s5cRcXB3 zbs6uQQ%^m^*|23Kf0UlHC^&hS5RaLJPd-P|7vDyBqcv<)qK`i!Vni?q0)Ziv0Y|3W zV^D6I+3YzoaCWY+^e4?22CpT7Catu!+t?II3IOx|>CV;Q#b*!e(ZNFnvF#z{t`|8u zA8*K3e}E?GZ!y4=Uv$w)4_^4%2mU%{`*l|<_tga zMPto;L2GY0tx`>0JlZ6s&yGdVuo4FHtamu}QR;Q@8(qP9$JCuK0*ee`TeY?j4Af`j zLPJ{FSCCrdt(;~L>TzlL{HeT6Im_fUoB8MMxwa_-&S%-d087wd_=Gu7!hpg9hz`W6 zPh2#avF|J;36KCd)i2nk*GOnk&@Phb$_6)RXB@NMaRNd_NoJ~DmE|*^ArI^A7izL? zeu0T$<`$qdrN(;r`b<@3UBJVV{iRuA8 zDhGxh5j9`Y0qzq2{lW=Q*dVq_yeG5)031?e51#wV?Rc(*fQm1yPkr7((ctlT7B)6z zL9&;ztu?hzHU8ECE+AKs-VLjS>9pJE0r0 zsHG5g(*7J5@N)uf2n+G5jeF8aji-`wosPIFGwahQn)TU_uA`S_zJC9#;XR~a?U~c$ z8FV@waSi$sr`lpi#zhs4PCtxLq=1%H86Q26heSTg2vPg+K72AZcTWI4b1g+ZZ#Q7) z80EJ<8T$VHO+MXBXGySstdOQMhB()6eYLb>4(%X>V0)4EV|8;673vkj=YtZD;6m7x z(66Mz51ODieE(?jhnLfUdvT!HvM0g@-ZDGOUR<>yaw`mj2;G>e!Tb0ygl_Uqs%&by zOur*&_xlacGLKX+RoTa;VaY${Y>rgRY=;5q!@$0E#4E*&hh?UQY?K+q6_g7mH54!DvI{#38RKqelOER{MzAPNqKF_2v zS*GtUQ{aqf9?WkcjRN;M4ua3gcrI-fzPPColdt+{e`|;AkST@m!5u=XxZQ`Lx5um6 z%Pj(0UI~N@Ea|c-7>h=?UmA>GFo=^zDnZ)X+K|hwy)5{o!6nTC^QH+Z*R!Inb#q^% zVwx;#p`H@lIJt>@S%<=@dkq(Nh@ur)Ibg)=6-R6YkA@*J;N&{|H}xhZH+U&MC23*Q zGpF4X$QDASh^lN1N0jmCQuGX#iR(FN*k}bs<~313oEEyQzB}^an>wszAj@n;WBi3< z{9d)}w!?Te%<9^jvdfd@0sFil`tpXM66oauvwSF%@{*i?2jK^{P#BnYa)#mmd+;2j8l8)M_F^rP<;h0AyL-{g;tAhKuL`f^8Wb6*Z; zpBXcxu)Y`YpSB2u0AKw*{R`He&D~MA$A7X!-CSN?9-5eVtSApx6nl-im(J?M5c_z_ zem8P)lEq0i(c>xjFPWA#EiF0yHl~T^4n-P}@DKU&T7o+~}-=k(n$r!|Eld3JwR9lb&u*09%w0Z8Za2_H};##~Cay)Pm z6K%ED)*LrmkP**HeA&Q*e`d>8+{EI`pD+#ZZZ4OpO^qnfEa!#ix?)-2U+qkSKRBluhG>EqS0Z;@w&x3Z@e*7s>}Ob7@- zRJ$`71@%~5WIu?{>>NTrv7Rp!2Uqt^Xka?ZVjR|=NgLurdVc7X%5Et1FqXUD@wCAsvCChj1Y705ALH@V?^76jrA?!zg1_RUX2WC9G zxCrLUvno;EUZ1-Cqr=MzDEN~2xY1YECioFe31fs-3wH>Dz)Cfmo;7K6qCNHNklJ|5 zw6Wm0>*t|mX-bJmw~>}saYAC729IJV3Ty4G|1Qv=z)FtzwJF0P$8Y_TreKq$?MV!Ro#Z1#*S|XQ4?R}+8h6Ti(B@jxrE+EvESX+w z7X0)n(NK9HkUKHRcE6+6=@eEANwei2N6DS@vPW?u@;pYgq^%b^<7wfV>~b*$3tjd4 zI7pIO=k~Eq19RZNAo|=k{{~=yLc5wprzVXMe^ZCC<-%aH1N;mn!@?JGfNOX_l%ML8Xy(@J2tjM6;#lIJe@P`BA>oZ!=WeeH{h|(8HHat< z%Z;Yx{R${SJv4uXgEfz~Ii)|0CWBj#IJ?|0cJje5Fuuk;IsZRYFIS#EGwW|_4VpPS zL$St_=oc`&_(S!gKtjF4#$+}Luad(E0b~`sp$3RFo@_;;>qZe050|5P09Jxyt6xYB zk!i)TVu)Bo$s1dk8nI=Gg|Z`dWW*N{I+T((FMks1JD`ywz|4Bt`#3bpN47bvMz1w2W=U>pW zQ}gK7ap2wlG>;VMsgsXl-9L@~(o64B^WEzp{jT6-1aEZYqyb zm{{nRJbDJ!xCUJg=OkZ(o3DbI13At%cDtHR^PcHF5@SP2r-?c}QtEw3X*2tgEP&Ym z6;cXDFxB<`T1?L#!=fV{ks%Nzv*jeNH@g_=zt((`ZX|vYLkvv6CufA6>y;l#3&;G6 z?Xa1wa%QfW5-g7QIQk0dOTSy+$_Af5O+;i~-gW{dd*g3s6y`S_sWBk*@t$u7kH6eh z`7*}4E)^q1-%d(_QHL>^htBVvvD1QN7Is8CW2_MH9jZ$F00?s za~}PjkAT(TVZFGxxJn|gWZtdmU$;!=c)6f)#~JF};O#dePoyH@Sk zdAfOnpn~7OS@OYM-6F%SzfDgaq*!~K%k=G7HrHO_f_M%`Xc`QIPt*!B(oDtiB_t28 zo9&9-Pt*J$ST*gsKC8XLe&v_Hw`D3tKN^AjAB~FA3$*ydjK#Ma8W~`j`cvzi(#r!) zAL103NJ%HAEgt3z#O~R!MIk{euwR3xSzGFf4og(IO*x^dsVP!kk;6M*$CAC*QAg7` z6se=GrC7k3G(Ol$lVMaaY7HLVNLgKlTOBYgTRsUcajoL_!G;_Yo&L%j;TjB#dH|N- zTMtsNxU}9%wH!XRJvpJ(;MOfNwvG!GC4ui&@}1l9?V|#gkwNbIT?^hnzt%++O^AZ4 zv`AmCrO4Bp`OdwDMkwlL(5iLJN$2ad+11e1vm{1BLPFbaW2zt#Va$>(@+y4gR7ONRXP|dM%*tSYvYcowI4)KD!KdWe^Pf+7oj!3?_Utw(s`r~Y>j^U5L~tRf zTz|&7;&Z;yP;sSqXNkQ1u}t24w=)dCdA40u-S%dabhDf*uW?lMqB#y(KM!1O6p zid;#8@?UCvyIf5ysS+BGDE-H{ZG?aw2Y>Yc%{bwLB3@*9dt@;kY_0MA;w$o3$Uv|>&xWZ z9P~0scjQ>`Sw5$Y6u*Zuco(k|Rzjaz((YPAiEh9UYu(0U=8YA1?bn1B#8+JKZY&f^15Y zorxZ)XtcVC^iB=`)l@j45VDx=UJyx7Z`M&;??L`p6*F1ZL=(Oyrd!zPu(uPua3JI0LSkjA2y+3=GFZkZjtlXP3eQb1?K<#z+qbX|S zjqE9yPlw$E@x;Y1!^Yj$DSJ9fQg%tQdq-=%x@$B{cul+qve&(=eM?cBrxwD8Zq#iH zzp@!~ozjPx?I8eyk4?qFrTTk3>ENt<}oPJD?ed-dSWU}vlO7LSG7~`@`K4yBs;K? z_WN+mzIRvsu4a3OUeAHIe6c_vkIb?2{79vw(L27qyIxwqxJs zDv5gh8H@!ynXxC?W|ZNtX63z}Z*F$F$$7m$F&Ut7(C1E=2{d5yir&EQf7D(4-75k< zq@+LiziZR~$_V_*gai{E(Vqseq+V@;Z~I_PeQd3L?C#;c?5-|A3@IimB#IOg7c~?W zzb7GfPgFtxiM)qIiU=LA{SSbvyRD;r!2biF{BMMXhoVWcE$tYEl#H~rxI~^}h9rtq5p From 57297a4b28f3228cc0191060d2e59f4b97d0d8b4 Mon Sep 17 00:00:00 2001 From: chaeyoon20 Date: Sun, 7 Nov 2021 04:53:25 +0900 Subject: [PATCH 15/60] add README.ko.md and assignment.ko.md --- .../15-analyzing/translations/README.ko.md | 46 +++++++++++++++++++ .../translations/assignment.ko.md | 22 +++++++++ 2 files changed, 68 insertions(+) create mode 100644 4-Data-Science-Lifecycle/15-analyzing/translations/README.ko.md create mode 100644 4-Data-Science-Lifecycle/15-analyzing/translations/assignment.ko.md diff --git a/4-Data-Science-Lifecycle/15-analyzing/translations/README.ko.md b/4-Data-Science-Lifecycle/15-analyzing/translations/README.ko.md new file mode 100644 index 00000000..7ae57b00 --- /dev/null +++ b/4-Data-Science-Lifecycle/15-analyzing/translations/README.ko.md @@ -0,0 +1,46 @@ +# 데이터 과학의 라이프 사이클: 분석하기 + +|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/15-Analyzing.png)| +|:---:| +| 데이터 과학의 라이프 사이클: 분석하기 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | + +## 강의 전 퀴즈 + +## [강의 전 퀴즈](https://red-water-0103e7a0f.azurestaticapps.net/quiz/28) + +데이터의 라이프사이클을 분석하면 데이터가 제안된 질문에 답하거나 특정 문제를 해결할 수 있음을 확인할 수 있습니다. 또한 이 단계는 모델이 이러한 질문과 문제를 올바르게 해결하는지 확인하는 데 초점을 맞출 수 있습니다. 이 과정에서는 데이터 내의 특징과 관계를 정의하는 기술이며 모델링을 위한 데이터를 준비하는 데 사용할 수 있는 탐색 데이터 분석(Exploratory Data Analysis) 또는 EDA에 초점을 맞춥니다. + + [Kaggle](https://www.kaggle.com/balaka18/email-spam-classification-dataset-csv/version/1)의 예제 데이터셋을 사용하여 파이썬 및 Pandas 라이브러리에 어떻게 적용할 수 있는지 보여드리겠습니다. 이 데이터셋에는 이메일에서 발견되는 몇 가지 일반적인 단어가 포함되어 있으며 이러한 이메일의 출처는 익명입니다. 이 디렉터리에 있는 [노트북](notebook.ipynb)을 사용하여 계속 진행하십시오. + +## 탐색 데이터 분석 + +라이프사이클의 캡처 단계는 데이터를 획득하는 단계이며 당면한 문제와 질문입니다. 하지만 데이터가 최종 결과를 지원하는 데 도움이 될 수 있는지 어떻게 알 수 있을까요? +데이터 과학자는 데이터를 획득할 때 다음과 같은 질문을 할 수 있습니다. +- 이 문제를 해결할 데이터가 충분한가요? +- 이 문제에 적합한 품질의 데이터입니까? +- 이 데이터를 통해 추가 정보를 발견하게 되면 목표를 바꾸거나 재정의하는 것을 고려해야 하나요? +탐색적 데이터 분석은 데이터를 파악하는 프로세스이며, 이러한 질문에 답하는 데 사용할 수 있을 뿐만 아니라 데이터셋으로 작업하는 데 따른 당면 과제를 파악할 수 있습니다. 이를 달성하기 위해 사용되는 몇 가지 기술에 초점을 맞춰보겠습니다. + +## 데이터 프로파일링, 기술 통계 및 Pandas +이 문제를 해결하기에 충분한 데이터가 있는지 어떻게 평가합니까? 데이터 프로파일링은 기술 통계 기법을 통해 데이터셋에 대한 일반적인 전체 정보를 요약하고 수집할 수 있습니다. 데이터 프로파일링은 우리가 사용할 수 있는 것을 이해하는 데 도움이 되며 기술 통계는 우리가 사용할 수 있는 것이 얼마나 많은지 이해하는 데 도움이 됩니다. + +이전 강의에서 우리는 Pandas를 사용하여 [`describe()` 함수]와 함께 기술 통계를 제공했습니다. 숫자 데이터에 대한 카운트, 최대값 및 최소값, 평균, 표준 편차 및 분위수를 제공합니다. `describe()` 함수와 같은 기술 통계를 사용하면 얼마나 가지고 있고 더 필요한지를 평가하는 데 도움이 될 수 있습니다. + +## 샘플링 및 쿼리 +대규모 데이터셋의 모든 것을 탐색하는 것은 매우 많은 시간이 걸릴 수 있으며 일반적으로 컴퓨터가 수행해야 하는 작업입니다. 그러나 샘플링은 데이터를 이해하는 데 유용한 도구이며 데이터 집합에 무엇이 있고 무엇을 나타내는지를 더 잘 이해할 수 있도록 해줍니다. 표본을 사용하여 확률과 통계량을 적용하여 데이터에 대한 일반적인 결론을 내릴 수 있습니다. 표본 추출하는 데이터의 양에 대한 규칙은 정의되어 있지 않지만, 표본 추출하는 데이터의 양이 많을수록 데이터에 대한 일반화의 정확성을 높일 수 있다는 점에 유의해야 합니다. +Pandas에는 받거나 사용하려는 임의의 샘플 수에 대한 아규먼트를 전달할 수 있는 [라이브러리 속 함수`sample()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sample.html)이 있습니다. + +데이터에 대한 일반적인 쿼리는 몇 가지 일반적인 질문과 이론에 답하는 데 도움이 될 수 있습니다. 샘플링과 달리 쿼리를 사용하면 질문이 있는 데이터의 특정 부분을 제어하고 집중할 수 있습니다. +Pandas 라이브러리의 [`query()` 함수](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html)를 사용하면 열을 선택하고 간단한 검색된 행을 통해 데이터에 대한 답변을 제공받을 수 있습니다. + +## 시각화를 통한 탐색 +시각화 생성을 시작하기 위해 데이터가 완전히 정리되고 분석될 때까지 기다릴 필요가 없습니다. 실제로 탐색하는 동안 시각적 표현이 있으면 데이터의 패턴, 관계 및 문제를 식별하는 데 도움이 될 수 있습니다. 또한, 시각화는 데이터 관리에 관여하지 않는 사람들과 의사 소통하는 수단을 제공하고 캡처 단계에서 해결되지 않은 추가 질문을 공유하고 명확히 할 수 있는 기회가 될 수 있습니다. 시각적으로 탐색하는 몇 가지 인기 있는 방법에 대해 자세히 알아보려면 [section on Visualizations](3-Data-Visualization)을 참조하세요. + +## 불일치 식별을 위한 탐색 +이 강의의 모든 주제는 누락되거나 일치하지 않는 값을 식별하는 데 도움이 될 수 있지만 Pandas는 이러한 값 중 일부를 확인하는 기능을 제공합니다. [isna() 또는 isnull()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.isna.html)에서 결측값을 확인할 수 있습니다. 데이터 내에서 이러한 값을 탐구할 때 중요한 한 가지 요소는 처음에 이러한 값이 왜 이렇게 되었는지 이유를 탐구하는 것입니다. 이는 [문제 해결을 위해 취해야 할 조치](2-Working-With-Data\08-data-preparation\notebook.ipynb)를 결정하는 데 도움이 될 수 있습니다. + +## [강의 전 퀴즈](https://red-water-0103e7a0f.azurestaticapps.net/quiz/27) + +## 과제 + +[Exploring for answers](assignment.md) diff --git a/4-Data-Science-Lifecycle/15-analyzing/translations/assignment.ko.md b/4-Data-Science-Lifecycle/15-analyzing/translations/assignment.ko.md new file mode 100644 index 00000000..882c40a0 --- /dev/null +++ b/4-Data-Science-Lifecycle/15-analyzing/translations/assignment.ko.md @@ -0,0 +1,22 @@ +# 정답 찾기 + +이는 지난 강의의 [assignment](..\14-Introduction\assignment.md)와 이어지며, 우리는 잠시 데이터셋을 살펴보았습니다. 이제 데이터를 더욱 자세히 살펴보겠습니다. + +다시 한번, 고객이 알고싶어하는 질문: **뉴욕의 노란 택시 승객들은 겨울이나 여름에 기사들에게 팁을 더 많이 주나요?** + +당신의 팀은 Data Science Lifecycle의 [Analyzing](Readme.md)단계에 있으며, 이 곳에서 데이터셋에 대한 탐색적 데이터분석을 수행해야합니다. 당신은 2019년 1월부터 7월까지 200건의 택시 거래가 포함된 노트북과 데이터셋을 제공받았습니다. + +## 지시사항 + +이 디렉토리에는 [notebook](assignment.ipynb)와 [Taxi & Limousine Commission](https://docs.microsoft.com/en-us/azure/open-datasets/dataset-taxi-yellow?tabs=azureml-opendatasets)의 데이터가 있습니다. 데이터에 대한 자세한 내용은 [dataset's dictionary](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf) 및 [user guide](https://www1.nyc.gov/assets/tlc/downloads/pdf/trip_record_user_guide.pdf)를 참조하세요. + +이번 강의에서 배운 몇 가지 기술을 사용하여 노트북에 있는 EDA를 직접 수행하고(원하는 경우 셀 추가) 다음 질문에 답하십시오. + +- 데이터의 어떤 다른 영향이 팁 금액에 영향을 미칠 수 있습니까? +- 클라이언트의 질문에 답하는 데 가장 필요없는 열은 무엇입니까? +- 지금까지 제공된 자료에 따르면, 데이터가 계절별 팁에대한 증거를 제공하는 것 같습니까? + +## Rubric + +모범 | 충분 | 개선 필요 +--- | --- | -- | From a0415e4c967e67597f9b39076befb693076cb80d Mon Sep 17 00:00:00 2001 From: qzylalala <304228244@qq.com> Date: Sun, 7 Nov 2021 21:16:59 +0800 Subject: [PATCH 16/60] [zh-cn] 3/README --- .../translations/README.zh-cn.md | 28 +++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 3-Data-Visualization/translations/README.zh-cn.md diff --git a/3-Data-Visualization/translations/README.zh-cn.md b/3-Data-Visualization/translations/README.zh-cn.md new file mode 100644 index 00000000..2a5393fa --- /dev/null +++ b/3-Data-Visualization/translations/README.zh-cn.md @@ -0,0 +1,28 @@ +# 可视化 + +![a bee on a lavender flower](../images/bee.jpg) +> 拍摄者 Jenna Lee 上传于 Unsplash + +数据可视化是数据科学家最重要的任务之一。一张图片有时胜过千言万语，同时可视化还可以帮助你指出你的数据中包含的各种有趣的特征，例如峰值、异常值、分组、趋势等等，这可以帮助你更好的了解你的数据。 + +在这五节课当中，你将接触到来源于大自然的数据，并使用各种不同的技术来完成有趣且漂亮的可视化。 + +### 主题 + +1. [可视化数据](../09-visualization-quantities/README.md) +1. [可视化数据分布](../10-visualization-distributions/README.md) +1. [可视化数据占比](../11-visualization-proportions/README.md) +1. [可视化数据间的关系](../12-visualization-relationships/README.md) +1. [做有意义的可视化](../13-meaningful-visualizations/README.md) + +### 致谢 + +这些可视化课程是由 [Jen Looper](https://twitter.com/jenlooper) 用 🌸 编写的 + +🍯 US Honey Production 所使用的数据来自 Jessica Li 在 [Kaggle](https://www.kaggle.com/jessicali9530/honey-production) 上的项目. 实际上，该 [数据集](https://usda.library.cornell.edu/concern/publications/rn301137d) 来自 [美国农业部](https://www.nass.usda.gov/About_NASS/index.php). + +🍄 mushrooms 所使用的数据集也是来自于 [Kaggle](https://www.kaggle.com/hatterasdunton/mushroom-classification-updated-dataset) ，该数据集经历过 Hatteras Dunton 的一些小修订. 该数据集包括对与姬松茸和环柄菇属中 23 种金针菇相对应的假设样本的描述。 蘑菇取自于奥杜邦协会北美蘑菇野外指南 (1981)。 该数据集于 1987 年捐赠给了 UCI ML(机器学习数据集仓库) 27 + +🦆 Minnesota Birds 的数据也来自于 [Kaggle](https://www.kaggle.com/hannahcollins/minnesota-birds) ，是由 Hannah Collins 从 [Wikipedia](https://en.wikipedia.org/wiki/List_of_birds_of_Minnesota) 中获取的. + +以上这些数据集都遵循 [CC0: Creative Commons](https://creativecommons.org/publicdomain/zero/1.0/) 条款. \ No newline at end of file From c3deb5759b54e9d02e581f9c5efec647f097974a Mon Sep 17 00:00:00 2001 From: JULO01 <90795588+JULO01@users.noreply.github.com> Date: Sun, 7 Nov 2021 14:51:33 +0100 Subject: [PATCH 17/60] Fixed Navigation Bar scaling of quiz-app --- quiz-app/src/App.vue | 34 ++++++++++++++++++++++++++-------- 1 file changed, 26 insertions(+), 8 deletions(-) diff --git a/quiz-app/src/App.vue b/quiz-app/src/App.vue index 65b54a08..8b5365df 100644 --- a/quiz-app/src/App.vue +++ b/quiz-app/src/App.vue @@ -1,14 +1,21 @@