├── .github ├── ISSUE_TEMPLATE │ ├── bug_report.md │ ├── feature_request.md │ └── lesson_update.md ├── PULL_REQUEST_TEMPLATE │ └── translation_checklist.md └── workflows │ └── azure-static-web-apps-purple-hill-04aebfb03.yml ├── .gitignore ├── 1-Introduction ├── 01-defining-data-science │ ├── README.md │ ├── assignment.md │ ├── images │ │ ├── ds_wordcloud.png │ │ └── video-def-ds.png │ ├── notebook.ipynb │ ├── solution │ │ ├── assignment.md │ │ └── notebook.ipynb │ └── translations │ │ ├── README.es.md │ │ ├── README.hi.md │ │ ├── README.ko.md │ │ ├── README.nl.md │ │ ├── README.pt-br.md │ │ ├── README.ru.md │ │ ├── assignment.es.md │ │ ├── assignment.hi.md │ │ ├── assignment.ko.md │ │ ├── assignment.nl.md │ │ ├── assignment.pt-br.md │ │ └── assignment.ru.md ├── 02-ethics │ ├── README.md │ ├── assignment.md │ └── translations │ │ ├── README.hi.md │ │ ├── README.ko.md │ │ ├── README.nl.md │ │ ├── README.pt-br.md │ │ ├── README.ru.md │ │ ├── assignment.hi.md │ │ ├── assignment.ko.md │ │ ├── assignment.nl.md │ │ ├── assignment.pt-br.md │ │ └── assignment.ru.md ├── 03-defining-data │ ├── README.md │ ├── assignment.md │ └── translations │ │ ├── README.es.md │ │ ├── README.hi.md │ │ ├── README.ko.md │ │ ├── README.pt-br.md │ │ ├── README.ru.md │ │ ├── README.tr.md │ │ ├── assignment.hi.md │ │ ├── assignment.ko.md │ │ ├── assignment.pt-br.md │ │ └── assignment.ru.md ├── 04-stats-and-probability │ ├── README.md │ ├── assignment.ipynb │ ├── assignment.md │ ├── images │ │ ├── boxplot_byrole.png │ │ ├── boxplot_explanation.png │ │ ├── height-boxplot.png │ │ ├── normal-histogram.png │ │ ├── probability-density.png │ │ ├── video-prob-and-stats.png │ │ ├── weight-boxplot.png │ │ ├── weight-height-relationship.png │ │ └── weight-histogram.png │ ├── notebook.ipynb │ ├── solution │ │ └── assignment.ipynb │ └── translations │ │ ├── README.hi.md │ │ ├── README.ko.md │ │ ├── README.pt-br.md │ │ ├── README.ru.md │ │ ├── assignment.hi.md │ │ ├── assignment.ko.md │ │ ├── assignment.pt-br.md │ │ └── assignment.ru.md ├── README.md ├── images │ └── data.jpg └── translations │ ├── README.es.md │ ├── README.fa.md │ ├── README.fr.md │ ├── README.hi.md │ ├── README.it.md │ ├── README.ko.md │ ├── README.ne.md │ ├── README.nl.md │ ├── README.pt-br.md │ ├── README.ru.md │ └── README.zh-cn.md ├── 2-Working-With-Data ├── 05-relational-databases │ ├── README.md │ ├── airports.db │ ├── assignment.md │ └── translations │ │ ├── README.hi.md │ │ ├── README.ko.md │ │ ├── README.md │ │ └── assignment.hi.md ├── 06-non-relational │ ├── CocaColaCo.xlsx │ ├── InventoryExample.xltm │ ├── PersonsData.json │ ├── README.md │ ├── TwitterData.json │ ├── assignment.md │ ├── images │ │ ├── columnar-db.png │ │ ├── cosmosdb-emulator-explorer.png │ │ ├── cosmosdb-emulator-persons-query.png │ │ ├── cosmosdb-emulator-persons.png │ │ ├── document-db.png │ │ ├── formula-excel.png │ │ ├── function-excel.png │ │ ├── graph-db.png │ │ ├── kv-db.png │ │ ├── new-container.png │ │ └── parts-of-spreadsheet.png │ └── translations │ │ ├── README.hi.md │ │ ├── README.ko.md │ │ └── assignment.hi.md ├── 07-python │ ├── R │ │ └── notebook.ipynb │ ├── README.md │ ├── assignment.md │ ├── images │ │ ├── covidspread.png │ │ ├── covidtreat.png │ │ ├── dataframe-1.png │ │ ├── timeseries-1.png │ │ ├── timeseries-2.png │ │ ├── timeseries-3.png │ │ └── video-ds-python.png │ ├── notebook-covidspread.ipynb │ ├── notebook-papers.ipynb │ ├── notebook.ipynb │ └── translations │ │ ├── README.ko.md │ │ └── assignment.hi.md ├── 08-data-preparation │ ├── README.md │ ├── assignment.ipynb │ ├── assignment.md │ ├── index.html │ ├── notebook.ipynb │ └── translations │ │ ├── README.ko.md │ │ ├── README.md │ │ └── README.tr.md ├── README.md ├── images │ └── data-love.jpg └── translations │ ├── README.es.md │ ├── README.fr.md │ ├── README.hi.md │ ├── README.ko.md │ ├── README.ne.md │ ├── README.nl.md │ ├── README.ru.md │ └── README.zh-cn.md ├── 3-Data-Visualization ├── 09-visualization-quantities │ ├── README.md │ ├── assignment.md │ ├── images │ │ ├── category-counts-02.png │ │ ├── category-counts.png │ │ ├── category-length-02.png │ │ ├── category-length.png │ │ ├── full-data-bar-02.png │ │ ├── full-data-bar.png │ │ ├── labeled-wingspan-02.png │ │ ├── labeled-wingspan.png │ │ ├── max-wingspan-02.png │ │ ├── max-wingspan-labels-02.png │ │ ├── max-wingspan-labels.png │ │ ├── max-wingspan.png │ │ ├── scatterplot-wingspan-02.png │ │ ├── scatterplot-wingspan.png │ │ ├── superimposed-02.png │ │ └── superimposed.png │ ├── notebook.ipynb │ ├── solution │ │ └── notebook.ipynb │ └── translations │ │ ├── README.es.md │ │ ├── README.hi.md │ │ ├── README.ko.md │ │ ├── README.pt-br.md │ │ ├── assignment.es.md │ │ ├── assignment.ko.md │ │ ├── assignment.ne.md │ │ └── assignment.pt-br.md ├── 10-visualization-distributions │ ├── README.md │ ├── assignment.md │ ├── images │ │ ├── 2D-wb.png │ │ ├── 2D.png │ │ ├── density1.png │ │ ├── density2.png │ │ ├── density3.png │ │ ├── density4.png │ │ ├── dist1-wb.png │ │ ├── dist1.png │ │ ├── dist2-wb.png │ │ ├── dist2.png │ │ ├── dist3-wb.png │ │ ├── dist3.png │ │ ├── histogram-conservation-wb.png │ │ ├── histogram-conservation.png │ │ ├── multi.png │ │ ├── scatter-wb.png │ │ └── scatter.png │ ├── notebook.ipynb │ ├── solution │ │ └── notebook.ipynb │ └── translations │ │ ├── README.es.md │ │ ├── README.hi.md │ │ ├── README.ko.md │ │ ├── README.pt-br.md │ │ ├── assignment.es.md │ │ ├── assignment.ko.md │ │ └── assignment.pt-br.md ├── 11-visualization-proportions │ ├── README.md │ ├── assignment.md │ ├── images │ │ ├── donut-wb.png │ │ ├── donut.png │ │ ├── pie1-wb.png │ │ ├── pie1.png │ │ └── waffle.png │ ├── notebook.ipynb │ ├── solution │ │ └── notebook.ipynb │ └── translations │ │ ├── README.es.md │ │ ├── README.hi.md │ │ ├── assignment.es.md │ │ └── assignment.zh-cn.md ├── 12-visualization-relationships │ ├── README.md │ ├── assignment.md │ ├── images │ │ ├── dual-line.png │ │ ├── facet.png │ │ ├── line1.png │ │ ├── line2.png │ │ ├── scatter1.png │ │ ├── scatter2.png │ │ └── scatter3.png │ ├── notebook.ipynb │ ├── solution │ │ └── notebook.ipynb │ └── translations │ │ ├── README.es.md │ │ ├── README.hi.md │ │ ├── README.ko.md │ │ ├── assignment.es.md │ │ └── assignment.ko.md ├── 13-meaningful-visualizations │ ├── README.md │ ├── assignment.md │ ├── images │ │ ├── 3d.png │ │ ├── bad-chart-1.png │ │ ├── bad-chart-2.jpg │ │ ├── bad-chart-3.jpg │ │ ├── bad-chart-4.jpg │ │ ├── busing.png │ │ ├── liaisons.png │ │ └── tornado.png │ ├── solution │ │ ├── .gitignore │ │ ├── README.md │ │ ├── babel.config.js │ │ ├── package-lock.json │ │ ├── package.json │ │ ├── public │ │ │ ├── favicon.ico │ │ │ └── index.html │ │ └── src │ │ │ ├── App.vue │ │ │ ├── assets │ │ │ └── letters.json │ │ │ ├── components │ │ │ └── Nodes.vue │ │ │ └── main.js │ ├── starter │ │ ├── .gitignore │ │ ├── README.md │ │ ├── babel.config.js │ │ ├── package-lock.json │ │ ├── package.json │ │ ├── public │ │ │ ├── favicon.ico │ │ │ └── index.html │ │ └── src │ │ │ ├── App.vue │ │ │ ├── assets │ │ │ └── letters.json │ │ │ ├── components │ │ │ └── Nodes.vue │ │ │ └── main.js │ └── translations │ │ ├── README.es.md │ │ ├── README.hi.md │ │ ├── assignment.es.md │ │ └── assignment.hi.md ├── R │ ├── 09-visualization-quantities │ │ ├── README.md │ │ ├── assignment.md │ │ └── images │ │ │ ├── MaxWingspan-lineplot-improved.png │ │ │ ├── MaxWingspan-lineplot.png │ │ │ ├── MaxWingspan-scatterplot-improved.png │ │ │ ├── MaxWingspan-scatterplot.png │ │ │ ├── category-length.png │ │ │ ├── comparingdata.png │ │ │ ├── stacked-bar-chart.png │ │ │ └── superimposed-values.png │ ├── 10-visualization-distributions │ │ ├── README.md │ │ ├── assignment.md │ │ └── images │ │ │ ├── 2d-plot.png │ │ │ ├── bodymass-per-order.png │ │ │ ├── bodymass-smooth.png │ │ │ ├── density-plot.png │ │ │ ├── distribution-30bins.png │ │ │ ├── distribution-over-the-entire-dataset.png │ │ │ ├── filtered-histogram.png │ │ │ ├── less-smooth-bodymass.png │ │ │ ├── max-length-per-order.png │ │ │ └── wingspan-conservation-collation.png │ ├── 11-visualization-proportions │ │ ├── README.md │ │ └── images │ │ │ ├── donut-wb.png │ │ │ ├── pie1-wb.png │ │ │ └── waffle.png │ ├── 12-visualization-relationships │ │ ├── README.md │ │ └── images │ │ │ ├── dual-line.png │ │ │ ├── facet.png │ │ │ ├── line1.png │ │ │ ├── line2.png │ │ │ ├── scatter1.png │ │ │ ├── scatter2.png │ │ │ └── scatter3.png │ └── 13-meaningful-vizualizations │ │ ├── README.md │ │ └── images │ │ ├── 3d.png │ │ ├── bad-chart-1.png │ │ ├── bad-chart-2.jpg │ │ ├── bad-chart-3.jpg │ │ ├── bad-chart-4.jpg │ │ ├── busing.png │ │ ├── liaisons.png │ │ └── tornado.png ├── README.md ├── images │ └── bee.jpg └── translations │ ├── README.es.md │ ├── README.hi.md │ ├── README.ko.md │ ├── README.ne.md │ ├── README.nl.md │ ├── README.pt-br.md │ ├── README.ru.md │ └── README.zh-cn.md ├── 4-Data-Science-Lifecycle ├── 14-Introduction │ ├── README.md │ ├── assignment.md │ ├── images │ │ ├── CRISP-DM.png │ │ ├── data-science-lifecycle.jpg │ │ └── tdsp-lifecycle2.png │ ├── notebook.ipynb │ └── translations │ │ ├── assignment.ko.md │ │ └── assignment.zh-cn.md ├── 15-analyzing │ ├── README.md │ ├── assignment.ipynb │ ├── assignment.md │ ├── notebook.ipynb │ └── translations │ │ ├── README.ko.md │ │ ├── assignment.hi.md │ │ ├── assignment.ko.md │ │ └── assignment.ne.md ├── 16-communication │ ├── README.md │ ├── assignment.md │ ├── data-communication-emerson-presentation-graphic.xlsx │ └── translations │ │ ├── README.hi.md │ │ └── README.ko.md ├── README.md ├── images │ └── communication.jpg └── translations │ ├── README.hi.md │ ├── README.ne.md │ ├── README.pt-br.md │ ├── README.ru.md │ └── README.zh-cn.md ├── 5-Data-Science-In-Cloud ├── 17-Introduction │ ├── README.md │ ├── assignment.md │ └── translations │ │ ├── README.hi.md │ │ ├── README.ko.md │ │ ├── README.ne.md │ │ ├── assignment.hi.md │ │ ├── assignment.ko.md │ │ └── assignment.ne.md ├── 18-Low-Code │ ├── README.md │ ├── assignment.md │ ├── images │ │ ├── aml-1.PNG │ │ ├── aml-2.PNG │ │ ├── aml-3.PNG │ │ ├── aml-4.PNG │ │ ├── cluster-1.PNG │ │ ├── cluster-2.PNG │ │ ├── cluster-3.PNG │ │ ├── consumption-1.PNG │ │ ├── datascienceinthecloud.jpg │ │ ├── dataset-1.PNG │ │ ├── dataset-2.PNG │ │ ├── dataset-3.PNG │ │ ├── deploy-1.PNG │ │ ├── deploy-2.PNG │ │ ├── deploy-3.PNG │ │ ├── project-schema.PNG │ │ ├── workspace-1.PNG │ │ ├── workspace-2.PNG │ │ ├── workspace-3.PNG │ │ ├── workspace-4.PNG │ │ ├── workspace-5.PNG │ │ └── workspace-6.PNG │ └── translations │ │ ├── README.ko.md │ │ ├── README.md │ │ └── assignment.ko.md ├── 19-Azure │ ├── README.md │ ├── assignment.md │ ├── images │ │ ├── compute-instance-1.PNG │ │ ├── notebook-1.PNG │ │ ├── notebook-2.PNG │ │ └── project-schema.PNG │ ├── notebook.ipynb │ ├── solution │ │ └── notebook.ipynb │ └── translations │ │ ├── README.ko.md │ │ ├── assignment.hi.md │ │ ├── assignment.ko.md │ │ └── assignment.ne.md ├── README.md ├── images │ └── cloud-picture.jpg └── translations │ ├── README.hi.md │ ├── README.ko.md │ ├── README.ne.md │ ├── README.ru.md │ └── README.zh-cn.md ├── 6-Data-Science-In-Wild ├── 20-Real-World-Examples │ ├── README.md │ ├── assignment.md │ ├── images │ │ ├── data-science-applications.png │ │ └── planetary-computer-explorer.png │ └── translations │ │ ├── README.es.md │ │ ├── README.ko.md │ │ ├── assignment.es.md │ │ ├── assignment.hi.md │ │ └── assignment.ko.md ├── README.md └── translations │ ├── README.es.md │ ├── README.hi.md │ ├── README.ko.md │ ├── README.np.md │ ├── README.ru.md │ └── README.zh-cn.md ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── LICENSE ├── README.md ├── SECURITY.md ├── SUPPORT.md ├── TRANSLATIONS.md ├── data ├── COVID │ ├── time_series_covid19_confirmed_global.csv │ ├── time_series_covid19_deaths_global.csv │ └── time_series_covid19_recovered_global.csv ├── SOCR_MLB.tsv ├── UID_ISO_FIPS_LookUp_Table.csv ├── birds.csv ├── diabetes.tsv ├── emails.csv ├── form.csv ├── honey.csv ├── mushrooms.csv └── taxi.csv ├── docs └── _sidebar.md ├── docsifytopdf.js ├── ds-for-beginners.gif ├── ds-for-beginners.png ├── for-teachers.md ├── images └── favicon.png ├── index.html ├── package-lock.json ├── package.json ├── quiz-app ├── .gitignore ├── LICENSE ├── README.md ├── babel.config.js ├── dist │ ├── css │ │ └── app.67375a05.css │ ├── favicon.ico │ ├── index.html │ ├── js │ │ ├── app.37274da6.js │ │ ├── app.37274da6.js.map │ │ ├── chunk-vendors.c1571e8f.js │ │ └── chunk-vendors.c1571e8f.js.map │ └── routes.json ├── package-lock.json ├── package.json ├── public │ ├── favicon.ico │ ├── index.html │ └── routes.json └── src │ ├── App.vue │ ├── assets │ └── translations │ │ ├── en │ │ ├── group-1.json │ │ ├── group-2.json │ │ ├── group-3.json │ │ ├── group-4.json │ │ ├── group-5.json │ │ ├── group-6.json │ │ └── index.js │ │ ├── es │ │ ├── group-1.json │ │ ├── group-2.json │ │ ├── group-3.json │ │ ├── group-4.json │ │ ├── group-5.json │ │ ├── group-6.json │ │ └── index.js │ │ ├── fr │ │ ├── group-1.json │ │ ├── group-2.json │ │ └── index.js │ │ └── index.js │ ├── components │ └── Quiz.vue │ ├── main.js │ ├── router │ └── index.js │ └── views │ ├── Home.vue │ └── NotFound.vue ├── sketchnotes ├── 00-Roadmap.png ├── 00-Title.png ├── 01-Definitions.png ├── 02-Ethics.png ├── 03-DefiningData.png ├── 04-Statistics-Probability.png ├── 05-RelationalData.png ├── 06-NoSQL.png ├── 07-WorkWithPython.png ├── 08-DataPreparation.png ├── 09-Visualizing-Quantities.png ├── 10-Visualizing-Distributions.png ├── 11-Visualizing-Proportions.png ├── 12-Visualizing-Relationships.png ├── 13-MeaningfulViz.png ├── 14-DataScience-Lifecycle.png ├── 15-Analyzing.png ├── 16-Communicating.png ├── 17-DataScience-Cloud.png ├── 18-DataScience-Cloud.png ├── 19-DataScience-Cloud.png ├── 20-DataScience-Humanities.png ├── 20-DataScience-RealWorld.png ├── 20-DataScience-Research.png ├── 20-DataScience-Sustainability.png └── README.md └── translations ├── README.fa.md ├── README.fr.md ├── README.ko.md ├── README.ne.MD ├── README.ne.md ├── README.nl.md ├── README.pt-br.md ├── README.pt-pt.md ├── README.ru.md ├── README.tr.md ├── README.zh-cn.md └── for-teachers.hi.md /.github/ISSUE_TEMPLATE/bug_report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Bug report 3 | about: Create a report to help us improve 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Describe the bug** 11 | A clear and concise description of what the bug is. 12 | 13 | **To Reproduce** 14 | Steps to reproduce the behavior: 15 | 1. Go to '...' 16 | 2. Click on '....' 17 | 3. Scroll down to '....' 18 | 4. See error 19 | 20 | **Expected behavior** 21 | A clear and concise description of what you expected to happen. 22 | 23 | **Screenshots** 24 | If applicable, add screenshots to help explain your problem. 25 | 26 | **Desktop (please complete the following information):** 27 | - OS: [e.g. iOS] 28 | - Browser [e.g. chrome, safari] 29 | - Version [e.g. 22] 30 | 31 | **Smartphone (please complete the following information):** 32 | - Device: [e.g. iPhone6] 33 | - OS: [e.g. iOS8.1] 34 | - Browser [e.g. stock browser, safari] 35 | - Version [e.g. 22] 36 | 37 | **Additional context** 38 | Add any other context about the problem here. 39 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/feature_request.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Feature request 3 | about: Suggest an idea for this project 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Is your feature request related to a problem? Please describe.** 11 | A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] 12 | 13 | **Describe the solution you'd like** 14 | A clear and concise description of what you want to happen. 15 | 16 | **Describe alternatives you've considered** 17 | A clear and concise description of any alternative solutions or features you've considered. 18 | 19 | **Additional context** 20 | Add any other context or screenshots about the feature request here. 21 | -------------------------------------------------------------------------------- /.github/PULL_REQUEST_TEMPLATE/translation_checklist.md: -------------------------------------------------------------------------------- 1 | ## Track translation progress by opening a draft PR using this template and checking off the translations completed 2 | 3 | Each lesson includes a translation of the README.md and the Assignment.md file, if available. Only mark the lesson complete if both those files are translated per lesson, please. 4 | 5 | - [ ] 1 6 | - [ ] 1-1 7 | - [ ] 1-2 8 | - [ ] 1-3 9 | - [ ] 2 10 | - [ ] 2-1 11 | - [ ] 2-2 12 | - [ ] 2-3 13 | - [ ] 2-4 14 | - [ ] 3 15 | - [ ] 3-1 16 | - [ ] 3-2 17 | - [ ] 3-3 18 | - [ ] 4 19 | - [ ] 4-1 20 | - [ ] 5 21 | - [ ] 5-1 22 | - [ ] 5-2 23 | - [ ] 5-3 24 | - [ ] 6 25 | - [ ] 6-1 26 | - [ ] 6-2 27 | - [ ] 6-3 28 | - [ ] 6-4 29 | - [ ] 6-5 30 | - [ ] 6-6 31 | - [ ] 7 32 | - [ ] 7-1 33 | - [ ] 7-2 34 | - [ ] 7-3 35 | - [ ] 7-4 36 | 37 | - [ ] Quiz (add a file in the quiz-app with all localizations) 38 | -------------------------------------------------------------------------------- /1-Introduction/01-defining-data-science/assignment.md: -------------------------------------------------------------------------------- 1 | # Assignment: Data Science Scenarios 2 | 3 | In this first assignment, we ask you to think about some real-life process or problem in different problem domains, and how you can improve it using the Data Science process. Think about the following: 4 | 5 | 1. Which data can you collect? 6 | 1. How would you collect it? 7 | 1. How would you store the data? How large the data is likely to be? 8 | 1. Which insights you might be able to get from this data? Which decisions we would be able to take based on the data? 9 | 10 | Try to think about 3 different problems/processes and describe each of the points above for each problem domain. 11 | 12 | Here are some of the problem domains and problems that can get you started thinking: 13 | 14 | 1. How can you use data to improve education process for children in schools? 15 | 1. How can you use data to control vaccination during the pandemic? 16 | 1. How can you use data to make sure you are being productive at work? 17 | ## Instructions 18 | 19 | Fill in the following table (substitute suggested problem domains for your own ones if needed): 20 | 21 | | Problem Domain | Problem | Which data to collect | How to store the data | Which insights/decisions we can make | 22 | |----------------|---------|-----------------------|-----------------------|--------------------------------------| 23 | | Education | | | | | 24 | | Vaccination | | | | | 25 | | Productivity | | | | | 26 | 27 | ## Rubric 28 | 29 | Exemplary | Adequate | Needs Improvement 30 | --- | --- | -- | 31 | One was able to identify reasonable data sources, ways of storing data and possible decisions/insights for all problem domains | Some of the aspects of the solution are not detailed, data storage is not discussed, at least 2 problem domains are described | Only parts of the data solution are described, only one problem domain is considered. 32 | -------------------------------------------------------------------------------- /1-Introduction/01-defining-data-science/images/ds_wordcloud.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/1-Introduction/01-defining-data-science/images/ds_wordcloud.png -------------------------------------------------------------------------------- /1-Introduction/01-defining-data-science/images/video-def-ds.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/1-Introduction/01-defining-data-science/images/video-def-ds.png -------------------------------------------------------------------------------- /1-Introduction/01-defining-data-science/translations/assignment.hi.md: -------------------------------------------------------------------------------- 1 | # असाइनमेंट: डाटा साइंस के परिदृश्य 2 | 3 | इस असाइनमेंट मे हम चाहते हैं कि आप कुछ असल ज़िंदगी की दिक्कतें या क्रिया-कलाप सोचें विभिन्न क्षेत्रों मे, और फिर सोचें कि इसको हम डाटा साइंस के प्रयोग से कैसे सुधार सकते हैं| इन चीजों के बारे मे सोचें: 4 | 5 | 1. आप कौनसी डाटा इकट्ठा कर सकते हैं? 6 | 1. आप उसको कैसे इकट्ठा करेंगे? 7 | 1. आप उस डाटा को कैसे संग्रहीत करेंगे? वो डाटा कितनी बड़ी होगी? 8 | 1. अस डाटा से आपको क्या अनुमान मिलेगा? उस डाटा के आधार पर आप क्या निर्णय ले सकते हैं? 9 | 10 | किन्ही 3 अलग दिक्कत या क्रिया-कलाप के बारे मे सोचे का प्रयास करें और ऊपर लिखे हर पॉइंट को अलग कार्यक्षेत्र के लिए वर्णित कीजिए| 11 | 12 | यहा कुछ कार्यक्षेत्र और दिक्कतें लिखी हैं जिनकी मदद से आप सोचना शुरू कर सकते हैं: 13 | 14 | 1. आप डाटा का प्रयोग करके विद्यालय जा रहे बच्चों की शिक्षा कैसे सुधार सकते हैं? 15 | 1. आप डाटा का प्रयोग करके महामारी के समय मे टीकाकरण कैसे नियंत्रित कर सकते हैं? 16 | 1. आप डाटा का प्रयोग करके अपने काम मे कैसे और उत्पादक बन सकते हैं? 17 | ## निर्देश: 18 | 19 | निम्नलिखित मेज को भरें (अपने विकल्प सुझावित क्षेत्रों की जगह लिखें अगर जरूरत हो तो): 20 | 21 | | समस्या डोमेन | समस्या | कॉनसी डाटा संग्रहीत करनी है | डाटा को कैसे संग्रहीत करना है | कॉन्से निर्णय ले सकते हैं | 22 | |----------------|---------|-----------------------|-----------------------|--------------------------------------| 23 | | शिक्षा | | | | | 24 | | टीकाकरण | | | | | 25 | | उत्पादकता | | | | | 26 | 27 | ## सरनामा 28 | 29 | अनुकरणीय | पर्याप्त | सुधार चाहिए 30 | --- | --- | -- | 31 | डाटा के स्तोत्र को पहचानने मे, उसको भंडारित मे और निर्णय लेने मे सक्षम थे | समाधान के कुछ हिस्से विस्तृत नहीं हैं, डाटा को संग्रहीत करना नहीं बताया गया है, कम से कम दो क्षेत्रों का वर्णन है | समाधान के सिर्फ कुछ ही हिस्सों का वर्णन है, सिर्फ एक क्षेत्र पर विचार किया है| 32 | -------------------------------------------------------------------------------- /1-Introduction/01-defining-data-science/translations/assignment.ko.md: -------------------------------------------------------------------------------- 1 | # 과제: 데이터 사이언스 시나리오 2 | 3 | 이 첫 번째 과제에서는 실제 프로세스 또는 여러 문제 영역의 문제에 대해 생각하고 데이터 사이언스 프로세스를 사용하여 이를 개선할 수 있는 방법에 대해 생각해 보도록 요청합니다. 다음에 대해 생각해 보십시오. 4 | 5 | 1. 어떤 데이터를 수집할 수 있습니까? 6 | 1. 어떻게 모을 것인가? 7 | 1. 데이터를 어떻게 저장하시겠습니까? 데이터가 얼마나 클 것 같습니까? 8 | 1. 이 데이터에서 얻을 수 있는 통찰력은 무엇입니까? 데이터를 기반으로 어떤 결정을 내릴 수 있습니까? 9 | 10 | 3가지 다른 문제/프로세스에 대해 생각하고 각 문제 영역에 대해 위의 각 요점을 설명하십시오. 11 | 12 | 다음은 생각을 시작할 수 있는 몇 가지 문제 영역과 문제입니다. 13 | 14 | 1. 학교에서 아이들의 교육 과정을 개선하기 위해 데이터를 어떻게 사용할 수 있습니까? 15 | 1. 대유행 기간 동안 예방 접종을 통제하기 위해 데이터를 어떻게 사용할 수 있습니까? 16 | 1. 직장에서 생산성을 유지하기 위해 데이터를 어떻게 사용할 수 있습니까? 17 | ## 지침 18 | 19 | 다음 표를 채우십시오(필요한 경우 제안된 문제 도메인을 자신의 도메인으로 대체). 20 | 21 | | 문제 도메인 | 문제 | 수집할 데이터 | 데이터를 저장하는 방법 | 우리가 내릴 수 있는 통찰력/결정| 22 | |----------------|---------|-----------------------|-----------------------|--------------------------------------| 23 | | 교육 | | | | | 24 | | 예방 접종 | | | | | 25 | | 생산성 | | | | | 26 | 27 | ## 기준표 28 | 29 | 모범 | 충분 | 개선 필요 30 | --- | --- | -- | 31 | 합리적인 데이터 소스, 데이터 저장 방법 및 모든 도메인 영역에 대한 가능한 결정/통찰력을 식별할 수 있습니다. | 솔루션의 일부 측면이 상세하지 않고, 데이터 저장이 논의되지 않고, 적어도 2개의 문제 영역이 설명되어 있습니다. | 데이터 솔루션의 일부만 설명되고 하나의 문제 영역만 고려됩니다. -------------------------------------------------------------------------------- /1-Introduction/01-defining-data-science/translations/assignment.ru.md: -------------------------------------------------------------------------------- 1 | # Домашнее задание: сценарии в науке о данных 2 | 3 | В первом домашнем задании Вам предлагается рассмотреть процессы в реальном мире или проблемы в различных областях и как вы можете улучшить положение дел используя науку о данных. Ответьте на следующие вопросы: 4 | 5 | 1. Какие данные Вы планируете собирать? 6 | 1. Как Вы хотите собрать данные? 7 | 1. Как Вы будете хранить данные? Насколько большими будут они? 8 | 1. Какие инсайты Вы сможете извлечь из этих данных? Какие решения сможете принять на основе этих данных? 9 | 10 | Подумайте о трёх различных проблемах/процессах и опишите каждую по пунктам, приведённым выше. 11 | 12 | Ниже приведены некоторые области и проблемы в них, с которых Вы можете начать: 13 | 14 | 1. Как можно использовать данные, чтобы улучшить образовательный процесс в школах? 15 | 1. Как можно использовать данные, чтобы управлять вакцинацией в период пандемии? 16 | 1. Как можно использовать данные, чтобы повысить свою продуктивность на работе? 17 | 18 | ## Форма ответов 19 | 20 | Заполните следующую таблицу (замените предложенные области на другие, если необходимо): 21 | 22 | | Область | Проблема | Какие данные нужно собрать | Как хранить данные | Какие инсайты/решения можно извлечь/принять | 23 | |----------------|---------|-----------------------|-----------------------|--------------------------------------| 24 | | Образование | | | | | 25 | | Вакцинация | | | | | 26 | | Продуктивность | | | | | 27 | 28 | ## Оценка 29 | 30 | Отлично | Достаточно | Нуждается в улучшении 31 | --- | --- | -- | 32 | Верно определены источники данных, способы хранения данных и возможные решения/инсайты для всех областей | Некоторые аспекты решения недостаточно детально рассмотрены, хранилища данных не рассмотрены, рассмотрены по меньшей мере 2 области | Решение неполное, рассмотрена только одна область 33 | -------------------------------------------------------------------------------- /1-Introduction/02-ethics/translations/assignment.ko.md: -------------------------------------------------------------------------------- 1 | ## 데이터 윤리 사례 연구 작성 2 | 3 | ## 지침 4 | 5 | 다양한 [데이터 윤리 과제](README?id=_2-ethics-challenges)에 대해 배웠고 실제 컨텍스트의 데이터 윤리 과제를 반영하는 [사례 연구](README?id=_3-case-studies)의 몇 가지 예를 보았습니다. 6 | 7 | 이 과제에서는 자신의 경험이나 친숙한 관련 실제 상황에서 데이터 윤리 문제를 반영하는 사례 연구를 작성합니다. 다음 단계를 따르세요. 8 | 9 | 1. `데이터 윤리 과제 선택`. [수업 예시](README?id=_2-ethics-challenges)를 보거나 [Deon 체크리스트](https://deon.drivedata.org/examples/)와 같은 온라인 예시를 탐색하여 영감을 얻으십시오. 10 | 11 | 2. `실제 사례 설명`. 이러한 특정 문제가 발생한 상황(헤드라인, 연구 연구 등) 또는 경험했던(지역 커뮤니티) 상황에 대해 생각해 보십시오. 문제와 관련된 데이터 윤리 질문에 대해 생각하고 이 문제로 인해 발생하는 잠재적인 피해 또는 의도하지 않은 결과에 대해 논의합니다. 보너스 포인트: 이 문제의 부정적인 영향을 제거하거나 완화하기 위해 여기에 적용될 수 있는 잠재적 솔루션 또는 프로세스에 대해 생각하십시오. 12 | 13 | 3. `관련 자료 목록 제공`. 하나 이상의 리소스(기사 링크, 개인 블로그 게시물 또는 이미지, 온라인 연구 논문 등)를 공유하여 이것이 실제 발생했음을 증명합니다. 보너스 포인트: 사고로 인한 잠재적 피해 및 결과를 보여주는 리소스를 공유하거나 재발을 방지하기 위해 취한 긍정적인 조치를 강조합니다. 14 | 15 | 16 | 17 | ## 기준표 18 | 19 | 모범 | 충분 | 개선 필요 20 | --- | --- | -- | 21 | 하나 이상의 데이터 윤리 문제가 식별됩니다.

사례 연구는 그 도전을 반영하는 실제 사건을 명확하게 설명하고 그로 인해 야기된 바람직하지 않은 결과 또는 피해를 강조합니다.

이 문제가 발생했음을 증명하는 연결된 리소스가 하나 이상 있습니다. | 하나의 데이터 윤리 과제가 식별됩니다.

적어도 하나의 관련 피해 또는 결과가 간략하게 논의됩니다.

그러나 논의가 제한적이거나 실제 발생에 대한 증거가 부족합니다. | 데이터 챌린지가 식별됩니다.

그러나 설명이나 리소스가 문제를 적절하게 반영하지 않거나 실제 상황임을 증명하지 못합니다. | -------------------------------------------------------------------------------- /1-Introduction/03-defining-data/assignment.md: -------------------------------------------------------------------------------- 1 | # Classifying Datasets 2 | 3 | ## Instructions 4 | 5 | Follow the prompts in this assignment to identify and classify the data with one of each of the following data types: 6 | 7 | **Structure Types**: Structured, Semi-Structured, or Unstructured 8 | 9 | **Value Types**: Qualitative or Quantitative 10 | 11 | **Source Types**: Primary or Secondary 12 | 13 | 1. A company has been acquired and now has a parent company. The data scientists have received a spreadsheet of customer phone numbers from the parent company. 14 | 15 | Structure Type: 16 | 17 | Value Type: 18 | 19 | Source Type: 20 | 21 | --- 22 | 23 | 2. A smart watch has been collecting heart rate data from its wearer, and the raw data is in JSON format. 24 | 25 | Structure Type: 26 | 27 | Value Type: 28 | 29 | Source Type: 30 | 31 | --- 32 | 33 | 3. A workplace survey of employee morale that is stored in a CSV file. 34 | 35 | Structure Type: 36 | 37 | Value Type: 38 | 39 | Source Type: 40 | 41 | --- 42 | 43 | 4. Astrophysicists are accessing a database of galaxies that has been collected by a space probe. The data contains the number of planets within in each galaxy. 44 | 45 | Structure Type: 46 | 47 | Value Type: 48 | 49 | Source Type: 50 | 51 | --- 52 | 53 | 5. A personal finance app uses APIs to connect to a user's financial accounts in order to calculate their net worth. They can see all of their transactions in a format of rows and columns and looks similar to a spreadsheet. 54 | 55 | Structure Type: 56 | 57 | Value Type: 58 | 59 | Source Type: 60 | 61 | ## Rubric 62 | 63 | Exemplary | Adequate | Needs Improvement 64 | --- | --- | -- | 65 | Correctly identifies all structure, value, and sources |Correctly identifies 3 all structure, value, and sources|Correctly identifies 2 or less all structure, value, and sources| -------------------------------------------------------------------------------- /1-Introduction/03-defining-data/translations/assignment.hi.md: -------------------------------------------------------------------------------- 1 | # डाटासेट को वर्गीकृत करना 2 | 3 | ## निर्देश 4 | 5 | इस असाइनमेंट मे निम्नलिखित प्रॉम्प्ट को देख कर डाटा को पहचाने व एक या उससे ज्यादा डाटा टाइप मे वर्गीकृत करें: 6 | 7 | **स्ट्रक्चर टाइप**: संरचित, अर्ध्य-संरचित अथवा असंरचित 8 | 9 | **वैल्यू टाइप**: गुणात्मक अथवा मात्रात्मक 10 | 11 | **सोर्स टाइप**: मुख्य अथवा माध्यमिक 12 | 13 | 1. एक कंपनी को अधिग्रहित किया गया है और अब उसकी पेरन्ट कंपनी है| डाटा वैज्ञानिकों को पेरन्ट कंपनी से ग्राहकों के फओबने नंबर की सूची प्राप्त हुई है| 14 | 15 | स्ट्रक्चर टाइप: 16 | 17 | वैल्यू टाइप: 18 | 19 | सोर्स टाइप: 20 | 21 | --- 22 | 23 | 2. एक स्मार्ट वाच उसको पहनने वाले का हृदय दर का माप ले रही है, जो की JSON फॉर्मैट मे है| 24 | 25 | स्ट्रक्चर टाइप: 26 | 27 | वैल्यू टाइप: 28 | 29 | सोर्स टाइप: 30 | 31 | --- 32 | 33 | 3. कुछ कर्मचारियो का कार्यस्थल सर्वेक्षण जो की एक CSV फाइल मे संग्रहीत है| 34 | 35 | स्ट्रक्चर टाइप: 36 | 37 | वैल्यू टाइप: 38 | 39 | सोर्स टाइप: 40 | 41 | --- 42 | 43 | 4. कुछ खगोल वैज्ञानिक एक आकाशगंगा के डेटाबेस को जांच रहे हैं जो अंतरिक्ष जांच से मिला है| उसकी डाटा मे हर आकाशगंगा मे स्थित ग्रहों की संख्या है| 44 | 45 | स्ट्रक्चर टाइप: 46 | 47 | वैल्यू टाइप: 48 | 49 | सोर्स टाइप: 50 | 51 | --- 52 | 53 | 5. एक वयऐक्टिक फाइनैन्स एप कुछ APIs के सहारे एक व्यक्ति के आर्थिक खाते से जुड़ता है व उनकी कुल योग्यता निकलता है| यूजर अपनी सारी लेनदेन को एक स्प्रेड्शीट की तरह पंक्ति और स्तम्भ के रूप मे देख सकते हैं| 54 | 55 | स्ट्रक्चर टाइप: 56 | 57 | वैल्यू टाइप: 58 | 59 | सोर्स टाइप: 60 | 61 | ## सरनामा 62 | 63 | अनुकरणीय | पर्याप्त | सुधार चाहिए 64 | --- | --- | -- | 65 | डाटा के स्तोत्र को पहचानने मे, उसको भंडारित मे और निर्णय लेने मे सक्षम थे | समाधान के कुछ हिस्से विस्तृत नहीं हैं, डाटा को संग्रहीत करना नहीं बताया गया है, कम से कम दो क्षेत्रों का वर्णन है | समाधान के सिर्फ कुछ ही हिस्सों का वर्णन है, सिर्फ एक क्षेत्र पर विचार किया है| 66 | -------------------------------------------------------------------------------- /1-Introduction/03-defining-data/translations/assignment.ko.md: -------------------------------------------------------------------------------- 1 | # 데이터셋 분류 2 | 3 | ## 지침 4 | 5 | 이 과제의 프롬프트에 따라 다음 데이터 타입 중 하나로 데이터를 식별하고 분류합니다. 6 | 7 | **구조 유형**: 구조화, 반구조화 또는 비구조화 8 | 9 | **값 유형**: 정성적 또는 정량적 10 | 11 | **소스 유형**: Primary 또는 Secondary 12 | 13 | 1. 회사가 인수되었고, 현재 모회사가 있습니다. 데이터 과학자들은 모회사로부터 고객 전화번호 스프레드시트를 받았습니다. 14 | 15 | 구조 유형: 16 | 17 | 값 유형: 18 | 19 | 소스 유형: 20 | 21 | --- 22 | 23 | 2. 스마트 워치는 착용자로부터 심박수 데이터를 수집하고 있으며 원시 데이터는 JSON 형식입니다. 24 | 25 | 구조 유형: 26 | 27 | 값 유형: 28 | 29 | 소스 유형: 30 | 31 | --- 32 | 33 | 3. CSV 파일에 저장된 직원 사기의 직장 설문 조사. 34 | 35 | 구조 유형: 36 | 37 | 값 유형: 38 | 39 | 소스 유형: 40 | 41 | --- 42 | 43 | 4. 천체 물리학자들은 우주 탐사선에 의해 수집된 은하 데이터베이스에 접근하고 있습니다. 데이터에는 각 은하에 있는 행성의 수가 포함됩니다. 44 | 45 | 구조 유형: 46 | 47 | 값 유형: 48 | 49 | 소스 유형: 50 | 51 | --- 52 | 53 | 5. 개인 금융 앱은 API를 사용하여 사용자의 금융 계정에 연결하여 순자산을 계산합니다. 행과 열 형식으로 모든 거래를 볼 수 있으며 스프레드시트와 유사하게 보입니다. 54 | 55 | 구조 유형: 56 | 57 | 값 유형: 58 | 59 | 소스 유형: 60 | 61 | ## 기준표 62 | 63 | 모범 | 충분 | 개선 필요 64 | --- | --- | -- | 65 | 모든 구조, 값 및 소스를 올바르게 식별 |모든 구조, 값 및 소스를 3개 모두 올바르게 식별|2개 이하의 모든 구조, 값 및 소스를 올바르게 식별| -------------------------------------------------------------------------------- /1-Introduction/03-defining-data/translations/assignment.ru.md: -------------------------------------------------------------------------------- 1 | # Классификация датасетов 2 | 3 | ## Порядок выполнения 4 | 5 | Следуйте подсказкам в данном домашнем задании, чтобы определить и классифицировать данные по следующим категориям: 6 | 7 | **Структура**: структурированные, полуструктурированные, неструктурированные 8 | 9 | **Величины**: количественные или качественные 10 | 11 | **Источники**: первичные или вторичные 12 | 13 | 1. Компания была приобретена и теперь у нее есть материнская компания. Дата сайентисты получили таблицу с номерами телефонов клиентов от материнской компании. 14 | 15 | Структура: 16 | 17 | Величина: 18 | 19 | Источник: 20 | 21 | --- 22 | 23 | 2. Умные часы собрали данные сердечного ритма владельца и сохранили сырые данные в формате JSON. 24 | 25 | Структура: 26 | 27 | Величина: 28 | 29 | Источник: 30 | 31 | --- 32 | 33 | 3. Опрос настроения сотрудников, результаты которого хранятся в файле CSV. 34 | 35 | Структура: 36 | 37 | Величина: 38 | 39 | Источник: 40 | 41 | --- 42 | 43 | 4. Астрофизики получили доступ к базе данных галактик, которая была собрана с помощью космического зонда. Данные содержат номера планет в переделах каждой галактики. 44 | 45 | Структура: 46 | 47 | Величина: 48 | 49 | Источник: 50 | 51 | --- 52 | 53 | 5. Приложение для управления финансами использует API для соединения с финансовым счетом пользователя для того, чтобы рассчитать его траты. Пользователи могут видеть свои транзакции в строках и столбцах, напоминающих таблицу. 54 | 55 | Структура: 56 | 57 | Величина: 58 | 59 | Источник: 60 | 61 | ## Оценка 62 | 63 | Отлично | Достаточно | Нуждается в улучшении 64 | --- | --- | -- | 65 | Верно определены все структуры, величины и источники |Верно определены структура, величины и источники трёх примеров целиком |Верно определены структура, величины и источники двух и менее примеров целиком | -------------------------------------------------------------------------------- /1-Introduction/04-stats-and-probability/assignment.md: -------------------------------------------------------------------------------- 1 | # Small Diabetes Study 2 | 3 | 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). 4 | 5 | | | AGE | SEX | BMI | BP | S1 | S2 | S3 | S4 | S5 | S6 | Y | 6 | |---|-----|-----|-----|----|----|----|----|----|----|----|----| 7 | | 0 | 59 | 2 | 32.1 | 101. | 157 | 93.2 | 38.0 | 4. | 4.8598 | 87 | 151 | 8 | | 1 | 48 | 1 | 21.6 | 87.0 | 183 | 103.2 | 70. | 3. | 3.8918 | 69 | 75 | 9 | | 2 | 72 | 2 | 30.5 | 93.0 | 156 | 93.6 | 41.0 | 4.0 | 4. | 85 | 141 | 10 | | ... | ... | ... | ... | ...| ...| ...| ...| ...| ...| ...| ... | 11 | 12 | ## Instructions 13 | 14 | * Open the [assignment notebook](assignment.ipynb) in a jupyter notebook environment 15 | * Complete all tasks listed in the notebook, namely: 16 | * [ ] Compute mean values and variance for all values 17 | * [ ] Plot boxplots for BMI, BP and Y depending on gender 18 | * [ ] What is the the distribution of Age, Sex, BMI and Y variables? 19 | * [ ] Test the correlation between different variables and disease progression (Y) 20 | * [ ] Test the hypothesis that the degree of diabetes progression is different between men and women 21 | 22 | ## Rubric 23 | 24 | Exemplary | Adequate | Needs Improvement 25 | --- | --- | -- | 26 | All required tasks are complete, 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साथ काम करेंगे। 3 | 4 | | | AGE | SEX | BMI | BP | S1 | S2 | S3 | S4 | S5 | S6 | Y | 5 | |---|-----|-----|-----|----|----|----|----|----|----|----|----| 6 | | 0 | 59 | 2 | 32.1 | 101. | 157 | 93.2 | 38.0 | 4. | 4.8598 | 87 | 151 | 7 | | 1 | 48 | 1 | 21.6 | 87.0 | 183 | 103.2 | 70. | 3. | 3.8918 | 69 | 75 | 8 | | 2 | 72 | 2 | 30.5 | 93.0 | 156 | 93.6 | 41.0 | 4.0 | 4. | 85 | 141 | 9 | | ... | ... | ... | ... | ...| ...| ...| ...| ...| ...| ...| ... | 10 | 11 | ## निर्देश 12 | 13 | * ज्यूपिटर नोटबुक वातावरण में [असाइनमेंट नोटबुक] (असाइनमेंट.आईपीएनबी) खोलें 14 | * नोटबुक में सूचीबद्ध सभी कार्यों को पूरा करें, अर्थात्: 15 | * [ ] सभी मूल्यों के लिए माध्य मान और विचरण की गणना करें 16 | * [ ] लिंग के आधार पर बीएमआई, बीपी और वाई के लिए प्लॉट बॉक्सप्लॉट 17 | * [ ] आयु, लिंग, बीएमआई और वाई चर का वितरण क्या है? 18 | * [ ] विभिन्न चर और रोग प्रगति के बीच सहसंबंध का परीक्षण करें (Y) 19 | * [ ] इस परिकल्पना का परीक्षण करें कि पुरुषों और महिलाओं के बीच मधुमेह की प्रगति की डिग्री अलग है 20 | 21 | ## सरनामा 22 | 23 | अनुकरणीय | पर्याप्त | सुधार की जरूरत 24 | --- | --- | -- | 25 | सभी आवश्यक कार्य पूर्ण, ग्राफिक रूप से सचित्र और समझाया गया है | अधिकांश कार्य पूर्ण हैं, ग्राफ़ और/या प्राप्त मूल्यों से स्पष्टीकरण या टेकअवे गायब हैं | केवल मूल कार्य जैसे माध्य/विचरण की गणना और मूल प्लॉट पूरे होते हैं, डेटा से कोई निष्कर्ष नहीं निकाला जाता है 26 | -------------------------------------------------------------------------------- /1-Introduction/04-stats-and-probability/translations/assignment.ko.md: -------------------------------------------------------------------------------- 1 | # 소당뇨병 연구 2 | 3 | 이 과제에서 우리는 [여기](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)에서 가져온 당뇨병 환자의 작은 데이터셋으로 작업할 것입니다. 4 | 5 | | | AGE | SEX | BMI | BP | S1 | S2 | S3 | S4 | S5 | S6 | Y | 6 | |---|-----|-----|-----|----|----|----|----|----|----|----|----| 7 | | 0 | 59 | 2 | 32.1 | 101. | 157 | 93.2 | 38.0 | 4. | 4.8598 | 87 | 151 | 8 | | 1 | 48 | 1 | 21.6 | 87.0 | 183 | 103.2 | 70. | 3. | 3.8918 | 69 | 75 | 9 | | 2 | 72 | 2 | 30.5 | 93.0 | 156 | 93.6 | 41.0 | 4.0 | 4. | 85 | 141 | 10 | | ... | ... | ... | ... | ...| ...| ...| ...| ...| ...| ...| ... | 11 | 12 | ## 지침 13 | 14 | * jupyter notebook 환경에서 [과제노트](assignment.ipynb) 열기 15 | * notebook 에 나열된 모든 작업, 즉: 16 | 17 | [ ] 모든 값의 평균값과 분산 계산 18 | 19 | [ ] 성별에 따른 BMI, BP 및 Y에 대한 플롯 상자 그림 20 | 21 | [ ] 연령, 성별, BMI 및 Y 변수의 분포는 무엇입니까? 22 | 23 | [ ] 다른 변수와 질병 진행 사이의 상관 관계 테스트(Y) 24 | 25 | [ ] 당뇨병 진행 정도가 남녀 간에 다르다는 가설 검정 26 | ## 기준표 27 | 28 | 모범 | 충분 | 개선 필요 29 | --- | --- | -- | 30 | 필요한 모든 작업이 완료되고 그래픽으로 설명 및 설명 되어 있음 | 대부분의 작업이 완료되었으며 그래프 및/또는 얻은 값의 설명이나 요약이 누락되었습니다. | 평균/분산 계산 및 기본 도표와 같은 기본 작업만 완료되어 있으며 데이터에서 결론이 내려지지 않습니다. -------------------------------------------------------------------------------- /1-Introduction/04-stats-and-probability/translations/assignment.pt-br.md: -------------------------------------------------------------------------------- 1 | # Pequeno Estudo de Diabetes 2 | 3 | Nessa tarefa, nós vamos trabalhar com um pequeno dataset de diabetes em pacientes retirados [daqui](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html). 4 | 5 | | | AGE | SEX | BMI | BP | S1 | S2 | S3 | S4 | S5 | S6 | Y | 6 | |---|-----|-----|-----|----|----|----|----|----|----|----|----| 7 | | 0 | 59 | 2 | 32.1 | 101. | 157 | 93.2 | 38.0 | 4. | 4.8598 | 87 | 151 | 8 | | 1 | 48 | 1 | 21.6 | 87.0 | 183 | 103.2 | 70. | 3. | 3.8918 | 69 | 75 | 9 | | 2 | 72 | 2 | 30.5 | 93.0 | 156 | 93.6 | 41.0 | 4.0 | 4. | 85 | 141 | 10 | | ... | ... | ... | ... | ...| ...| ...| ...| ...| ...| ...| ... | 11 | 12 | ## Instruções 13 | 14 | * Abre o [notebook da tarefa](assignment.ipynb) em um ambiente jupyter notebook 15 | * Complete todas as tarefas listadas no notebook, nomeadamente: 16 | [ ] Compute os valores de média e variância para todos os valores 17 | [ ] "Plote" boxplots para BMI, BP e Y dependendo do gênero 18 | [ ] Qual a distribuição das variáveis Age, Sex, BMI e Y? 19 | [ ] Teste a correlação entre diferentes variáveis e progressão da doença (Y) 20 | [ ] Teste a hipótese que o grau de progressão da diabetes é diferente entre homens e mulheres 21 | ## Rubrica 22 | 23 | Exemplar | Adequado | Precisa melhorar 24 | --- | --- | -- | 25 | Todas as tarefas estão completados, graficamente ilustradas e explicadas | A maior para das tarefas estão completadas, explicações ou conclusões a partir de gráficos e/ou valores obtidos estão faltando | Apenas as tarefas básicas como computar a média/variância e "plots" básicos estão completados, nenhuma conclusão é feita a partir dos dados dados. -------------------------------------------------------------------------------- /1-Introduction/04-stats-and-probability/translations/assignment.ru.md: -------------------------------------------------------------------------------- 1 | # Небольшое исследование диабета 2 | 3 | В данном домашнем задании мы поработаем с небольшим датасетом пациентов с диабетом, взятым [здесь](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html). 4 | 5 | | | AGE | SEX | BMI | BP | S1 | S2 | S3 | S4 | S5 | S6 | Y | 6 | |---|-----|-----|-----|----|----|----|----|----|----|----|----| 7 | | 0 | 59 | 2 | 32.1 | 101. | 157 | 93.2 | 38.0 | 4. | 4.8598 | 87 | 151 | 8 | | 1 | 48 | 1 | 21.6 | 87.0 | 183 | 103.2 | 70. | 3. | 3.8918 | 69 | 75 | 9 | | 2 | 72 | 2 | 30.5 | 93.0 | 156 | 93.6 | 41.0 | 4.0 | 4. | 85 | 141 | 10 | | ... | ... | ... | ... | ...| ...| ...| ...| ...| ...| ...| ... | 11 | 12 | ## Порядок выполнения 13 | 14 | * Откройте [блокнот для домашнего задания](../assignment.ipynb) в окружении Jupyter 15 | * Выполните все перечисленные там задачи, а именно: 16 | [ ] Вычислите средние значения и дисперсии для всех величин 17 | [ ] Постройте "ящики с усами" для признаков BMI, BP и Y в зависимости от пола пациента 18 | [ ] Какие распределения имеют признаки Age, Sex, BMI и Y? 19 | [ ] Проверьте корреляцию между различными переменными и степенью болезни (столбец Y) 20 | [ ] Проверьте гипотезу о том, что степень диабета различается у мужчин и женщин 21 | 22 | ## Оценка 23 | 24 | Отлично | Достаточно | Нуждается в улучшении 25 | --- | --- | -- | 26 | Все задачи выполнены, проиллюстрированы и содержат пояснения | Большинство задач выполнены, пояснения или выводы из графиков и/или полученных значений отсутствуют | Некоторые простые задачи, такие как вычисление среднего/дисперсии, выполнены, выводы на основе данных не были получены -------------------------------------------------------------------------------- /1-Introduction/README.md: -------------------------------------------------------------------------------- 1 | # Introduction to Data Science 2 | 3 | ![data in action](images/data.jpg) 4 | > Photo by Stephen Dawson on Unsplash 5 | 6 | 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. 7 | 8 | ### Topics 9 | 10 | 1. [Defining Data Science](01-defining-data-science/README.md) 11 | 2. [Data Science Ethics](02-ethics/README.md) 12 | 3. [Defining Data](03-defining-data/README.md) 13 | 4. [Introduction to Statistics and Probability](04-stats-and-probability/README.md) 14 | 15 | ### Credits 16 | 17 | These lessons were written with ❤️ by [Nitya Narasimhan](https://twitter.com/nitya) and [Dmitry Soshnikov](https://twitter.com/shwars). 18 | -------------------------------------------------------------------------------- /1-Introduction/images/data.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/1-Introduction/images/data.jpg -------------------------------------------------------------------------------- /1-Introduction/translations/README.es.md: -------------------------------------------------------------------------------- 1 | # Introducción a la Ciencia de Datos 2 | 3 | ![Datos en acción](../images/data.jpg) 4 | > Fotografía de Stephen Dawson en Unsplash 5 | 6 | En estas lecciones descubrirás cómo se define la Ciencia de Datos y aprenderás acerca de 7 | las cosideraciones éticas que deben ser tomadas por un científico de datos. También aprenderás 8 | cómo se definen los datos y un poco de probabilidad y estadística, el núcleo académico de la Ciencia de Datos. 9 | 10 | ### Temas 11 | 12 | 1. [Definiendo la Ciencia de Datos](../01-defining-data-science/README.md) 13 | 2. [Ética de la Ciencia de Datos](../02-ethics/README.md) 14 | 3. [Definición de Datos](../03-defining-data/translations/README.es.md) 15 | 4. [Introducción a la probabilidad y estadística](../04-stats-and-probability/README.md) 16 | 17 | ### Créditos 18 | 19 | Éstas lecciones fueron escritas con ❤️ por [Nitya Narasimhan](https://twitter.com/nitya) y [Dmitry Soshnikov](https://twitter.com/shwars). 20 | -------------------------------------------------------------------------------- /1-Introduction/translations/README.fa.md: -------------------------------------------------------------------------------- 1 |
2 | 3 | # مقدمه‌ای بر علم داده 4 | 5 | 6 | ![data in action](../images/data.jpg) 7 | > تصویر از Stephen Dawson در Unsplash 8 | 9 | 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. 10 | 11 | ### سرفصل ها 12 | 13 | 1. [تعریف علم داده](../01-defining-data-science/README.md) 14 | 2. [اصول اخلاقی علم داده](../02-ethics/README.md) 15 | 3. [تعریف داده](../03-defining-data/README.md) 16 | 4. [مقدمه ای بر آمار و احتمال](../04-stats-and-probability/README.md) 17 | 18 | ### تهیه کنندگان 19 | 20 | این درس ها با ❤️ توسط [Nitya Narasimhan](https://twitter.com/nitya) و [Dmitry Soshnikov](https://twitter.com/shwars) تهیه شده است. 21 |
22 | -------------------------------------------------------------------------------- /1-Introduction/translations/README.fr.md: -------------------------------------------------------------------------------- 1 | # Introduction à la science des données 2 | 3 | ![données en action](images/data.jpg) 4 | > Photo par Stephen Dawson sur Unsplash 5 | 6 | Dans ces leçons, vous découvrirez comment la science des données est définie et apprendrez les considérations éthiques qui doivent être prises en compte par un scientifique des données. Vous apprendrez également comment les données sont définies et vous vous familiariserez avec les statistiques et les probabilités, qui sont les principaux domaines académiques de la science des données. 7 | 8 | ### Thèmes 9 | 10 | 1. [Définition de la science des données](01-defining-data-science/README.md) 11 | 2. [Éthique de la science des données](02-ethics/README.md) 12 | 3. [Définir les données](03-defining-data/README.md) 13 | 4. [Introduction aux statistiques et aux probabilités](04-stats-and-probability/README.md) 14 | 15 | ### Crédits 16 | 17 | Ces leçons ont été rédigées avec ❤️ par [Nitya Narasimhan](https://twitter.com/nitya) et [Dmitry Soshnikov](https://twitter.com/shwars). -------------------------------------------------------------------------------- /1-Introduction/translations/README.hi.md: -------------------------------------------------------------------------------- 1 | # डेटा विज्ञान का परिचय 2 | 3 | ![डेटा इन एक्शन](../images/data.jpg) 4 | >Photo by Stephen Dawson on Unsplash 5 | 6 | इन पाठों में, आप जानेंगे कि डेटा विज्ञान को कैसे परिभाषित किया जाता है और उन नैतिक विचारों के बारे में जानेंगे जिन पर एक डेटा वैज्ञानिक को विचार करना चाहिए। आप यह भी जानेंगे कि डेटा को कैसे परिभाषित किया जाता है तथा आप डेटा की सांख्यिकी और संभाव्यता के बारे में भी थोरा सीखेगे जोकी एक डेटा विज्ञान के मुख्य शैक्षणिक डोमेन है । 7 | ### विषय 8 | 9 | 1. [डेटा साइंस को परिभाषित करना](01-defining-data-science/README.md) 10 | 2. [डेटा साइंस एथिक्स](02-ethics/README.md) 11 | 3. [डेटा को परिभाषित करना](03-defining-data/README.md) 12 | 4. [सांख्यिकी और संभाव्यता का परिचय](04-stats-and-probability/README.md) 13 | 14 | ### क्रेडिट 15 | 16 | ये पाठ [Nitya Narasimhan](https://twitter.com/nitya) और [Dmitry Soshnikov](https://twitter.com/shwars) द्वारा ❤️ के साथ लिखे गए थे। 17 | -------------------------------------------------------------------------------- /1-Introduction/translations/README.it.md: -------------------------------------------------------------------------------- 1 | # Introduzione alla Data Science 2 | 3 | ![data in action](../images/data.jpg) 4 | > Fotografia di Stephen Dawson su Unsplash 5 | 6 | In queste lezioni, scoprirai come la Data Science è definita e apprenderai le considerazioni etiche che devono essere considerate da un data scientist. Imparerai anche come i dati sono definiti e ti verrà introdotta un po' di statistica e probabilità, i principali temi accademici della Data Science. 7 | 8 | ### Topics 9 | 10 | 1. [Definizione di Data Science](01-defining-data-science/README.md) 11 | 2. [L'etica della Data Science](02-ethics/README.md) 12 | 3. [Definizione di Dati](03-defining-data/README.md) 13 | 4. [Introduzione alla Statistica e alla Probabilità](04-stats-and-probability/README.md) 14 | 15 | ### Credits 16 | 17 | Queste lezioni sono state scritte con ❤️ da [Nitya Narasimhan](https://twitter.com/nitya) e [Dmitry Soshnikov](https://twitter.com/shwars). 18 | -------------------------------------------------------------------------------- /1-Introduction/translations/README.ko.md: -------------------------------------------------------------------------------- 1 | # 데이터 과학(Data Science) 소개 2 | 3 | ![활용중인 데이터](../images/data.jpg) 4 | > 촬영 작가: Stephen Dawson on Unsplash 5 | 6 | 이 수업에서는 데이터 과학이 어떻게 정의되는지 알아보고 데이터 과학자가 고려해야 하는 윤리적인 사항들에 대해 배웁니다. 또한 데이터가 어떻게 정의되는지 배우고 데이터 과학의 핵심 학문 영역인 확률과 통계에 대해서 간단히 배우게 됩니다. 7 | 8 | ### 주제 9 | 10 | 1. [데이터 과학 정의](../01-defining-data-science/translations/README.ko.md) 11 | 2. [데이터 과학 윤리](../02-ethics/translations/README.ko.md) 12 | 3. [데이터 정의](../03-defining-data/translations/README.ko.md) 13 | 4. [확률과 통계 소개](../04-stats-and-probability/translations/README.ko.md) 14 | 15 | ### 크레딧 16 | 17 | 강의를 제작한 분: [Nitya Narasimhan](https://twitter.com/nitya) 과 [Dmitry Soshnikov](https://twitter.com/shwars) -------------------------------------------------------------------------------- /1-Introduction/translations/README.ne.md: -------------------------------------------------------------------------------- 1 | # डाटा विज्ञान को परिचय 2 | 3 | ![कार्यमा डाटा](../images/data.jpg) 4 | > फोटो Stephen Dawson on Unsplash द्वारा 5 | 6 | यी पाठहरूमा, तपाईंले डाटा विज्ञानलाई कसरी परिभाषित गरिएको छ भनेर थाहा पाउनुहुनेछ र डेटा वैज्ञानिकले विचार गर्नुपर्ने नैतिक विचारहरूको बारेमा सिक्नुहुनेछ। तपाइँ यो पाठमा कसरी डाटा परिभाषित गरिएको छ र डाटा विज्ञान को मुख्य शैक्षिक डोमेन, statistics र probability पनि केहि मात्रामा जान्नुहुनेछ । 7 | 8 | ### विषयहरु 9 | 10 | १. [डाटा विज्ञान को परिभाषा](01-defining-data-science/README.md) 11 | २. [डाटा विज्ञान नैतिकता](02-ethics/README.md) 12 | ३. [डाटा परिभाषा ](03-defining-data/README.md) 13 | ४. [Statistics र Probability को परिभाषा](04-stats-and-probability/README.md) 14 | 15 | ### विशेष धन्यवाद 16 | यी पाठहरु N [नित्य नरसिम्हन](https://twitter.com/nitya) र [दिमित्री सोश्निकोभ](https://twitter.com/shwars) द्वारा ❤️ का साथ लेखिएको हो। 17 | -------------------------------------------------------------------------------- /1-Introduction/translations/README.nl.md: -------------------------------------------------------------------------------- 1 | # Inleiding tot datawetenschap 2 | 3 | ![data in actie](images/data.jpg) 4 | > Beeld door Stephen Dawson op Unsplash 5 | 6 | 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. 7 | 8 | ### Onderwerpen 9 | 10 | 1. [Data Science definiëren](01-defining-data-science/README.md) 11 | 2. [Ethiek in Data Science](02-ethics/README.md) 12 | 3. [Data definiëren](03-defining-data/README.md) 13 | 4. [Inleiding tot statistiek en kansrekening](04-stats-and-probability/README.md) 14 | 15 | ### Credits 16 | 17 | Dit lesmateriaal is met liefde ❤️ geschreven door [Nitya Narasimhan](https://twitter.com/nitya) en [Dmitry Soshnikov](https://twitter.com/shwars). 18 | -------------------------------------------------------------------------------- /1-Introduction/translations/README.pt-br.md: -------------------------------------------------------------------------------- 1 | # Introdução a Ciência de Dados 2 | 3 | ![dados em ação](../images/data.jpg) 4 | > Foto por Stephen Dawson em Unsplash 5 | 6 | Nessas aulas, você irá descobrir como Ciência de Dados é definida e aprender sobre considerações éticas que devem ser consideradas por um cientista de dado. Você também irá aprender como dados são definidos e um pouco sobre estatística e probabilidade, os principais domínios acadêmicos da Ciência de Dados. 7 | 8 | ### Tópicos 9 | 10 | 1. [Definindo Ciência de Dados](01-defining-data-science/README.md) 11 | 2. [Ética da Ciência de Dados](02-ethics/README.md) 12 | 3. [Definindo Dados](03-defining-data/README.md) 13 | 4. [Introdução a Estatística e Probabilidade](04-stats-and-probability/README.md) 14 | 15 | ### Cŕeditos 16 | 17 | Essas aulas foram escritas com ❤️ por [Nitya Narasimhan](https://twitter.com/nitya) e [Dmitry Soshnikov](https://twitter.com/shwars). 18 | -------------------------------------------------------------------------------- /1-Introduction/translations/README.ru.md: -------------------------------------------------------------------------------- 1 | # Введение в науку о данных 2 | 3 | ![data in action](../images/data.jpg) 4 | > Photo by Stephen Dawson on Unsplash 5 | 6 | Пройдя данные уроки Вы узнаете, что такое наука о данных и изучите этические аспекты, которые должен учитывать каждый дата сайентист. Вы также узнаете, что такое данные и немного познакомитесь со статистикой и теорией вероятности, центральной областью науки о данных. 7 | 8 | ### Разделы 9 | 10 | 1. [Что такое наука о данных](01-defining-data-science/README.md) 11 | 2. [Этика и наука о данных](02-ethics/README.md) 12 | 3. [Что такое данные](03-defining-data/README.md) 13 | 4. [Введение в статистику и теорию вероятности](04-stats-and-probability/README.md) 14 | 15 | ### Благодарности 16 | 17 | Данные уроки были написаны с ❤️ [Nitya Narasimhan](https://twitter.com/nitya) и [Dmitry Soshnikov](https://twitter.com/shwars). 18 | -------------------------------------------------------------------------------- /1-Introduction/translations/README.zh-cn.md: -------------------------------------------------------------------------------- 1 | # 数据科学简介 2 | 3 | ![data in action](../images/data.jpg) 4 | > Photo by Stephen Dawson on Unsplash 5 | 6 | 在这些课程中，你将了解到数据科学是如何定义的，以及数据科学家必须考虑的伦理问题。你还将学习数据是如何定义的，还有一些关于统计学和概率的知识，这是数据科学的核心学术领域。 7 | 8 | ### 主题 9 | 10 | 1. [数据科学的定义](../01-defining-data-science/README.md) 11 | 2. [数据科学伦理](../02-ethics/README.md) 12 | 3. [数据的定义](../03-defining-data/README.md) 13 | 4. [统计与概率概论](../04-stats-and-probability/README.md) 14 | 15 | ### 致谢 16 | 17 | 这些课程由 [Nitya Narasimhan](https://twitter.com/nitya) 和 [Dmitry Soshnikov](https://twitter.com/shwars) 用 ❤️ 编写。 18 | -------------------------------------------------------------------------------- /2-Working-With-Data/05-relational-databases/airports.db: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/05-relational-databases/airports.db -------------------------------------------------------------------------------- /2-Working-With-Data/05-relational-databases/translations/README.md: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /2-Working-With-Data/06-non-relational/CocaColaCo.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/06-non-relational/CocaColaCo.xlsx -------------------------------------------------------------------------------- /2-Working-With-Data/06-non-relational/InventoryExample.xltm: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/06-non-relational/InventoryExample.xltm -------------------------------------------------------------------------------- /2-Working-With-Data/06-non-relational/PersonsData.json: -------------------------------------------------------------------------------- 1 | 2 | [ 3 | { 4 | "firstname": "Christophe", 5 | "age": 32 6 | }, 7 | { 8 | "firstname": "Prema", 9 | "age": 20 10 | }, 11 | { 12 | "firstname": "Arthur", 13 | "age": 15 14 | }, 15 | { 16 | "firstname": "Zoe", 17 | "age": 7 18 | }, 19 | { 20 | "firstname": "Keisha", 21 | "age": 84 22 | }, 23 | { 24 | "firstname": "Jackie", 25 | "age": 45 26 | } 27 | ] -------------------------------------------------------------------------------- /2-Working-With-Data/06-non-relational/assignment.md: -------------------------------------------------------------------------------- 1 | # Soda Profits 2 | 3 | ## Instructions 4 | 5 | The [Coca Cola Co spreadsheet](CocaColaCo.xlsx) is missing some calculations. Your task is to: 6 | 7 | 1. Calculate the Gross profits of FY '15, '16, '17, and '18 8 | - Gross Profit = Net Operating revenues - Cost of goods sold 9 | 1. Calculate the average of all the gross profits. Try to do this with a function. 10 | - Average = Sum of gross profits divided by the number of fiscal years (10) 11 | - Documentation on the [AVERAGE function](https://support.microsoft.com/en-us/office/average-function-047bac88-d466-426c-a32b-8f33eb960cf6) 12 | 1. This is an Excel file, but it should be editable in any spreadsheet platform 13 | 14 | [Data source credit to Yiyi Wang](https://www.kaggle.com/yiyiwang0826/cocacola-excel) 15 | 16 | ## Rubric 17 | 18 | Exemplary | Adequate | Needs Improvement 19 | --- | --- | -- | 20 | -------------------------------------------------------------------------------- /2-Working-With-Data/06-non-relational/images/columnar-db.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/06-non-relational/images/columnar-db.png -------------------------------------------------------------------------------- /2-Working-With-Data/06-non-relational/images/cosmosdb-emulator-explorer.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/06-non-relational/images/cosmosdb-emulator-explorer.png -------------------------------------------------------------------------------- /2-Working-With-Data/06-non-relational/images/cosmosdb-emulator-persons-query.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/06-non-relational/images/cosmosdb-emulator-persons-query.png -------------------------------------------------------------------------------- /2-Working-With-Data/06-non-relational/images/cosmosdb-emulator-persons.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/06-non-relational/images/cosmosdb-emulator-persons.png -------------------------------------------------------------------------------- /2-Working-With-Data/06-non-relational/images/document-db.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/06-non-relational/images/document-db.png -------------------------------------------------------------------------------- /2-Working-With-Data/06-non-relational/images/formula-excel.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/06-non-relational/images/formula-excel.png -------------------------------------------------------------------------------- /2-Working-With-Data/06-non-relational/images/function-excel.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/06-non-relational/images/function-excel.png -------------------------------------------------------------------------------- /2-Working-With-Data/06-non-relational/images/graph-db.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/06-non-relational/images/graph-db.png -------------------------------------------------------------------------------- /2-Working-With-Data/06-non-relational/images/kv-db.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/06-non-relational/images/kv-db.png -------------------------------------------------------------------------------- /2-Working-With-Data/06-non-relational/images/new-container.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/06-non-relational/images/new-container.png -------------------------------------------------------------------------------- /2-Working-With-Data/06-non-relational/images/parts-of-spreadsheet.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/06-non-relational/images/parts-of-spreadsheet.png -------------------------------------------------------------------------------- /2-Working-With-Data/06-non-relational/translations/assignment.hi.md: -------------------------------------------------------------------------------- 1 | # सोडा लाभ 2 | 3 | ## निर्देश 4 | 5 | [कोका कोला कंपनी स्प्रेडशीट](../CocaColaCo.xlsx) में कुछ गणनाएं नहीं हैं। आपका कार्य है: 6 | 7 | 1. वित्त वर्ष '15, '16, '17, और '18' के सकल लाभ की गणना करें 8 | - सकल लाभ = शुद्ध परिचालन राजस्व - बेची गई वस्तुओं की लागत 9 | 1. सभी सकल लाभ के औसत की गणना करें। इसे एक फ़ंक्शन के साथ करने का प्रयास करें। 10 | - औसत = वित्तीय वर्षों की संख्या से विभाजित सकल लाभ का योग (10) 11 | - [औसत फ़ंक्शन](https://support.microsoft.com/en-us/office/average-function-047bac88-d466-426c-a32b-8f33eb960cf6) पर दस्तावेज़ीकरण 12 | 1. यह एक एक्सेल फाइल है, लेकिन इसे किसी भी स्प्रेडशीट प्लेटफॉर्म में संपादित किया जा सकता है 13 | 14 | [यीयी वांग को डेटा स्रोत क्रेडिट](https://www.kaggle.com/yiyiwang0826/cocacola-excel) 15 | 16 | ## रूब्रिक 17 | 18 | अनुकरणीय | पर्याप्त | सुधार की जरूरत 19 | --- | --- | -- | 20 | -------------------------------------------------------------------------------- /2-Working-With-Data/07-python/images/covidspread.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/07-python/images/covidspread.png -------------------------------------------------------------------------------- /2-Working-With-Data/07-python/images/covidtreat.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/07-python/images/covidtreat.png -------------------------------------------------------------------------------- /2-Working-With-Data/07-python/images/dataframe-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/07-python/images/dataframe-1.png -------------------------------------------------------------------------------- /2-Working-With-Data/07-python/images/timeseries-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/07-python/images/timeseries-1.png -------------------------------------------------------------------------------- /2-Working-With-Data/07-python/images/timeseries-2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/07-python/images/timeseries-2.png -------------------------------------------------------------------------------- /2-Working-With-Data/07-python/images/timeseries-3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/07-python/images/timeseries-3.png -------------------------------------------------------------------------------- /2-Working-With-Data/07-python/images/video-ds-python.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/07-python/images/video-ds-python.png -------------------------------------------------------------------------------- /2-Working-With-Data/08-data-preparation/assignment.md: -------------------------------------------------------------------------------- 1 | # Evaluating Data from a Form 2 | 3 | A client has been testing a [small form](index.html) to gather some basic data about their client-base. They have brought their findings to you to validate the data they have gathered. You can open the `index.html` page in the browser to take a look at the form. 4 | 5 | You have been provided a [dataset of csv records](../../data/form.csv) that contain entries from the form as well as some basic visualizations. The client pointed out that some of the visualizations look incorrect but they're unsure about how to resolve them. You can explore it in the [assignment notebook](assignment.ipynb). 6 | 7 | ## Instructions 8 | 9 | Use the techniques in this lesson to make recommendations about the form so it captures accurate and consistent information. 10 | 11 | ## Rubric 12 | 13 | Exemplary | Adequate | Needs Improvement 14 | --- | --- | -- | 15 | -------------------------------------------------------------------------------- /2-Working-With-Data/08-data-preparation/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | Entry Form 9 | 10 | 11 | 12 |

Please Fill out the Form (* required)

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20 | 21 | 22 | 23 | -------------------------------------------------------------------------------- /2-Working-With-Data/08-data-preparation/translations/README.md: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /2-Working-With-Data/README.md: -------------------------------------------------------------------------------- 1 | # Working with Data 2 | 3 | ![data love](images/data-love.jpg) 4 | > Photo by Alexander Sinn on Unsplash 5 | 6 | In these lessons, you will learn some of the ways that data can be managed, manipulated, and used in applications. You will learn about relational and non-relational databases and how data can be stored in them. You'll learn the fundamentals of working with Python to manage data, and you'll discover some of the many ways that you can work with Python to manage and mine data. 7 | ### Topics 8 | 9 | 1. [Relational databases](05-relational-databases/README.md) 10 | 2. [Non-relational databases](06-non-relational/README.md) 11 | 3. [Working with Python](07-python/README.md) 12 | 4. [Preparing data](08-data-preparation/README.md) 13 | 14 | ### Credits 15 | 16 | These lessons were written with ❤️ by [Christopher Harrison](https://twitter.com/geektrainer), [Dmitry Soshnikov](https://twitter.com/shwars) and [Jasmine Greenaway](https://twitter.com/paladique) 17 | -------------------------------------------------------------------------------- /2-Working-With-Data/images/data-love.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/2-Working-With-Data/images/data-love.jpg -------------------------------------------------------------------------------- /2-Working-With-Data/translations/README.es.md: -------------------------------------------------------------------------------- 1 | # Trabajando con datos 2 | 3 | ![Amor por los datos](../images/data-love.jpg) 4 | > Fotografía de Alexander Sinn en Unsplash 5 | 6 | 7 | En estas lecciones, aprenderás algunas de las formas para manejar datos, también aprenderás a manipularlos y usarlos en aplicaciones. Aprendera sobre bases de datos relacionales y no relacionales así como también almacenar datos en estas. Veremos los fundamentos de Python para poder administrar datos y extraerlos. 8 | 9 | ### Temas 10 | 11 | 1. [Bases de datos relacionales](../05-relational-databases/translations/README.es.md) 12 | 2. [Bases de datos no relacionales](../06-non-relational/README.md) 13 | 3. [Trabajando con Python](../07-python/README.md) 14 | 4. [Preparando datos](../08-data-preparation/README.md) 15 | 16 | ### Créditos 17 | 18 | Estas lecciones fueron escritas con ❤️ por [Christopher Harrison](https://twitter.com/geektrainer), [Dmitry Soshnikov](https://twitter.com/shwars) y [Jasmine Greenaway](https://twitter.com/paladique) 19 | -------------------------------------------------------------------------------- /2-Working-With-Data/translations/README.fr.md: -------------------------------------------------------------------------------- 1 | # Travailler avec les données 2 | 3 | ![l'amour des données](images/data-love.jpg) 4 | > Photo par Alexander Sinn sur Unsplash 5 | 6 | Dans ces leçons, vous découvrirez quelques-unes des façons dont les données peuvent être gérées, manipulées et utilisées dans les applications. Vous découvrirez les bases de données relationnelles et non relationnelles et la manière dont les données peuvent y être stockées. Vous apprendrez les principes fondamentaux de l'utilisation de Python pour gérer les données et vous découvrirez quelques-unes des nombreuses façons de travailler avec Python pour gérer et exploiter les données. 7 | 8 | ### Thèmes 9 | 10 | 1. [Bases de données relationnelles](05-relational-databases/README.md) 11 | 2. [Bases de données non relationnelles](06-non-relational/README.md) 12 | 3. [Travailler avec Python](07-python/README.md) 13 | 4. [Préparation des données](08-data-preparation/README.md) 14 | 15 | ### Crédits 16 | 17 | Ces leçons ont été rédigées avec ❤️ par [Christopher Harrison](https://twitter.com/geektrainer), [Dmitry Soshnikov](https://twitter.com/shwars) et [Jasmine Greenaway](https://twitter.com/paladique) -------------------------------------------------------------------------------- /2-Working-With-Data/translations/README.hi.md: -------------------------------------------------------------------------------- 1 | # डेटा के साथ काम करना 2 | 3 | ![डेटा से प्यार](../images/data-love.jpg) 4 | > तस्वीर Alexander Sinn द्वारा Unsplash 5 | पर 6 | 7 | 8 | इन पाठों में, आप कुछ ऐसे तरीके सीखेंगे जिनसे डेटा को प्रबंधित, हेरफेर और अनुप्रयोगों में उपयोग किया जा सकता है। आप रिलेशनल और नॉन-रिलेशनल डेटाबेस के बारे में जानेंगे और उनमें डेटा कैसे स्टोर किया जा सकता है। आप डेटा को प्रबंधित करने के लिए पायथन के साथ काम करने के मूल सिद्धांतों को सीखेंगे, और आप कुछ ऐसे तरीकों की खोज करेंगे जिनसे आप डेटा को प्रबंधित करने और माइन करने के लिए पायथन के साथ काम कर सकते हैं। 9 | ### विषय 10 | 11 | 1. [संबंधपरक डेटाबेस](../05-relational-databases/README.md) 12 | 2. [[गैर-संबंधपरक डेटाबेस](../06-non-relational/README.md) 13 | 3. [पायथन के साथ काम करना](../07-python/README.md) 14 | 4. [डेटा तैयार करना](../08-data-preparation/README.md) 15 | 16 | ### क्रेडिट 17 | 18 | ये पाठ [क्रिस्टोफर हैरिसन](https://twitter.com/geektrainer), [दिमित्री सोशनिकोव](https://twitter.com/shwars) और [जैस्मीन ग्रीनवे](https://twitter.com/shwars) द्वारा ❤️ से लिखे गए थे। 19 | -------------------------------------------------------------------------------- /2-Working-With-Data/translations/README.ko.md: -------------------------------------------------------------------------------- 1 | # 데이터작업 2 | 3 | ![데이터 사랑](../images/data-love.jpg) 4 | > 촬영작가: Alexander Sinn on Unsplash 5 | 6 | 이 수업에서는 응용 프로그램에서 데이터를 관리, 조작 및 사용할 수 있는 여러 방법에 대해 배웁니다. 또한 관계형 및 비관계형 데이터베이스에 대해 배우고 데이터가 이러한 데이터베이스에 어떻게 저장되는지 배웁니다. 파이썬으로 데이터를 다루는 기본 원리를 배우며, 이를 통해 데이터를 관리하고 마이닝(data mining) 할 수 있는 다양한 방법을 발견할 수 있을 것입니다. 7 | 8 | ### 주제 9 | 10 | 1. [관계형 데이터베이스](../05-relational-databases/translations/README.ko.md) 11 | 2. [비관계형 데이터베이스](../06-non-relational/translations/README.ko.md) 12 | 3. [Python 활용하기](../07-python/translations/README.ko.md) 13 | 4. [데이터 준비](../08-data-preparation/translations/README.ko.md) 14 | 15 | ### 크레딧 16 | 17 | 강의를 제작한 분: [Christopher Harrison](https://twitter.com/geektrainer), [Dmitry Soshnikov](https://twitter.com/shwars) 와 [Jasmine Greenaway](https://twitter.com/paladique) -------------------------------------------------------------------------------- /2-Working-With-Data/translations/README.ne.md: -------------------------------------------------------------------------------- 1 | # डाटासँग सहकार्य 2 | 3 | ![डाटा माया](images/data-love.jpg) 4 | > फोटो Alexander Sinn द्वारा Unsplash बाट 5 | 6 | यी पाठहरुमा, तपाइँ केहि तरिकाहरु डाटा को प्रबंधन, हेरफेर, र अनुप्रयोगहरुमा प्रयोग गर्न सकिन्छ को बारे मा जान्न सक्नुहुन्छ। तपाइँ रिलेशनल र नन-रिलेशनल डाटाबेस को बारे मा र कसरी डाटा उनीहरुमा भण्डारण गर्न सकिन्छ जान्नुहुनेछ। तपाइँ डेटा को प्रबन्ध गर्न को लागी Python संग काम गर्ने आधारभूत कुराहरु सिक्नुहुनेछ, र तपाइँ Python द्वारा डाटा माइन साथै म्यानेज गर्ने धेरै मध्य केहि तरिकाहरु पत्ता लगाउन सक्नुहुनेछ । 7 | ### बिषयहरु 8 | 9 | १. [रेलशनल डाटाबेस](05-relational-databases/README.md) 10 | २. [नन रेलशनल डाटाबेस](06-non-relational/README.md) 11 | ३. [Python सँग सहकार्य](07-python/README.md) 12 | ४. [डाटा को तयारी](08-data-preparation/README.md) 13 | 14 | ### बिषेष धन्यवाद 15 | यी पाठहरू ❤️ का साथ [Christopher Harrison](https://twitter.com/geektrainer), [Dmitry Soshnikov](https://twitter.com/shwars) र [Jasmine Greenaway](https://twitter.com/paladique) द्वारा लेखिएको हो । 16 | -------------------------------------------------------------------------------- /2-Working-With-Data/translations/README.nl.md: -------------------------------------------------------------------------------- 1 | # Werken met gegevens 2 | 3 | ![data love](images/data-love.jpg) 4 | > Beeld door Alexander Sinn op Unsplash 5 | 6 | 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. 7 | ### Onderwerpen 8 | 9 | 1. [Relationele databases](05-relational-databases/README.md) 10 | 2. [Niet-relationale databases](06-non-relational/README.md) 11 | 3. [Aan de slag met Python](07-python/README.md) 12 | 4. [Data voorbereiden](08-data-preparation/README.md) 13 | 14 | ### Credits 15 | 16 | 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) 17 | -------------------------------------------------------------------------------- /2-Working-With-Data/translations/README.ru.md: -------------------------------------------------------------------------------- 1 | # Работа с данными 2 | 3 | ![data love](../images/data-love.jpg) 4 | > Photo by Alexander Sinn on Unsplash 5 | 6 | На этих уроках Вы изучите способы управления данными, методы работы с ними и как данные могут быть использованы в приложениях. Вы познакомитесь с реляционными и нереляционными базами данных и с тем, как они хранят данные. Вы овладеете основами обработки данных при помощи языка программирования Python. 7 | 8 | ### Разделы 9 | 10 | 1. [Реляционные базы данных](05-relational-databases/README.md) 11 | 2. [Нереляционные базы данных](06-non-relational/README.md) 12 | 3. [Работа с языком программирования Python](07-python/README.md) 13 | 4. [Подготовка данных](08-data-preparation/README.md) 14 | 15 | ### Благодарности 16 | 17 | Данные уроки были написаны с ❤️ [Christopher Harrison](https://twitter.com/geektrainer), [Dmitry Soshnikov](https://twitter.com/shwars) и [Jasmine Greenaway](https://twitter.com/paladique) 18 | -------------------------------------------------------------------------------- /2-Working-With-Data/translations/README.zh-cn.md: -------------------------------------------------------------------------------- 1 | # 处理数据 2 | 3 | ![data love](../images/data-love.jpg) 4 | > 摄影者 Alexander Sinn 于 Unsplash 5 | 6 | 在这些课程中, 你将学习到一些关于数据管理、数据操作和应用的方式。你将学习关系和非关系数据库，以及数据如何存储在他们中。你将学习 Python 语言的基础知识，同时还会发现一些使用 Python 来管理和挖掘数据的方式。 7 | 8 | ### 话题 9 | 10 | 1. [关系数据库](../05-relational-databases/README.md) 11 | 2. [非关系数据库](../06-non-relational/README.md) 12 | 3. [使用 Python](../07-python/README.md) 13 | 4. [准备数据](../08-data-preparation/README.md) 14 | 15 | ### 致谢 16 | 17 | 这些课程由 [Christopher Harrison](https://twitter.com/geektrainer)， [Dmitry Soshnikov](https://twitter.com/shwars) 和 [Jasmine Greenaway](https://twitter.com/paladique) 用 ❤️ 编写 18 | -------------------------------------------------------------------------------- /3-Data-Visualization/09-visualization-quantities/assignment.md: -------------------------------------------------------------------------------- 1 | # Lines, Scatters and Bars 2 | 3 | ## Instructions 4 | 5 | In this lesson, you worked with line charts, scatterplots, and bar charts to show interesting facts about this dataset. In this assignment, dig deeper into the dataset to discover a fact about a given type of bird. For example, create a notebook visualizing all the interesting data you can uncover about Snow Geese. 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"interpreter": { 30 | "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" 31 | } 32 | }, 33 | "nbformat": 4, 34 | "nbformat_minor": 2 35 | } -------------------------------------------------------------------------------- /3-Data-Visualization/09-visualization-quantities/translations/assignment.es.md: -------------------------------------------------------------------------------- 1 | # Líneas, Dispersiones y Barras 2 | 3 | ## Instrucciones 4 | 5 | En esta lección, has trabajado con gráficos de líneas, gráficos de dispersión y gráficos de barras para mostrar hechos interesantes sobre este conjunto de datos. En esta asignación, profundiza en el conjunto de datos para descubrir un hecho sobre un tipo de ave determinado. Por ejemplo, crea un cuaderno que visualice todos los datos interesantes que puedas descubrir sobre los gansos de nieve. Utiliza los tres gráficos mencionados anteriormente para contar una historia en tu cuaderno. 6 | 7 | ## Rúbrica 8 | 9 | Ejemplar | Adecuado | Necesita mejorar 10 | --- | --- | -- | 11 | El cuaderno se presenta con buenas anotaciones, una narración sólida y gráficos atractivos | Al cuaderno le falta uno de estos elementos | Al cuaderno le faltan dos de estos elementos -------------------------------------------------------------------------------- /3-Data-Visualization/09-visualization-quantities/translations/assignment.ko.md: -------------------------------------------------------------------------------- 1 | # 선, 산점도, 막대 그래프 2 | 3 | ## 지침 4 | 5 | 이 강의에서는 선형 차트, 산점도 및 막대형 차트를 사용하여 이 데이터 셋에 대한 흥미로운 사실을 보여 주었습니다. 이 과제에서는 데이터셋을 자세히 조사하여 특정 유형의 새에 대한 사실을 발견하는 과정을 진행합니다. 예를 들어, 흰기러기(Snow Geese) 에 대한 모든 흥미로운 데이터를 시각화하는 노트북을 만드는 것이 있습니다. 위에서 언급한 세 가지의 플롯을 사용하여 여러분의 노트북을 만들어보세요. 6 | 7 | ## 기준표 8 | 9 | 모범적인 | 적당한 | 개선 필요 10 | --- | --- | -- | 11 | 좋은 주석처리, 탄탄한 내용, 매력적인 그래프로 노트북 작성 | 노트북에 다음 요소 중 하나가 없습니다. | 노트북에 요소 중에 두 가지가 없습니다. 12 | -------------------------------------------------------------------------------- /3-Data-Visualization/09-visualization-quantities/translations/assignment.ne.md: -------------------------------------------------------------------------------- 1 | # रेखाहरू, स्क्याटरहरू र बारहरू 2 | 3 | ## निर्देशनहरू 4 | 5 | यस पाठमा, तपाईंले यस डेटा सेटको बारेमा रोचक तथ्यहरू देखाउन लाइन चार्टहरू, स्क्याटर चार्टहरू, र बार चार्टहरूसँग काम गर्नुभएको छ। यस असाइनमेन्टमा, तपाइँ एक विशेष प्रकारको चराको बारेमा तथ्य पत्ता लगाउन आफ्नो डेटा सेटमा ड्रिल डाउन गर्नुहुन्छ। उदाहरणका लागि, एउटा नोटबुक सिर्जना गर्नुहोस् जुन तपाईंले स्नो गिजको बारेमा पत्ता लगाउन सक्ने सबै रोचक तथ्यहरू प्रदर्शन गर्दछ। तपाईंको नोटबुकमा कथा बताउन माथि उल्लेखित तीनवटा चार्टहरू प्रयोग गर्नुहोस्। 6 | 7 | ## रुब्रिक 8 | 9 | अनुकरणीय | पर्याप्त | सुधार चाहिन्छ 10 | --- | --- | - | 11 | नोटबुक राम्रो एनोटेसन, बलियो कथन र आकर्षक ग्राफिक्स संग प्रस्तुत गरिएको छ | नोटबुकमा यी वस्तुहरू मध्ये एउटा हराइरहेको छ | नोटबुकमा यी दुई वस्तुहरू हराइरहेका छन् | -------------------------------------------------------------------------------- /3-Data-Visualization/09-visualization-quantities/translations/assignment.pt-br.md: -------------------------------------------------------------------------------- 1 | # Linhas, dispersão e barras 2 | 3 | ## Instruções 4 | 5 | Nesta aula, você trabalhou com gráficos de linhas, dispersão e barras para mostrar fatos interessantes sobre este dataset. Nesta tarefa, explore o mesmo dataset mais a fundo para descobrir algo sobre um dado tipo de ave. Por exemplo, crie um notebook que mostre visualizações de todos os fatos interessantes que encontrar sobre os Snow Geese (gansos-das-neves). Use os três tipos de gráficos mencionados anteriormente para contar uma história em seu notebook. 6 | 7 | ## Rubrica 8 | 9 | Exemplar | Adequado | Precisa melhorar 10 | --- | --- | -- | 11 | O notebook foi apresentado com boas anotações, contação de histórias (storytelling) sólida e gráficos cativantes | O notebook não tem um desses elementos | O notebook não tem dois desses elementos 12 | -------------------------------------------------------------------------------- /3-Data-Visualization/10-visualization-distributions/assignment.md: -------------------------------------------------------------------------------- 1 | # Apply your skills 2 | 3 | ## Instructions 4 | 5 | So far, you have worked with the Minnesota birds dataset to discover information about bird quantities and population density. Practice your application of these techniques by trying a different dataset, perhaps sourced from [Kaggle](https://www.kaggle.com/). 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"orig_nbformat": 4, 13 | "language_info": { 14 | "name": "python" 15 | } 16 | }, 17 | "nbformat": 4, 18 | "nbformat_minor": 2 19 | } -------------------------------------------------------------------------------- /3-Data-Visualization/10-visualization-distributions/translations/assignment.es.md: -------------------------------------------------------------------------------- 1 | # Aplica tus habilidades 2 | 3 | ## Instrucciones 4 | 5 | Hasta ahora, has trabajado con el conjunto de datos de aves de Minnesota para descubrir información sobre las cantidades de aves y la densidad de población. Practica tu aplicación de estas técnicas probando un conjunto de datos diferente, quizás procedente de [Kaggle](https://www.kaggle.com/). Construye un cuaderno para contar una historia sobre este conjunto de datos, y asegúrate de utilizar histogramas al hablar de él. 6 | ## Rúbrica 7 | 8 | Ejemplar | Adecuado | Necesita mejorar 9 | --- | --- | -- | 10 | Un cuaderno se presenta con anotaciones sobre este conjunto de datos, incluyendo su fuente, y utiliza al menos 5 histogramas para descubrir hechos sobre los datos. | Un cuaderno se presenta con anotaciones incompletas o con errores. | Un cuaderno se presenta sin anotaciones e incluye errores. -------------------------------------------------------------------------------- /3-Data-Visualization/10-visualization-distributions/translations/assignment.ko.md: -------------------------------------------------------------------------------- 1 | # 기술 적용해보기 2 | 3 | ## 지시사항 4 | 5 | 지금까지 새의 양과 개체 밀도에 대한 정보를 찾기 위해서 미네소타 새 데이터셋으로 작업하였습니다. [Kaggle](https://www.kaggle.com/)에서 제공하는 다른 데이터셋을 사용하여 이러한 기술 적용을 연습해보세요. 이 데이터셋에 대해서 알려줄 수 있는 노트북을 만들고, 논의할 때 히스토그램을 사용하세요. 6 | ## 채점기준표 7 | 8 | 모범 | 충분 | 개선 필요 9 | --- | --- | -- | 10 | 노트북은 출처를 포함하여 이 데이터셋에 대한 주석이 제공되며, 데이터에 대한 사실을 발견하기 위해서 최소 5개의 히스토그램을 사용합니다. | 노트북은 불완전한 주석이나 버그가 표시됩니다. | 노트북은 주석 없이 표시되며 버그가 포함되어 있습니다. -------------------------------------------------------------------------------- /3-Data-Visualization/10-visualization-distributions/translations/assignment.pt-br.md: -------------------------------------------------------------------------------- 1 | # Aplique seus conhecimentos 2 | 3 | ## Instruções 4 | 5 | Até agora, você trabalhou com o dataset de aves de Minnesota para descobrir informação sobre quantidades de aves e densidade populacional. Pratique essas técnicas usando outro dataset, talvez do [Kaggle](https://www.kaggle.com/). Faça um notebook que conta uma história sobre esse dataset, e lembre-se de usar histogramas para isso. 6 | 7 | ## Rubrica 8 | 9 | Exemplar | Adequado | Precisa melhorar 10 | --- | --- | -- | 11 | O notebook tem anotações sobre o dataset, incluindo sua origem, e usa pelo menos 5 histogramas para descobrir fatos sobre os dados. | O notebook tem anotações incompletas ou bugs | O notebook não possui nenhuma anotação e contṕem bugs. 12 | -------------------------------------------------------------------------------- /3-Data-Visualization/11-visualization-proportions/assignment.md: -------------------------------------------------------------------------------- 1 | # Try it in Excel 2 | 3 | ## Instructions 4 | 5 | Did you know you can create donut, pie, and waffle charts in Excel? Using a dataset of your choice, create these three charts right in an Excel spreadsheet. 6 | 7 | ## Rubric 8 | 9 | | Exemplary | Adequate | Needs Improvement | 10 | | ------------------------------------------------------- | ------------------------------------------------- | ------------------------------------------------------ | 11 | | An Excel spreadsheet is presented with all three charts | An Excel spreadsheet is presented with two charts | An Excel spreadsheet is presented with only one chart | -------------------------------------------------------------------------------- /3-Data-Visualization/11-visualization-proportions/images/donut-wb.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/11-visualization-proportions/images/donut-wb.png -------------------------------------------------------------------------------- /3-Data-Visualization/11-visualization-proportions/images/donut.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/11-visualization-proportions/images/donut.png -------------------------------------------------------------------------------- /3-Data-Visualization/11-visualization-proportions/images/pie1-wb.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/11-visualization-proportions/images/pie1-wb.png -------------------------------------------------------------------------------- /3-Data-Visualization/11-visualization-proportions/images/pie1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/11-visualization-proportions/images/pie1.png -------------------------------------------------------------------------------- /3-Data-Visualization/11-visualization-proportions/images/waffle.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/11-visualization-proportions/images/waffle.png -------------------------------------------------------------------------------- /3-Data-Visualization/11-visualization-proportions/notebook.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "source": [ 6 | "# 🍄 Mushroom Proportions" 7 | ], 8 | "metadata": {} 9 | } 10 | ], 11 | "metadata": { 12 | "orig_nbformat": 4, 13 | "language_info": { 14 | "name": "python" 15 | } 16 | }, 17 | "nbformat": 4, 18 | "nbformat_minor": 2 19 | } -------------------------------------------------------------------------------- /3-Data-Visualization/11-visualization-proportions/translations/assignment.es.md: -------------------------------------------------------------------------------- 1 | ## Pruébalo en Excel 2 | 3 | ## Instrucciones 4 | 5 | ¿Sabías que puedes crear gráficos de donas, pastel y waffle en Excel? Utilizando un conjunto de datos de su elección, crea estos tres gráficos directamente en una hoja de cálculo de Excel. 6 | 7 | ## Rúbrica 8 | 9 | | Ejemplar | Adecuado | Necesita mejorar 10 | | ------------------------------------------------------- | ------------------------------------------------- | ------------------------------------------------------ | 11 | | Una hoja de cálculo de Excel se presenta con los tres gráficos | Una hoja de cálculo de Excel se presenta con dos gráficos | Una hoja de cálculo de Excel se presenta con un solo gráfico | -------------------------------------------------------------------------------- /3-Data-Visualization/11-visualization-proportions/translations/assignment.zh-cn.md: -------------------------------------------------------------------------------- 1 | # 在 Excel 中试试 2 | 3 | ## 指示 4 | 5 | 你知道在 Excel 中可以创建圆环图、饼图和华夫饼图吗？使用你选择的数据集，直接在 Excel 电子表格中创建这三种图表。 6 | 7 | ## 评分表 8 | 9 | | 优秀 | 一般 | 需要改进 | 10 | | ----------------------- | ------------------------ | ---------------------- | 11 | | 在 Excel 中制作了三种图表 | 在 Excel 中制作了两种图表 | 在 Excel 中只制作了一种图表 | 12 | -------------------------------------------------------------------------------- /3-Data-Visualization/12-visualization-relationships/assignment.md: -------------------------------------------------------------------------------- 1 | # Dive into the beehive 2 | 3 | ## Instructions 4 | 5 | In this lesson you started looking at a dataset around bees and their honey production over a period of time that saw losses in the bee colony population overall. Dig deeper into this dataset and build a notebook that can tell the story of the health of the bee population, state by state and year by year. Do you discover anything interesting about this dataset? 6 | 7 | ## Rubric 8 | 9 | | Exemplary | Adequate | Needs Improvement | 10 | | ------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------- | ---------------------------------------- | 11 | | A notebook is presented with a story annotated with at least three different charts showing aspects of the dataset, state over state and year over year | The notebook lacks one of these elements | The notebook lacks two of these elements | -------------------------------------------------------------------------------- /3-Data-Visualization/12-visualization-relationships/images/dual-line.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/12-visualization-relationships/images/dual-line.png -------------------------------------------------------------------------------- /3-Data-Visualization/12-visualization-relationships/images/facet.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/12-visualization-relationships/images/facet.png -------------------------------------------------------------------------------- /3-Data-Visualization/12-visualization-relationships/images/line1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/12-visualization-relationships/images/line1.png -------------------------------------------------------------------------------- /3-Data-Visualization/12-visualization-relationships/images/line2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/12-visualization-relationships/images/line2.png -------------------------------------------------------------------------------- /3-Data-Visualization/12-visualization-relationships/images/scatter1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/12-visualization-relationships/images/scatter1.png -------------------------------------------------------------------------------- /3-Data-Visualization/12-visualization-relationships/images/scatter2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/12-visualization-relationships/images/scatter2.png -------------------------------------------------------------------------------- /3-Data-Visualization/12-visualization-relationships/images/scatter3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/12-visualization-relationships/images/scatter3.png -------------------------------------------------------------------------------- /3-Data-Visualization/12-visualization-relationships/notebook.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "source": [ 6 | "# Visualizing Honey Production 🍯 🐝" 7 | ], 8 | "metadata": {} 9 | } 10 | ], 11 | "metadata": { 12 | "orig_nbformat": 4, 13 | "language_info": { 14 | "name": "python" 15 | } 16 | }, 17 | "nbformat": 4, 18 | "nbformat_minor": 2 19 | } -------------------------------------------------------------------------------- /3-Data-Visualization/12-visualization-relationships/translations/assignment.es.md: -------------------------------------------------------------------------------- 1 | # Sumérgete en la colmena 2 | 3 | ## Instrucciones 4 | 5 | En esta lección has empezado a estudiar un conjunto de datos sobre las abejas y su producción de miel durante un periodo de tiempo en el que se han producido pérdidas en la población de colonias de abejas en general. Profundiza en este conjunto de datos y construye un cuaderno que pueda contar la historia de la salud de la población de abejas, estado por estado y año por año. ¿Descubres algo interesante en este conjunto de datos? 6 | 7 | ## Rúbrica 8 | 9 | | Ejemplar | Adecuado | Necesita mejorar 10 | | ------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------- | ---------------------------------------- | 11 | | El cuaderno se presenta con una historia anotada con al menos tres gráficos diferentes que muestran aspectos del conjunto de datos, estado sobre estado y año sobre año | El cuaderno carece de uno de estos elementos | El cuaderno carece de dos de estos elementos -------------------------------------------------------------------------------- /3-Data-Visualization/12-visualization-relationships/translations/assignment.ko.md: -------------------------------------------------------------------------------- 1 | # 벌집 탐구하기 2 | 3 | ## 지시사항 4 | 5 | 이 수업에서는 벌 군집 개체수가 전반적으로 감소한 기간 동안의 벌과 벌들의 꿀 생산량에 대한 데이터셋을 살펴보기 시작했습니다. 이 데이터셋을 자세히 살펴보고 주별, 연도별 벌 개체군의 건강에 대해서 알려줄 수 있는 노트북을 만드세요. 6 | 7 | ## 채점기준표 8 | 9 | | 모범 | 충분 | 개선 필요 | 10 | | ------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------- | ---------------------------------------- | 11 | | 노트북은 데이터셋의 양상, 주별 상태 및 연도별을 보여주는 최소 3개의 다른 차트로 주석이 달린 프로그램을 제공됩니다. | 노트북에는 이러한 요소 중 하나가 없습니다. | 노트북에는 이러한 요소 중 두 가지가 없습니다. | -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/assignment.md: -------------------------------------------------------------------------------- 1 | # Build your own custom vis 2 | 3 | ## Instructions 4 | 5 | Using the code sample in this project to create a social network, mock up data of your own social interactions. You could map your usage of social media or make a diagram of your family members. Create an interesting web app that shows a unique visualization of a social network. 6 | ## Rubric 7 | 8 | Exemplary | Adequate | Needs Improvement 9 | --- | --- | -- | 10 | A GitHub repo is presented with code that runs properly (try deploying it as a static web app) and has an annotated README explaining the project | The repo does not run properly or is not documented well | The repo does not run properly and is not documented well -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/images/3d.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/13-meaningful-visualizations/images/3d.png -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-1.png -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-2.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-2.jpg -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-3.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-3.jpg -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-4.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-4.jpg -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/images/busing.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/13-meaningful-visualizations/images/busing.png -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/images/liaisons.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/13-meaningful-visualizations/images/liaisons.png -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/images/tornado.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/13-meaningful-visualizations/images/tornado.png -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/solution/.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | node_modules 3 | /dist 4 | 5 | 6 | # local env files 7 | .env.local 8 | .env.*.local 9 | 10 | # Log files 11 | npm-debug.log* 12 | yarn-debug.log* 13 | yarn-error.log* 14 | pnpm-debug.log* 15 | 16 | # Editor directories and files 17 | .idea 18 | .vscode 19 | *.suo 20 | *.ntvs* 21 | *.njsproj 22 | *.sln 23 | *.sw? 24 | -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/solution/README.md: -------------------------------------------------------------------------------- 1 | # Dangerous Liaisons data visualization project 2 | 3 | To get started, you need to ensure that you have NPM and Node running on your machine. Install the dependencies (npm install) and then run the project locally (npm run serve): 4 | 5 | ## Project setup 6 | ``` 7 | npm install 8 | ``` 9 | 10 | ### Compiles and hot-reloads for development 11 | ``` 12 | npm run serve 13 | ``` 14 | 15 | ### Compiles and minifies for production 16 | ``` 17 | npm run build 18 | ``` 19 | 20 | ### Lints and fixes files 21 | ``` 22 | npm run lint 23 | ``` 24 | 25 | ### Customize configuration 26 | See [Configuration Reference](https://cli.vuejs.org/config/). 27 | -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/solution/babel.config.js: -------------------------------------------------------------------------------- 1 | module.exports = { 2 | presets: [ 3 | '@vue/cli-plugin-babel/preset' 4 | ] 5 | } 6 | -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/solution/package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "liaisons", 3 | "version": "0.1.0", 4 | "private": true, 5 | "scripts": { 6 | "serve": "vue-cli-service serve", 7 | "build": "vue-cli-service build", 8 | "lint": "vue-cli-service lint" 9 | }, 10 | "dependencies": { 11 | "core-js": "^3.6.5", 12 | "vue": "^2.6.11", 13 | "vue-d3-network": "0.1.28" 14 | }, 15 | "devDependencies": { 16 | "@vue/cli-plugin-babel": "~5.0.8", 17 | "@vue/cli-plugin-eslint": "~5.0.8", 18 | "@vue/cli-service": "~5.0.8", 19 | "babel-eslint": "^10.1.0", 20 | "eslint": "^6.7.2", 21 | "eslint-plugin-vue": "^6.2.2", 22 | "vue-template-compiler": "^2.6.11" 23 | }, 24 | "eslintConfig": { 25 | "root": true, 26 | "env": { 27 | "node": true 28 | }, 29 | "extends": [ 30 | "plugin:vue/essential", 31 | "eslint:recommended" 32 | ], 33 | "parserOptions": { 34 | "parser": "babel-eslint" 35 | }, 36 | "rules": {} 37 | }, 38 | "browserslist": [ 39 | "> 1%", 40 | "last 2 versions", 41 | "not dead" 42 | ] 43 | } 44 | -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/solution/public/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/13-meaningful-visualizations/solution/public/favicon.ico -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/solution/public/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | Les Liaisons Dangereuses: Visualization 9 | 10 | 11 | 17 |
18 | 19 | 20 | 21 | -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/solution/src/App.vue: -------------------------------------------------------------------------------- 1 | 6 | 7 | 17 | 18 | -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/solution/src/main.js: -------------------------------------------------------------------------------- 1 | import Vue from "vue"; 2 | import App from "./App.vue"; 3 | 4 | Vue.config.productionTip = false; 5 | 6 | new Vue({ 7 | render: h => h(App) 8 | }).$mount("#app"); 9 | -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/starter/.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | node_modules 3 | /dist 4 | 5 | 6 | # local env files 7 | .env.local 8 | .env.*.local 9 | 10 | # Log files 11 | npm-debug.log* 12 | yarn-debug.log* 13 | yarn-error.log* 14 | pnpm-debug.log* 15 | 16 | # Editor directories and files 17 | .idea 18 | .vscode 19 | *.suo 20 | *.ntvs* 21 | *.njsproj 22 | *.sln 23 | *.sw? 24 | -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/starter/README.md: -------------------------------------------------------------------------------- 1 | # Dangerous Liaisons data visualization project 2 | 3 | To get started, you need to ensure that you have NPM and Node running on your machine. Install the dependencies (npm install) and then run the project locally (npm run serve): 4 | 5 | ## Project setup 6 | ``` 7 | npm install 8 | ``` 9 | 10 | ### Compiles and hot-reloads for development 11 | ``` 12 | npm run serve 13 | ``` 14 | 15 | ### Compiles and minifies for production 16 | ``` 17 | npm run build 18 | ``` 19 | 20 | ### Lints and fixes files 21 | ``` 22 | npm run lint 23 | ``` 24 | 25 | ### Customize configuration 26 | See [Configuration Reference](https://cli.vuejs.org/config/). 27 | -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/starter/babel.config.js: -------------------------------------------------------------------------------- 1 | module.exports = { 2 | presets: [ 3 | '@vue/cli-plugin-babel/preset' 4 | ] 5 | } 6 | -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/starter/package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "liaisons", 3 | "version": "0.1.0", 4 | "private": true, 5 | "scripts": { 6 | "serve": "vue-cli-service serve", 7 | "build": "vue-cli-service build", 8 | "lint": "vue-cli-service lint" 9 | }, 10 | "dependencies": { 11 | "core-js": "^3.6.5", 12 | "vue": "^3.5.16", 13 | "vue-d3-network": "0.1.28" 14 | }, 15 | "devDependencies": { 16 | "@vue/cli-plugin-babel": "~5.0.8", 17 | "@vue/cli-plugin-eslint": "~5.0.8", 18 | "@vue/cli-service": "~5.0.8", 19 | "babel-eslint": "^10.1.0", 20 | "eslint": "^6.7.2", 21 | "eslint-plugin-vue": "^6.2.2", 22 | "vue-template-compiler": "^2.6.11" 23 | }, 24 | "eslintConfig": { 25 | "root": true, 26 | "env": { 27 | "node": true 28 | }, 29 | "extends": [ 30 | "plugin:vue/essential", 31 | "eslint:recommended" 32 | ], 33 | "parserOptions": { 34 | "parser": "babel-eslint" 35 | }, 36 | "rules": {} 37 | }, 38 | "browserslist": [ 39 | "> 1%", 40 | "last 2 versions", 41 | "not dead" 42 | ] 43 | } 44 | -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/starter/public/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/3-Data-Visualization/13-meaningful-visualizations/starter/public/favicon.ico -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/starter/public/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | Les Liaisons Dangereuses: Visualization 9 | 10 | 11 | 17 |
18 | 19 | 20 | 21 | -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/starter/src/App.vue: -------------------------------------------------------------------------------- 1 | 6 | 7 | 17 | 18 | -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/starter/src/main.js: -------------------------------------------------------------------------------- 1 | import Vue from "vue"; 2 | import App from "./App.vue"; 3 | 4 | Vue.config.productionTip = false; 5 | 6 | new Vue({ 7 | render: h => h(App) 8 | }).$mount("#app"); 9 | -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/translations/assignment.es.md: -------------------------------------------------------------------------------- 1 | # Construye tu propio vis a medida 2 | 3 | ## Instrucciones 4 | 5 | Utilizando el ejemplo de código de este proyecto para crear una red social, maqueta los datos de tus propias interacciones sociales. Podrías mapear tu uso de las redes sociales o hacer un diagrama de los miembros de tu familia. Crea una aplicación web interesante que muestre una visualización única de una red social. 6 | ## Rúbrica 7 | 8 | Ejemplar | Adecuado | Necesita mejorar 9 | --- | --- | -- | 10 | Un repo de GitHub se presenta con código que se ejecuta correctamente (intenta desplegarlo como una aplicación web estática) y tiene un README anotado que explica el proyecto | El repo no se ejecuta correctamente o no está bien documentado | El repo no se ejecuta correctamente y no está bien documentado -------------------------------------------------------------------------------- /3-Data-Visualization/13-meaningful-visualizations/translations/assignment.hi.md: -------------------------------------------------------------------------------- 1 | # अपना खुद का कस्टम दृश्य बनाएं 2 | 3 | ## निर्देश 4 | 5 | एक सामाजिक नेटवर्क बनाने के लिए इस परियोजना में कोड नमूने का उपयोग करते हुए, अपने स्वयं के सामाजिक इंटरैक्शन के डेटा का मजाक उड़ाएं। आप सोशल मीडिया के अपने उपयोग को मैप कर सकते हैं या अपने परिवार के सदस्यों का आरेख बना सकते हैं। एक दिलचस्प वेब ऐप बनाएं जो सोशल नेटवर्क का एक अनूठा विज़ुअलाइज़ेशन दिखाता है। 6 | ## रूब्रिक 7 | 8 | अनुकरणीय | पर्याप्त | सुधार की जरूरत 9 | --- | --- | -- | 10 | एक गिटहब रेपो कोड के साथ प्रस्तुत किया जाता है जो ठीक से चलता है (इसे एक स्थिर वेब ऐप के रूप में तैनात करने का प्रयास करें) और परियोजना को समझाते हुए एक एनोटेट रीडमे है | रेपो ठीक से नहीं चलता है या अच्छी तरह से प्रलेखित नहीं है | रेपो ठीक से नहीं चलता है और अच्छी तरह से प्रलेखित नहीं है -------------------------------------------------------------------------------- /3-Data-Visualization/R/09-visualization-quantities/assignment.md: -------------------------------------------------------------------------------- 1 | # Lines, Scatters and Bars 2 | 3 | ## Instructions 4 | 5 | In this lesson, you worked with line charts, scatterplots, and bar charts to show interesting facts about this dataset. 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이 다섯 개의 수업에서는 자연에서 얻은 데이터를 탐색하고, 다양한 기술을 사용하여 흥미롭고 아름다운 시각화를 만들어봅시다. 10 | ### 주제 11 | 12 | 1. [수량 시각화](../09-visualization-quantities/README.md) 13 | 2. [분포 시각화](../10-visualization-distributions/README.md) 14 | 3. [비율 시각화](../11-visualization-proportions/README.md) 15 | 4. [관계 시각화](../12-visualization-relationships/README.md) 16 | 5. [의미있는 시각화 만들기](../13-meaningful-visualizations/README.md) 17 | 18 | ### 크레딧 19 | 20 | 강의를 만드신 분: [Jen Looper](https://twitter.com/jenlooper) 21 | 22 | 🍯 미국 꿀 생산에 대한 데이터는 [Kaggle](https://www.kaggle.com/jessicali9530/honey-production)의 Jessica Li의 프로젝트에서 제공되는 것입니다. 이 [데이터](https://usda.library.cornell.edu/concern/publications/rn301137d)는 [미국 농무부](https://www.nass.usda.gov/About_NASS/index.php)에서 만들어졌습니다. 23 | 24 | 🍄 버섯에 대한 데이터 역시 [Kaggle](https://www.kaggle.com/hatterasdunton/mushroom-classification-updated-dataset)에서 제공되었고, Hatteras Dunton이 수정했습니다. 이 데이터 셋에는 Agaricus 및 Lepiota 과에 속하는 23종의 주름 버섯목에 해당하는 가상 샘플에 대한 설명이 포함되어 있습니다. 버섯에 대한 정보는 'The Audubon Society Field Guide to North American Mushrooms(1981)'에서 발췌했습니다. 이 데이터 셋은 1987년 UCI ML 27에 기증되었습니다. 25 | 26 | 🦆 Minnesota 새에 대한 데이터는 Hannah Collins가 [위키피디아](https://en.wikipedia.org/wiki/List_of_birds_of_Minnesota)에서 스크랩한 [Kaggle](https://www.kaggle.com/hannahcollins/minnesota-birds) 데이터 입니다. 27 | 28 | 모든 데이터 셋에는 [CC0: Creative Commons](https://creativecommons.org/publicdomain/zero/1.0/) 라이선스가 부여됩니다. 29 | 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /3-Data-Visualization/translations/README.zh-cn.md: -------------------------------------------------------------------------------- 1 | # 可视化 2 | 3 | ![a bee on a lavender flower](../images/bee.jpg) 4 | > 拍摄者 Jenna Lee 上传于 Unsplash 5 | 6 | 数据可视化是数据科学家最重要的任务之一。在有的时候，一图可以胜千言。除此之外，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。 7 | 8 | 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。 9 | 10 | ### 主题 11 | 12 | 1. [可视化数据](../09-visualization-quantities/README.md) 13 | 1. [可视化数据分布](../10-visualization-distributions/README.md) 14 | 1. [可视化数据占比](../11-visualization-proportions/README.md) 15 | 1. [可视化数据间的关系](../12-visualization-relationships/README.md) 16 | 1. [做有意义的可视化](../13-meaningful-visualizations/README.md) 17 | 18 | ### 致谢 19 | 20 | 这些可视化课程是由 [Jen Looper](https://twitter.com/jenlooper) 用 🌸 编写的 21 | 22 | 🍯 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。事实上，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)。 23 | 24 | 🍄 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 27 （机器学习数据集仓库） 25 | 26 | 🦆 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。 27 | 28 | 以上这些数据集都遵循 [CC0: Creative Commons](https://creativecommons.org/publicdomain/zero/1.0/) 条款。 29 | -------------------------------------------------------------------------------- /4-Data-Science-Lifecycle/14-Introduction/assignment.md: -------------------------------------------------------------------------------- 1 | # Assessing a Dataset 2 | 3 | A client has approached your team for help in investigating a taxi customer's seasonal spending habits in New York City. 4 | 5 | They want to know: **Do yellow taxi passengers in New York City tip drivers more in the winter or summer?** 6 | 7 | Your team is in the [Capturing](Readme.md#Capturing) stage of the Data Science Lifecycle and you are in charge of handling the dataset. You have been provided a notebook and [data](../../data/taxi.csv) to explore. 8 | 9 | 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). 10 | You can also open the taxi data file in text editor or spreadsheet software like Excel. 11 | 12 | ## Instructions 13 | 14 | - Assess whether or not the data in this dataset can help answer the question. 15 | - 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. 16 | - Write 3 questions that you would ask the client for more clarification and better understanding of the problem. 17 | 18 | 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. 19 | 20 | ## Rubric 21 | 22 | Exemplary | Adequate | Needs Improvement 23 | --- | --- | -- | 24 | -------------------------------------------------------------------------------- /4-Data-Science-Lifecycle/14-Introduction/images/CRISP-DM.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/4-Data-Science-Lifecycle/14-Introduction/images/CRISP-DM.png -------------------------------------------------------------------------------- /4-Data-Science-Lifecycle/14-Introduction/images/data-science-lifecycle.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/4-Data-Science-Lifecycle/14-Introduction/images/data-science-lifecycle.jpg -------------------------------------------------------------------------------- /4-Data-Science-Lifecycle/14-Introduction/images/tdsp-lifecycle2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/4-Data-Science-Lifecycle/14-Introduction/images/tdsp-lifecycle2.png -------------------------------------------------------------------------------- /4-Data-Science-Lifecycle/14-Introduction/translations/assignment.ko.md: -------------------------------------------------------------------------------- 1 | # 데이터셋 평가 2 | 3 | 한 고객이 뉴욕에서 택시 고객의 계절별 소비 습관을 조사하는 데 도움을 청하기 위해 귀하의 팀에 연락했습니다. 4 | 5 | 그들은 알고 싶어한다: **뉴욕의 노란 택시 승객들은 겨울이나 여름에 기사들에게 팁을 더 많이 주는가?** 6 | 7 | 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. 8 | 9 | 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. 10 | 엑셀과 같은 텍스트 편집기나 스프레드시트 소프트웨어에서 택시 데이터 파일을 열 수도 있습니다. 11 | 12 | ## 지시사항 13 | 14 | - 이 데이터 세트의 데이터가 질문에 대답하는 데 도움이 될 수 있는지 여부를 평가합니다. 15 | - [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. 16 | - 고객에게 문제에 대한 보다 명확한 설명과 이해를 위해 물어볼 질문 3개를 작성합니다. 17 | 18 | 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. 19 | 20 | ## 표제 21 | 22 | 모범 | 충분 | 개선 필요 23 | --- | --- | -- | 24 | -------------------------------------------------------------------------------- /4-Data-Science-Lifecycle/14-Introduction/translations/assignment.zh-cn.md: -------------------------------------------------------------------------------- 1 | # 获得一组数据集 2 | 3 | 一位客户向你的团队寻求帮助，调查一位出租车乘客在纽约市的季节性消费习惯。 4 | 5 | 他们想知道：**纽约市黄色出租车乘客在冬夏两季是否会给司机更多小费？** 6 | 7 | 你的团队正处于数据科学生命周期的[捕获](../Readme.md#Capturing)阶段,而你正在处理数据集。有笔记本和[数据](../../../data/taxi.csv)可供你探索。 8 | 9 | 在这个目录中, [笔记本](../notebook.ipynb)利用Python加载来自[纽约市出租车和豪华轿车委员会](https://docs.microsoft.com/en-us/azure/open-datasets/dataset-taxi-yellow?tabs=azureml-opendatasets)的黄色出租车出行数据。 10 | 你也可以用文本编辑器或电子表格软件，如Excel来打开出租车数据文件。 11 | 12 | ## 指示 13 | 14 | - 评估数据集中的数据是否能帮助回答问题。 15 | - 探索[纽约市开放数据目录](https://data.cityofnewyork.us/browse?sortBy=most_accessed&utf8=%E2%9C%93)。确定一个可以帮助回答客户问题的额外数据集。 16 | - 写下三个你会问客户的问题，以更清晰、更好理解客户的问题。 17 | 18 | 查询[数据字典](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf)和[用户指南](https://www1.nyc.gov/assets/tlc/downloads/pdf/trip_record_user_guide.pdf)以获得更多关于数据的信息。 19 | 20 | ## 评分表 21 | 22 | 优秀|一般|需要改进 23 | ---|---|--| -------------------------------------------------------------------------------- /4-Data-Science-Lifecycle/15-analyzing/assignment.md: -------------------------------------------------------------------------------- 1 | # Exploring for answers 2 | 3 | This is a continuation of the previous lesson's [assignment](../14-Introduction/assignment.md), where we briefly took a look at the data set. Now we will be taking a deeper look at the data. 4 | 5 | Again, the question the client wants to know: **Do yellow taxi passengers in New York City tip drivers more in the winter or summer?** 6 | 7 | Your team is in the [Analyzing](README.md) stage of the Data Science Lifecycle, where you are responsible for doing exploratory data analysis on the dataset. You have been provided a notebook and dataset that contains 200 taxi transactions from January and July 2019. 8 | 9 | ## Instructions 10 | 11 | In this directory is a [notebook](assignment.ipynb) and data from the [Taxi & Limousine Commission](https://docs.microsoft.com/en-us/azure/open-datasets/dataset-taxi-yellow?tabs=azureml-opendatasets). 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. 12 | 13 | 14 | Use some the techniques in this lesson to do your own EDA in the notebook (add cells if you'd like) and answer the following questions: 15 | 16 | - What other influences in the data could affect the tip amount? 17 | - What columns will most likely not be needed to answer the client's questions? 18 | - Based on what has been provided so far, does the data seem to provide any evidence of seasonal tipping behavior? 19 | 20 | 21 | ## Rubric 22 | 23 | Exemplary | Adequate | Needs Improvement 24 | --- | --- | -- | 25 | -------------------------------------------------------------------------------- /4-Data-Science-Lifecycle/15-analyzing/translations/assignment.hi.md: -------------------------------------------------------------------------------- 1 | # जवाब की तलाश 2 | 3 | यह पिछले पाठ के [असाइनमेंट](..\14-Introduction\assignment.md) की निरंतरता है, जहां हमने संक्षेप में डेटा सेट पर एक नज़र डाली। अब हम आंकड़ों पर गहराई से विचार करेंगे। 4 | 5 | फिर से, वह प्रश्न जो ग्राहक जानना चाहता है: **क्या न्यूयॉर्क शहर में पीली टैक्सी के यात्री सर्दियों या गर्मियों में ड्राइवरों को अधिक टिप देते हैं?** 6 | 7 | आपकी टीम डेटा विज्ञान जीवनचक्र के [विश्लेषण] (Readme.md) चरण में है, जहां आप डेटासेट पर खोजपूर्ण डेटा विश्लेषण करने के लिए जिम्मेदार हैं। आपको एक नोटबुक और डेटासेट प्रदान किया गया है जिसमें जनवरी और जुलाई 2019 से 200 टैक्सी लेनदेन शामिल हैं। 8 | 9 | ## निर्देश 10 | 11 | इस निर्देशिका में एक [नोटबुक](असाइनमेंट.आईपीएनबी) और [टैक्सी और लिमोसिन कमीशन] का डेटा है(https://docs.microsoft.com/en-us/azure/open-datasets/dataset-taxi- Yellow? tabs=azureml-opendatasets)। [डेटासेट डिक्शनरी] (https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_ Yellow.pdf) और [उपयोगकर्ता गाइड](https://www1.nyc.gov/assets/tlc/) का संदर्भ लें। डाउनलोड/पीडीएफ/trip_record_user_guide.pdf) डेटा के बारे में अधिक जानकारी के लिए। 12 | 13 | 14 | इस पाठ की कुछ तकनीकों का उपयोग नोटबुक में अपना स्वयं का EDA करने के लिए करें (यदि आप चाहें तो सेल जोड़ें) और निम्नलिखित प्रश्नों के उत्तर दें: 15 | 16 | - डेटा में अन्य कौन से प्रभाव टिप राशि को प्रभावित कर सकते हैं? 17 | - क्लाइंट के सवालों के जवाब देने के लिए किन कॉलमों की सबसे ज्यादा जरूरत नहीं होगी? 18 | - अब तक जो प्रदान किया गया है, उसके आधार पर, क्या डेटा मौसमी टिपिंग व्यवहार का कोई सबूत प्रदान करता है? 19 | 20 | 21 | ## रूब्रिक 22 | 23 | अनुकरणीय | पर्याप्त | सुधार की जरूरत 24 | --- | --- | -- | 25 | -------------------------------------------------------------------------------- /4-Data-Science-Lifecycle/15-analyzing/translations/assignment.ko.md: -------------------------------------------------------------------------------- 1 | # 정답 찾기 2 | 3 | 이는 지난 강의의 [assignment](..\14-Introduction\assignment.md)와 이어지며, 우리는 잠시 데이터셋을 살펴보았습니다. 이제 데이터를 더욱 자세히 살펴보겠습니다. 4 | 5 | 다시 한번, 고객이 알고싶어하는 질문: **뉴욕의 노란 택시 승객들은 겨울이나 여름에 기사들에게 팁을 더 많이 주나요?** 6 | 7 | 당신의 팀은 Data Science Lifecycle의 [Analyzing](README.ko.md)단계에 있으며, 이 곳에서 데이터셋에 대한 탐색적 데이터분석을 수행해야합니다. 당신은 2019년 1월부터 7월까지 200건의 택시 거래가 포함된 노트북과 데이터셋을 제공받았습니다. 8 | 9 | ## 지시사항 10 | 11 | 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. 12 | 13 | 이번 강의에서 배운 몇 가지 기술을 사용하여 노트북에 있는 EDA를 직접 수행하고(원하는 경우 셀 추가) 다음 질문에 답하십시오. 14 | 15 | - 데이터의 어떤 다른 영향이 팁 금액에 영향을 미칠 수 있습니까? 16 | - 클라이언트의 질문에 답하는 데 가장 필요없는 열은 무엇입니까? 17 | - 지금까지 제공된 자료에 따르면, 데이터가 계절별 팁에대한 증거를 제공하는 것 같습니까? 18 | 19 | ## Rubric 20 | 21 | 모범 | 충분 | 개선 필요 22 | --- | --- | -- | 23 | -------------------------------------------------------------------------------- /4-Data-Science-Lifecycle/15-analyzing/translations/assignment.ne.md: -------------------------------------------------------------------------------- 1 | # उत्तरको खोज 2 | 3 | यो पहिलेको पाठको [असाइनमेंट](..\14-Introduction\assignment.md) निरंतरता हो, जहां हामीले संक्षेपमा डेटा सेटमा एक नज़र लगाएका थियौ। अब हामी आंकडालाई गहिराईवाट विचार गर्नेछौ। 4 | 5 | फेरि, उही प्रश्न ग्राहक के जान्न चाहन्छ: **के न्यु योर्क शहरका पहेंलो ट्याक्सी यात्रुहरूले जाडो वा गर्मीमा चालकहरूलाई बढी टिप दिन्छन्?** 6 | 7 | तपाईंको टोली डेटा विज्ञान जीवनचक्रको [विश्लेषण] (Readme.md) चरणमा छ, जहाँ तपाईं डेटासेटहरूमा अन्वेषण डेटा विश्लेषण गर्न जिम्मेवार हुनुहुन्छ। तपाईलाई एउटा नोटबुक र डाटासेट प्रदान गरिएको छ जसमा जनवरी र जुलाई २०१९ सम्मका २०० ट्याक्सी लेनदेनहरू समावेश छन्। 8 | ## निर्देश 9 | 10 | यस डाइरेक्टरीमा [नोटबुक](assignment.ipnb) र [ट्याक्सी र लिमोसिन कमिसन](https://docs.microsoft.com/en-us/azure/open-datasets/dataset-taxi- Yellow? tabs= azureml-opendatasets)। [डेटासेट शब्दकोश] (https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_ Yellow.pdf) र [प्रयोगकर्ता गाइड](https://www1.nyc.gov/assets/tlc/) सन्दर्भ को। डाउनलोड/pdf/trip_record_user_guide.pdf) डाटा बारे थप जानकारीको लागि। 11 | 12 | नोटबुकमा तपाईंको आफ्नै EDA बनाउनका लागि यस पाठका केही प्रविधिहरू प्रयोग गर्नुहोस् (यदि तपाईं चाहनुहुन्छ भने कक्षहरू थप्नुहोस्) र निम्न प्रश्नहरूको जवाफ दिनुहोस्: 13 | 14 | - डेटामा अन्य कुन प्रभावहरूले टिप रकमलाई असर गर्न सक्छ? 15 | - ग्राहकका प्रश्नहरूको जवाफ दिन कुन स्तम्भहरू प्रायः आवश्यक पर्दैन? 16 | - अहिलेसम्म प्रदान गरिएको कुराको आधारमा, डेटाले मौसमी टिपिङ व्यवहारको कुनै प्रमाण दिन्छ? 17 | 18 | ## रूब्रिक 19 | 20 | अनुकरणीय | पर्याप्त | सुधारको जरूरत 21 | --- | --- | -- | 22 | -------------------------------------------------------------------------------- /4-Data-Science-Lifecycle/16-communication/assignment.md: -------------------------------------------------------------------------------- 1 | # Tell a story 2 | 3 | ## Instructions 4 | 5 | Data Science is all about storytelling. Pick any dataset and write a short paper about a story you could tell about it. What do you hope your dataset can reveal? What will you do if its revelations prove problematic? What if your data doesn't easily unlock its secrets? Think of the scenarios your dataset might present and write them down. 6 | 7 | ## Rubric 8 | 9 | Exemplary | Adequate | Needs Improvement 10 | --- | --- | -- | 11 | 12 | A one-page essay is presented in .doc format with the dataset explained, documented, credited, and a coherent story is presented about it with detailed examples from the data.| A shorter essay is presented in less detailed format | The essay is found lacking in one of the above details. -------------------------------------------------------------------------------- /4-Data-Science-Lifecycle/16-communication/data-communication-emerson-presentation-graphic.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/4-Data-Science-Lifecycle/16-communication/data-communication-emerson-presentation-graphic.xlsx -------------------------------------------------------------------------------- /4-Data-Science-Lifecycle/README.md: -------------------------------------------------------------------------------- 1 | # The Data Science Lifecycle 2 | 3 | ![communication](images/communication.jpg) 4 | > Photo by Headway on Unsplash 5 | 6 | In these lessons, you'll explore some of the aspects of the Data Science lifecycle, including analysis and communication around data. 7 | 8 | ### Topics 9 | 10 | 1. [Introduction](14-Introduction/README.md) 11 | 2. [Analyzing](15-analyzing/README.md) 12 | 3. [Communication](16-communication/README.md) 13 | 14 | ### Credits 15 | 16 | These lessons were written with ❤️ by [Jalen McGee](https://twitter.com/JalenMCG) and [Jasmine Greenaway](https://twitter.com/paladique) 17 | -------------------------------------------------------------------------------- /4-Data-Science-Lifecycle/images/communication.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/4-Data-Science-Lifecycle/images/communication.jpg -------------------------------------------------------------------------------- /4-Data-Science-Lifecycle/translations/README.hi.md: -------------------------------------------------------------------------------- 1 | # डेटा विज्ञान के जीवनचक्र 2 | ![संचार](../images/communication.jpg) 3 | >तस्वीर Headway द्वारा Unsplash पर 4 | 5 | इन पाठों में, आप डेटा विज्ञान जीवनचक्र के कुछ पहलुओं का पता लगाएंगे, जिसमें डेटा के आसपास विश्लेषण और संचार शामिल है। 6 | 7 | ### विषय 8 | 1. [परिचय](../14-Introduction/README.md) 9 | 2. [विश्लेषण](../15-analyzing/README.md) 10 | 3. [संचार](../16-communication/README.md) 11 | 12 | ### क्रेडिट 13 | ये पाठ [जालेन मैक्गी](https://twitter.com/JalenMCG) और [जैस्मीन ग्रीनवे](https://twitter.com/paladique) द्वारा ❤️ से लिखे गए हैं। 14 | -------------------------------------------------------------------------------- /4-Data-Science-Lifecycle/translations/README.ne.md: -------------------------------------------------------------------------------- 1 | # डाटा विज्ञान जीवनचक्र 2 | 3 | ![संचार](images/communication.jpg) 4 | 5 | > फोटो Headway मा Unsplash द्वारा 6 | 7 | यी पाठहरुमा, तपाइँ डाटा विज्ञान जीवन चक्र को केहि पहलुहरु को अन्वेषण गर्नुहुनेछ, विश्लेषण र डाटा को आसपास संचार सहित। 8 | 9 | ### बिषयहरु 10 | 11 | १. [परिचय](14-Introduction/README.md) 12 | २. [विश्लेषण](15-Analyzing/README.md) 13 | ३. [संचार](16-communication/README.md) 14 | 15 | यी पाठहरु ❤️ का साथ [Jalen McGee](https://twitter.com/JalenMCG) र [Jasmine Greenaway](https://twitter.com/paladique) द्वारा लेखिएको हो । 16 | -------------------------------------------------------------------------------- /4-Data-Science-Lifecycle/translations/README.pt-br.md: -------------------------------------------------------------------------------- 1 | # O ciclo de vida da Ciência de Dados 2 | 3 | ![communication](../images/communication.jpg) 4 | > Foto por Headway em Unsplash 5 | 6 | Nessas lições, você explorará alguns dos aspectos do ciclo de vida da ciência de dados, incluindo análise e comunicação em torno de dados. 7 | 8 | ### Tópicos 9 | 10 | 1. [Introdução](14-Introduction/README.md) 11 | 2. [Analisando](15-Analyzing/README.md) 12 | 3. [Comunicação](16-communication/README.md) 13 | 14 | ### Créditos 15 | 16 | Estas lições foram escritas com ❤️ por [Jalen McGee](https://twitter.com/JalenMCG) e [Jasmine Greenaway](https://twitter.com/paladique) 17 | -------------------------------------------------------------------------------- /4-Data-Science-Lifecycle/translations/README.ru.md: -------------------------------------------------------------------------------- 1 | # Введение в жизненный цикл проекта в области науки о данных 2 | 3 | ![communication](../images/communication.jpg) 4 | > Photo by Headway on Unsplash 5 | 6 | В данных уроках вы познакомитесь с этапами жизненного циклы проекта в области науки о данных, включая анализ данных и взаимодействие на их основе. 7 | 8 | ### Разделы 9 | 10 | 1. [Введение в жизненный цикл проекта в области науки о данных](14-Introduction/README.md) 11 | 2. [Анализ данных](15-Analyzing/README.md) 12 | 3. [Взаимодействие на основе данных](16-communication/README.md) 13 | 14 | ### Благодарности 15 | 16 | Данные уроки были написаны с ❤️ [Jalen McGee](https://twitter.com/JalenMCG) и [Jasmine Greenaway](https://twitter.com/paladique) 17 | -------------------------------------------------------------------------------- /4-Data-Science-Lifecycle/translations/README.zh-cn.md: -------------------------------------------------------------------------------- 1 | # 数据科学的生命周期 2 | 3 | ![communication](../images/communication.jpg) 4 | > 拍摄者 Headway 上传于 Unsplash 5 | 6 | 在这些课程中，你将探索到数据科学生命周期的一些方面，包括围绕数据展开的分析和数据之间的沟通。 7 | 8 | ### 主题 9 | 10 | 1. [简介](../14-Introduction/README.md) 11 | 2. [数据分析](../15-Analyzing/README.md) 12 | 3. [数据沟通](../16-communication/README.md) 13 | 14 | ### 致谢 15 | 16 | 这些课程由 [Jalen McGee](https://twitter.com/JalenMCG) 和 [Jasmine Greenaway](https://twitter.com/paladique) 用 ❤️ 编写 17 | -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/17-Introduction/assignment.md: -------------------------------------------------------------------------------- 1 | # Market Research 2 | 3 | ## Instructions 4 | 5 | In this lesson you learned that there are several important cloud providers. Do some market research to discover what each can offer to the Data Scientist. Are the offerings comparable? Write a paper to describe the offerings of three or more of these cloud providers. 6 | ## Rubric 7 | 8 | Exemplary | Adequate | Needs Improvement 9 | --- | --- | -- | 10 | A one page paper describes three cloud providers' data science offerings and differentiates between them. | A shorter paper is presented | A paper is presented without completing the analysis -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/17-Introduction/translations/assignment.hi.md: -------------------------------------------------------------------------------- 1 | # मार्केट रिसर्च 2 | 3 | ## निर्देश 4 | 5 | इस पाठ में आपने सीखा कि कई महत्वपूर्ण क्लाउड प्रदाता हैं। डेटा साइंटिस्ट को प्रत्येक क्या पेशकश कर सकता है, यह जानने के लिए कुछ मार्केट रिसर्च करें। क्या उनके सब्स्क्रिप्शन्स तुलनीय हैं? इनमें से तीन या अधिक क्लाउड प्रदाताओं की पेशकशों का वर्णन करने के लिए एक पेपर लिखें। 6 | ## स्कोर गाइड 7 | 8 | उदाहरणात्मक | पर्याप्त | सुधार की जरूरत है 9 | --- | --- | -- | 10 | एक पृष्ठ का पेपर तीन क्लाउड प्रदाताओं के डेटा विज्ञान प्रसाद का वर्णन करता है और उनके बीच अंतर करता है। | एक छोटा पेपर प्रस्तुत किया गया है। | विश्लेषण पूरा किए बिना एक पेपर प्रस्तुत किया गया है। -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/17-Introduction/translations/assignment.ko.md: -------------------------------------------------------------------------------- 1 | # 시장 조사 2 | 3 | ## 지침 4 | 5 | 이 학습에서는 몇 가지 중요한 클라우드 제공자가 있다는 것을 배웠습니다. 시장 조사를 통해 각각이 데이터 과학자에게 무엇을 제공할 수 있는지 알아보세요. 제공하는 것들이 비교될 수 있습니까? 3개 이상의 클라우드 제공업체가 제공하는 서비스를 설명하는 문서를 작성하십시오. 6 | ## 기준표 7 | 8 | 모범 | 충분 | 개선 필요 9 | --- | --- | -- | 10 | 한 페이지짜리 문서에서는 세 가지 클라우드 제공업체의 데이터 과학 제품에 대해 설명하고 이를 구분합니다. | 더 짧은 논문이 제시됩니다 | 분석을 완료하지 않고 논문을 발표함 -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/17-Introduction/translations/assignment.ne.md: -------------------------------------------------------------------------------- 1 | # मार्केट रिसर्च 2 | 3 | ## निर्देश 4 | 5 | यस पाठमा तपाईले धेरै महत्वपूर्ण क्लाउड प्रदाताहरु सिक्नभएको छ। डेटा साइंटिस्ट बारे केही खोज गर्नुहोस् र के सहायता पुर्याउन सक्नुहुन्छ सोच्नुहोस। के उनको सब्स्क्रिप्शन्स तुलनीय छ? यिनिहरु मध्यमा तीन या अधिक क्लाउड प्रदाताको पेशकशको वर्णन गर्नका लागि एउटा पेपर लेख्नुहोस्। 6 | ## स्कोर गाइड 7 | 8 | उदाहरणात्मक | पर्याप्त | सुधारको जरूरत हछ 9 | --- | --- | -- | 10 | एक पृष्ठको पेपरमा तीन क्लाउड प्रदाताको डेटा विज्ञान, प्रसादको वर्णन गरोस् र उनीहरु बीचको अंतरको बारेमा होस् | एउटा छोटो पेपर प्रस्तुत गरिएको छ | विश्लेषण पूरा नगरी एक पेपर प्रस्तुत गरिएको छ । -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/18-Low-Code/assignment.md: -------------------------------------------------------------------------------- 1 | # Low code/No code Data Science project on Azure ML 2 | 3 | ## Instructions 4 | 5 | We saw how to use the Azure ML platform to train, deploy and consume a model in a Low code/No code fashion. 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소비하는 데 사용할 수 있는 일부 데이터를 찾아보십시오. [Kaggle](https://kaggle.com) 및 [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)에서 데이터셋을 찾을 수 있습니다. 6 | 7 | ## 기준표 8 | 9 | | 모범 | 충분 | 개선 필요 | 10 | |----------|----------|-------| 11 | |데이터를 업로드할 때 필요한 경우 피처(feature) 타입 변경을 처리했습니다. 필요한 경우 데이터도 정리했습니다. AutoML을 통해 데이터셋에 대한 교육을 실행하고 모델 설명을 확인했습니다. 최고의 모델을 배포했고 사용할 수 있었습니다. | 데이터를 업로드할 때 필요한 경우 피처(feature) 타입 변경을 처리했습니다. AutoML을 통해 데이터셋에 대한 교육을 실행하고 최상의 모델을 배포하여 사용할 수 있었습니다. | AutoML에서 훈련한 최고의 모델을 배포했으며 이를 사용할 수 있었습니다. | -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/19-Azure/assignment.md: -------------------------------------------------------------------------------- 1 | # Data Science project using Azure ML SDK 2 | 3 | ## Instructions 4 | 5 | We saw how to use the Azure ML platform to train, deploy and consume a model with the Azure ML SDK. Now look around for some data that you could use to train an other model, deploy it and consume it. You can look for datasets on [Kaggle](https://kaggle.com) and [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109). 6 | 7 | ## Rubric 8 | 9 | | Exemplary | Adequate | Needs Improvement | 10 | |-----------|----------|-------------------| 11 | |When doing the AutoML Configuration, you went through the SDK documentation to see what parameters you could use. You ran a training on a dataset through AutoML using Azure ML SDK, and you checked the model explanations. You deployed the best model and you were able to consume it through the Azure ML SDK. | You ran a training on a dataset through AutoML using Azure ML SDK, and you checked the model explanations. You deployed the best model and you were able to consume it through the Azure ML SDK. | You ran a training on a dataset through AutoML using Azure ML SDK. You deployed the best model and you were able to consume it through the Azure ML SDK. | 12 | -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/19-Azure/images/compute-instance-1.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/5-Data-Science-In-Cloud/19-Azure/images/compute-instance-1.PNG -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/19-Azure/images/notebook-1.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/5-Data-Science-In-Cloud/19-Azure/images/notebook-1.PNG -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/19-Azure/images/notebook-2.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/5-Data-Science-In-Cloud/19-Azure/images/notebook-2.PNG -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/19-Azure/images/project-schema.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/5-Data-Science-In-Cloud/19-Azure/images/project-schema.PNG -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/19-Azure/solution/notebook.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/5-Data-Science-In-Cloud/19-Azure/solution/notebook.ipynb -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/19-Azure/translations/assignment.hi.md: -------------------------------------------------------------------------------- 1 | # Azure ML SDK का उपयोग करके डेटा विज्ञान परियोजना 2 | 3 | ## निर्देश 4 | 5 | हमने देखा कि एज़्योर एमएल एसडीके के साथ एक मॉडल को प्रशिक्षित करने, तैनात करने और उपभोग करने के लिए एज़्योर एमएल प्लेटफॉर्म का उपयोग कैसे किया जाता है। अब कुछ डेटा के लिए चारों ओर देखें, जिसका उपयोग आप किसी अन्य मॉडल को प्रशिक्षित करने, उसे परिनियोजित करने और उसका उपभोग करने के लिए कर सकते हैं। आप [कागल](https://kaggle.com) और [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-77958 पर डेटासेट ढूंढ सकते हैं। -bethanycheum&ocid=AID3041109)। 6 | 7 | ## रूब्रिक 8 | 9 | | अनुकरणीय | पर्याप्त | सुधार की जरूरत | 10 | |-----------|----------|---------------------| 11 | |ऑटोएमएल कॉन्फ़िगरेशन करते समय, आपने यह देखने के लिए कि आप किन मापदंडों का उपयोग कर सकते हैं, एसडीके दस्तावेज़ीकरण को देखा। आपने Azure ML SDK का उपयोग करके AutoML के माध्यम से डेटासेट पर एक प्रशिक्षण चलाया, और आपने मॉडल स्पष्टीकरण की जाँच की। आपने सबसे अच्छा मॉडल तैनात किया और आप Azure ML SDK के माध्यम से इसका उपभोग करने में सक्षम थे। | आपने Azure ML SDK का उपयोग करके AutoML के माध्यम से डेटासेट पर एक प्रशिक्षण चलाया, और आपने मॉडल स्पष्टीकरण की जाँच की। आपने सबसे अच्छा मॉडल तैनात किया और आप Azure ML SDK के माध्यम से इसका उपभोग करने में सक्षम थे। | आपने Azure ML SDK का उपयोग करके AutoML के माध्यम से डेटासेट पर एक प्रशिक्षण चलाया। आपने सबसे अच्छा मॉडल तैनात किया और आप Azure ML SDK के माध्यम से इसका उपभोग करने में सक्षम थे। | 12 | -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/19-Azure/translations/assignment.ko.md: -------------------------------------------------------------------------------- 1 | # Azure ML SDK를 사용한 데이터 사이언스 프로젝트 2 | 3 | ## 지침 4 | 5 | Azure ML 플랫폼을 사용하여 Azure ML SDK로 모델을 학습, 배포 및 사용하는 방법을 살펴보았습니다. 이제 다른 모델을 학습하고 배포하고 소비하는 데 사용할 수 있는 일부 데이터를 찾아보십시오. [Kaggle](https://kaggle.com) 및 [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)에서 데이터 셋을 찾을 수 있습니다. 6 | 7 | ## 기준표 8 | 9 | | 모범 | 충분 | 개선 필요 | 10 | |----------|----------|-------| 11 | |AutoML 구성을 수행할 때 사용할 수 있는 매개변수를 확인하기 위해 SDK 문서를 살펴보았습니다. Azure ML SDK를 사용하여 AutoML을 통해 데이터 셋에 대한 교육을 실행하고 모델 설명을 확인했습니다. 최고의 모델을 배포했고 Azure ML SDK를 통해 사용할 수 있었습니다. | Azure ML SDK를 사용하여 AutoML을 통해 데이터 셋에 대한 교육을 실행하고 모델 설명을 확인했습니다. 최고의 모델을 배포했고 Azure ML SDK를 통해 사용할 수 있었습니다. | Azure ML SDK를 사용하여 AutoML을 통해 데이터 셋에 대한 교육을 실행했습니다. 최고의 모델을 배포했고 Azure ML SDK를 통해 사용할 수 있었습니다. | -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/19-Azure/translations/assignment.ne.md: -------------------------------------------------------------------------------- 1 | # Azure ML SDK प्रयोग गरेर डाटा विज्ञान परियोजना 2 | 3 | ## निर्देशन 4 | 5 | हामीले Azure ML SDK सँग मोडेललाई तालिम, डिप्लोय, र उपभोग गर्न Azure ML प्लेटफर्म कसरी प्रयोग गर्ने भनेर हेर्यौं। अब केहि डेटा को लागी वरिपरि हेर्नुहोस् जुन तपाईले अर्को मोडेललाई प्रशिक्षित गर्न, प्रयोग गर्न र उपभोग गर्न सक्नुहुन्छ। तपाईंले Kaggle र [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) मा डाटासेट फेला पार्न सक्नुहुन्छ। 6 | 7 | ## रूब्रिक 8 | 9 | |अनुकरणीय | पर्याप्त | सुधारको आवश्यकता | 10 | 11 | | AutoML कन्फिगरेसन गरिरहँदा, तपाईंले कुन प्यारामिटरहरू प्रयोग गर्न सक्नुहुन्छ भनी हेर्न SDK कागजात हेर्नुभयो। तपाईंले Azure ML SDK प्रयोग गरेर AutoML मार्फत डेटासेटमा प्रशिक्षण चलाउनुभयो, र तपाईंले मोडेल स्पष्टीकरण जाँच गर्नुभयो। तपाईंले उत्तम मोडेल प्रयोग गर्नुभयो र तपाईं Azure ML SDK मार्फत यसलाई उपभोग गर्न सक्षम हुनुभयो।|तपाईंले Azure ML SDK प्रयोग गरेर AutoML मार्फत डेटासेटमा प्रशिक्षण चलाउनुभयो, र तपाईंले मोडेल स्पष्टीकरण जाँच गर्नुभयो। तपाईंले उत्तम मोडेल प्रयोग गर्नुभयो र तपाईं Azure ML SDK मार्फत यसलाई उपभोग गर्न सक्षम हुनुभयो। | तपाईंले Azure ML SDK प्रयोग गरेर AutoML मार्फत डेटासेटमा प्रशिक्षण दिनुभयो। तपाईंले उत्तम मोडेल प्रयोग गर्नुभयो र तपाईं Azure ML SDK मार्फत यसलाई उपभोग गर्न सक्षम हुनुभयो। | 12 | -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/README.md: -------------------------------------------------------------------------------- 1 | # Data Science in the Cloud 2 | 3 | ![cloud-picture](images/cloud-picture.jpg) 4 | 5 | > Photo by [Jelleke Vanooteghem](https://unsplash.com/@ilumire) from [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) 6 | 7 | When it comes to doing data science with big data, the cloud can be a game changer. In the next three lessons, we are going to see what the cloud is and why it can be very helpful. We are also going to explore a heart failure dataset and build a model to help assess the probability of someone having a heart failure. We will use the power of the cloud to train, deploy and consume a model in two different ways. One way using only the user interface in a Low code/No code fashion, the other way using the Azure Machine Learning Software Developer Kit (Azure ML SDK). 8 | 9 | ![project-schema](19-Azure/images/project-schema.PNG) 10 | 11 | ### Topics 12 | 13 | 1. [Why use Cloud for Data Science?](17-Introduction/README.md) 14 | 2. [Data Science in the Cloud: The "Low code/No code" way ](18-Low-Code/README.md) 15 | 3. [Data Science in the Cloud: The "Azure ML SDK" way ](19-Azure/README.md) 16 | 17 | ### Credits 18 | These lessons were written with ☁️ and 💕 by [Maud Levy](https://twitter.com/maudstweets) and [Tiffany Souterre](https://twitter.com/TiffanySouterre) 19 | 20 | Data for the Heart Failure Prediction project is sourced from [ 21 | Larxel](https://www.kaggle.com/andrewmvd) on [Kaggle](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data). It is licensed under the [Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) 22 | -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/images/cloud-picture.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/5-Data-Science-In-Cloud/images/cloud-picture.jpg -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/translations/README.hi.md: -------------------------------------------------------------------------------- 1 | # क्लाउड में डेटा साइंस 2 | 3 | ![क्लाउड-चित्र](../images/cloud-picture.jpg) 4 | 5 | > [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) से [जेलेके वनूटेघम](https://unsplash.com/@ilumire) द्वारा फोटो। 6 | 7 | जब बड़े डेटा के साथ डेटा साइंस करने की बात आती है, तो क्लाउड गेम चेंजर हो सकता है। अगले तीन पाठों में हम यह देखने जा रहे हैं कि क्लाउड क्या है और यह इतना मददगार क्यों हो सकता है। हम हृद्पात (दिल की धड़कन रुकना) के डेटासेट का भी पता लगाने जा रहे हैं और किसी के हृद्पात की संभावना का आकलन करने में मदद करने के लिए एक मॉडल का निर्माण करने जा रहे हैं। हम दो अलग-अलग तरीकों से एक मॉडल को प्रशिक्षित करने, डिप्लॉय करने और उपभोग करने के लिए क्लाउड की शक्ति का उपयोग करेंगे। एक तरीका कम कोड/नो कोड फैशन में केवल यूजर इंटरफेस का उपयोग करके, दूसरा तरीका एज़ूर मशीन लर्निंग सॉफ्टवेयर डेवलपर किट (एज़ूर एमएल एस.डी.के) का उपयोग करके। 8 | 9 | ![प्रॉजेक्ट-स्कीमा](../19-Azure/images/project-schema.PNG) 10 | 11 | ### विषय 12 | 13 | 1. [डेटा साइंस के लिए क्लाउड का उपयोग क्यों करें?](../17-Introduction/README.md) 14 | 2. [क्लाउड में डेटा साइंस: "लो कोड/नो कोड" तरीका](../18-Low-Code/README.md) 15 | 3. [क्लाउड में डेटा साइंस: "एज़ूर एमएल एस.डी.के" तरीका](../19-Azure/README.md) 16 | 17 | ### आभार सूची 18 | ये पाठ [मौड लेवी](https://twitter.com/maudstweets) और [टिफ़नी सॉटर्रे](https://twitter.com/TiffanySouterre) द्वारा ☁️ और 💕 के साथ लिखे गए थे। 19 | 20 | हार्ट फेल्योर प्रेडिक्शन प्रोजेक्ट के लिए डेटा [कागल](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data) पर [लारक्सेल](https://www.kaggle.com/andrewmvd) से प्राप्त किया गया है। इसे [एट्रिब्यूशन 4.0 इंटरनेशनल (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) के तहत लाइसेंस दिया गया है। -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/translations/README.ko.md: -------------------------------------------------------------------------------- 1 | # 클라우드에서의 데이터 과학 2 | 3 | ![cloud-picture](../images/cloud-picture.jpg) 4 | 5 | > [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape)의 [Jelleke Vanooteghem](https://unsplash.com/@ilumire)의 사진 6 | 7 | 빅 데이터로 데이터 과학을 수행할 때 클라우드를 사용하면 판도를 뒤집어 놓을 수 있습니다(game changer). 다음 세 강의에서는 클라우드가 무엇이며 왜 매우 유용할 수 있는지 알아보겠습니다. 우리는 또한 심부전 데이터셋을 탐색하고 누군가가 심부전에 걸릴 확률을 평가하는 데 도움이 되는 모델을 구축할 것입니다. 클라우드의 힘을 사용하여 두 가지 방식으로 모델을 훈련, 배포 및 사용합니다. 첫번째 방법은 로우 코드(Low code)/노 코드(No code) 방식으로 사용자 인터페이스만 사용하는 방법, 두번째 방법으로는 Azure 기계 학습 소프트웨어 개발자 키트(Azure ML SDK)를 사용하는 방법이 있습니다. 8 | 9 | ![프로젝트 스키마](../19-Azure/images/project-schema.PNG) 10 | 11 | ### 주제 12 | 13 | 1. [데이터 사이언스에 클라우드를 사용하는 이유](17-Introduction/translations/README.ko.mdd) 14 | 2. [클라우드에서의 데이터 과학: "Low code/No code" 방식](../18-Low-Code/translations/README.ko.md) 15 | 3. [클라우드에서의 데이터 과학: "Azure ML SDK" 방식](../19-Azure/translations/README.ko.md) 16 | 17 | ### 크레딧 18 | 이 수업은 [Maud Levy](https://twitter.com/maudstweets)와 [Tiffany Souterre](https://twitter.com/TiffanySouterre)의 ☁️ 과 💕 로 작성했습니다. 19 | 20 | 심부전 예측 프로젝트의 데이터 출처는 [Kaggle](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data)의 [Larxel](https://www.kaggle.com/andrewmvd)에게 있습니다. [Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)에 따라 라이선스가 부여됩니다. -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/translations/README.ne.md: -------------------------------------------------------------------------------- 1 | # डाटा बिज्ञान क्लाउडमा 2 | 3 | ![क्लाउड-चित्र](images/cloud-picture.jpg) 4 | 5 | >चित्र खिच्ने [Jelleke Vanooteghem](https://unsplash.com/@ilumire) कहाँबाट [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) 6 | 7 | जब डाटा बिज्ञान ठुला डाटामा प्रयोग गरिन्छ, तब क्लाउडले खेल परिवर्तन गर्न सक्छ। आगामी तीन पाठमा,हामी क्लाउड भनेको के हो र किन सहयोगी हुन्छ भन्ने सिक्न गईरहेका छौ।हामी यिनै पाठ्यक्रममा मुटु रोगीहरुको डाटासेट पत्ता लगाउने र मुटु रोगको संभावना पत्ता लगाउने मोडको तयार पार्ने छौ। हामी क्लाउडको तागत प्रयोग गरेर मोडललाई तालीम ,तैनाथ र उपभोग विभिन्न तरिकाले गर्नेछौ। यसमा प्रकृयामा एउटा तरिका भनेको user interface निम्न कोड्/नो कोड ढंगले प्रयोग गर्ने हो भने अर्को तरिका Azure Machine Learning Software Developer Kit प्रयोग गरेर हो। 8 | 9 | ![प्रोजेक्ट-योजना](19-Azure/images/project-schema.PNG) 10 | 11 | ### पाठहरु 12 | १. [किन डाटा बिज्ञानमा क्लाउड प्रयोग गर्ने ?](१७-परिचय/README.md) 13 | २. [डाटा बिज्ञानमा क्लाउड: निम्न कोड्/नो कोड ढंग ](१८-निम्न-कोड्/README.md) 14 | 3. [डाटा बिज्ञानमा क्लाउडः "Azure ML SDK" तरिका ](19-Azure/README.md) 15 | 16 | ### श्रेय 17 | यि पाठहरु ☁️ र 💕 ले लेखिएको [Maud Levy](https://twitter.com/maudstweets) र [Tiffany Souterre](https://twitter.com/TiffanySouterre) 18 | 19 | मुटु फेल हुने भविष्यबानि गरिएको डाटा निकालिएको सोर्स[ 20 | Larxel](https://www.kaggle.com/andrewmvd) on [Kaggle](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data).लाइसेन्स गरिएको नियम [Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) 21 | -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/translations/README.ru.md: -------------------------------------------------------------------------------- 1 | # Наука о данных в облачной инфраструктуре 2 | 3 | ![cloud-picture](../images/cloud-picture.jpg) 4 | 5 | > Photo by [Jelleke Vanooteghem](https://unsplash.com/@ilumire) from [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) 6 | 7 | Когда приходит время анализировать по-настоящему большие данные, использование облачных технологий может обеспечить неоспоримое преимущество. В следующих трёх уроках вы узнаете, что такое облачная инфраструктура и чем она может быть полезна. Для этого мы исследуем набор данных о сердечной недостаточности и построим модель оценки вероятности появления данной болезни. Мы применим все преимущества облачных технологий для тренировки, развёртывания и использования модели в двумя способами. Первый спосои - это использование только пользовательского интерфейса с минимальным применением программирования, второй - использование инструмента под названием Azure Machine Learning Software Developer Kit (Azure ML SDK). 8 | 9 | ![project-schema](../19-Azure/images/project-schema.PNG) 10 | 11 | ### Разделы 12 | 13 | 1. [Преимущества облачной инфраструктуры для науки о данных.](17-Introduction/README.md) 14 | 2. [Наука о данных в облачной инфраструктуре: подходы с минимальным использованием программирования и без него.](18-Low-Code/README.md) 15 | 3. [Наука о данных в облачной инфраструктуре: применение Azure ML SDK](19-Azure/README.md) 16 | 17 | ### Благодарности 18 | Данные уроки были написаны с ☁️ и 💕 [Maud Levy](https://twitter.com/maudstweets) and [Tiffany Souterre](https://twitter.com/TiffanySouterre) 19 | 20 | 21 | Данные для прогнозирования сердечной недостаточности были собраны [ 22 | Larxel](https://www.kaggle.com/andrewmvd) и хранятся на портале [Kaggle](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data). Датасет распространятеся по лицензии [Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) 23 | -------------------------------------------------------------------------------- /5-Data-Science-In-Cloud/translations/README.zh-cn.md: -------------------------------------------------------------------------------- 1 | # 云上的数据科学 2 | 3 | ![cloud-picture](../images/cloud-picture.jpg) 4 | 5 | > 拍摄者 [Jelleke Vanooteghem](https://unsplash.com/@ilumire) 上传于 [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) 6 | 7 | 将“云”应用在大数据相关的数据科学上时，它可以起到四两拨千斤的作用。在接下来的三节课程中，我们将逐一了解并学习“云”的基本概念，并尝试弄明白它对数据科学有何帮助。我们也会通过探索心力衰竭这一疾病相关的数据集来构建模型，并用它来评估某人患有心力衰竭的概率。通过借助“云”的力量，我们将采取两种不同的方式来训练、部署、使用该模型。第一种方式仅采用低代码或无代码的用户界面，另一种则采用了 Azure 机器学习软件开发工具包 (Azure ML SDK)。 8 | 9 | ![project-schema](../19-Azure/images/project-schema.PNG) 10 | 11 | ### 主题 12 | 13 | 1. [为什么要将云应用于数据科学？](17-Introduction/README.md) 14 | 2. [云上的数据科学：通过低代码/无代码方式](18-Low-Code/README.md) 15 | 3. [云上的数据科学：通过 (Azure ML SDK)](19-Azure/README.md) 16 | 17 | ### 致谢 18 | 19 | 这些课程由 [Maud Levy](https://twitter.com/maudstweets) 和 [Tiffany Souterre](https://twitter.com/TiffanySouterre) 用 ☁️ 及 💕 编写。 20 | 21 | 心力衰竭预测项目的数据来源于 [Larxel](https://www.kaggle.com/andrewmvd) （在 [Kaggle](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data) 上）。 适用 [Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) 许可证。 22 | -------------------------------------------------------------------------------- /6-Data-Science-In-Wild/20-Real-World-Examples/images/data-science-applications.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/6-Data-Science-In-Wild/20-Real-World-Examples/images/data-science-applications.png -------------------------------------------------------------------------------- /6-Data-Science-In-Wild/20-Real-World-Examples/images/planetary-computer-explorer.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/6-Data-Science-In-Wild/20-Real-World-Examples/images/planetary-computer-explorer.png -------------------------------------------------------------------------------- /6-Data-Science-In-Wild/README.md: -------------------------------------------------------------------------------- 1 | # Data Science in the Wild 2 | 3 | Real-world applications of data science across industries. 4 | 5 | ### Topics 6 | 7 | 1. [Data Science in the Real World](20-Real-World-Examples/README.md) 8 | 9 | ### Credits 10 | 11 | Written with ❤️ by [Nitya Narasimhan](https://twitter.com/nitya) 12 | -------------------------------------------------------------------------------- /6-Data-Science-In-Wild/translations/README.es.md: -------------------------------------------------------------------------------- 1 | # Ciencia de Datos en la naturaleza 2 | 3 | Aplicaciones del mundo real de la ciencia de datos en las industrias. 4 | 5 | ### Temas 6 | 7 | 1. [Ciencia de datos en el mundo real](../20-Real-World-Examples/translations/README.es.md) 8 | 9 | ### Créditos 10 | 11 | Escrito con ❤️ por [Nitya Narasimhan](https://twitter.com/nitya) 12 | -------------------------------------------------------------------------------- /6-Data-Science-In-Wild/translations/README.hi.md: -------------------------------------------------------------------------------- 1 | # जंगली प्रारूप में डेटा विज्ञान 2 | 3 | उद्योगों में डेटा विज्ञान के वास्तविक दुनिया के अनुप्रयोग। 4 | 5 | ### विषय 6 | 7 | 1. [वास्तविक दुनिया में डेटा विज्ञान](../20-Real-World-Examples/README.md) 8 | 9 | ### क्रेडिट 10 | 11 | [नित्या नरसिम्हन](https://twitter.com/nitya) द्वारा ❤️ के साथ लिखा गया 12 | -------------------------------------------------------------------------------- /6-Data-Science-In-Wild/translations/README.ko.md: -------------------------------------------------------------------------------- 1 | # 실제 환경에서의 데이터 과학 2 | 3 | 산업 전반에 걸친 데이터 과학의 실제 적용. 4 | 5 | ### 토픽 6 | 7 | 1. [현실에서의 데이터 과학](../20-Real-World-Examples/README.md) 8 | 9 | ### 출처 10 | 11 | [Nitya Narasimhan](https://twitter.com/nitya)에 의해 쓰여졌음 ❤️ 12 | -------------------------------------------------------------------------------- /6-Data-Science-In-Wild/translations/README.np.md: -------------------------------------------------------------------------------- 1 | # जंगली प्रारूप मा डाटा विज्ञान 2 | 3 | डाटा विज्ञानको वास्तविक दुनियामा प्रयोग। 4 | 5 | ### विषय 6 | 7 | 1. [वास्तविक दुनियामा डाटा विज्ञान](../20-Real-World-Examples/README.md) 8 | 9 | ### क्रेडिट 10 | 11 | [नित्या नरसिम्हन](https://twitter.com/nitya) द्वारा ❤️ को साथ लेखिएको। 12 | -------------------------------------------------------------------------------- /6-Data-Science-In-Wild/translations/README.ru.md: -------------------------------------------------------------------------------- 1 | # Наука о данных на практике 2 | 3 | Примеры реального использования науки о данных в приложениях во многих отраслях. 4 | 5 | ### Разделы 6 | 7 | 1. [Наука о данных на практике](../20-Real-World-Examples/README.md) 8 | 9 | ### Благодарности 10 | 11 | Написано с ❤️ [Nitya Narasimhan](https://twitter.com/nitya) 12 | -------------------------------------------------------------------------------- /6-Data-Science-In-Wild/translations/README.zh-cn.md: -------------------------------------------------------------------------------- 1 | # 现实中的数据科学 2 | 3 | 数据科学在各行业的实际应用。 4 | 5 | ### 主题 6 | 7 | 1. [现实世界中的数据科学](../20-Real-World-Examples/README.md) 8 | 9 | ### 致谢 10 | 11 | 本课程由 [Nitya Narasimhan](https://twitter.com/nitya) 用 ❤️ 编写。 12 | -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Microsoft Open Source Code of Conduct 2 | 3 | This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). 4 | 5 | Resources: 6 | 7 | - [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/) 8 | - [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) 9 | - Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns 10 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing 2 | 3 | This project welcomes contributions and suggestions. Most contributions require you to 4 | agree to a Contributor License Agreement (CLA) declaring that you have the right to, 5 | and actually do, grant us the rights to use your contribution. For details, visit 6 | https://cla.microsoft.com. 7 | 8 | When you submit a pull request, a CLA-bot will automatically determine whether you need 9 | to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the 10 | instructions provided by the bot. You will only need to do this once across all repositories using our CLA. 11 | 12 | This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). 13 | For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) 14 | or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. 15 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) Microsoft Corporation. 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE 22 | -------------------------------------------------------------------------------- /SUPPORT.md: -------------------------------------------------------------------------------- 1 | # Support 2 | ## How to file issues and get help 3 | 4 | This project uses GitHub Issues to track bugs and feature requests. Please search the existing 5 | issues before filing new issues to avoid duplicates. For new issues, file your bug or 6 | feature request as a new Issue. 7 | 8 | For help and questions about using this project, file an issue. 9 | 10 | ## Microsoft Support Policy 11 | 12 | Support for this repository is limited to the resources listed above. 13 | -------------------------------------------------------------------------------- /TRANSLATIONS.md: -------------------------------------------------------------------------------- 1 | # Contribute by translating lessons 2 | 3 | We welcome translations for the lessons in this curriculum! 4 | 5 | > Note, please do not use Google Translate or other machine translation tools for translations, but rather use your own proficiency and manually translate the files. 6 | 7 | ## Guidelines 8 | 9 | There are folders in each lesson folder and lesson introduction folder which contain the translated markdown files. 10 | 11 | > Note, please do not translate any code in the code sample files; the only things to translate are README, assignments, and the quizzes. Thanks! 12 | 13 | Translated files should follow this naming convention: 14 | 15 | **README._[language]_.md** 16 | 17 | where _[language]_ is a two letter language abbreviation following the ISO 639-1 standard (e.g. `README.es.md` for Spanish and `README.nl.md` for Dutch). 18 | 19 | **assignment._[language]_.md** 20 | 21 | Similar to Readme's, please translate the assignments as well. 22 | 23 | **Quizzes** 24 | 25 | 1. Add your translation to the quiz-app by adding a file here: https://github.com/microsoft/Data-Science-For-Beginners/tree/main/quiz-app/src/assets/translations, with proper naming convention (en.json, fr.json). **Please don't localize the words 'true' or 'false' however. thanks!** 26 | 27 | 2. Add your language code to the dropdown in the quiz-app's App.vue file. 28 | 29 | 3. Edit the quiz-app's [translations index.js file](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/quiz-app/src/assets/translations/index.js) to add your language. 30 | 31 | 4. Finally, edit ALL the quiz links in your translated README.md files to point directly to your translated quiz: https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/1 becomes https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/1?loc=id 32 | 33 | **THANK YOU** 34 | 35 | We truly appreciate your efforts! 36 | -------------------------------------------------------------------------------- /data/form.csv: -------------------------------------------------------------------------------- 1 | birth_month,state,pet 2 | January,,Cats 3 | JAN,CA,Cats 4 | Sept,Hawaii,Dog 5 | january,AK,Dog 6 | July,RI,Cats 7 | September,California,Cats 8 | April,CA,Dog 9 | January,California,Cats 10 | November,FL,Dog 11 | December,Florida,Cats -------------------------------------------------------------------------------- /docs/_sidebar.md: -------------------------------------------------------------------------------- 1 | - Introduction 2 | - [Defining Data Science](../1-Introduction/01-defining-data-science/README.md) 3 | - [Ethics of Data Science](../1-Introduction/02-ethics/README.md) 4 | - [Defining Data](../1-Introduction/03-defining-data/README.md) 5 | - [Probability and Stats](../1-Introduction/04-stats-and-probability/README.md) 6 | - Working With Data 7 | - [Relational Databases](../2-Working-With-Data/05-relational-databases/README.md) 8 | - [Nonrelational Databases](../2-Working-With-Data/06-non-relational/README.md) 9 | - [Python](../2-Working-With-Data/07-python/README.md) 10 | - [Data Preparation](../2-Working-With-Data/08-data-preparation/README.md) 11 | - Data Visualization 12 | - [Visualizing Quantities](../3-Data-Visualization/09-visualization-quantities/README.md) 13 | - [Visualizing Distributions](../3-Data-Visualization/10-visualization-distributions/README.md) 14 | - [Visualizing Proportions](../3-Data-Visualization/11-visualization-proportions/README.md) 15 | - [Visualizing Relationships](../3-Data-Visualization/12-visualization-relationships/README.md) 16 | - [Meaningful Visualizations](../3-Data-Visualization/13-meaningful-visualizations/README.md) 17 | - Data Science Lifecycle 18 | - [Introduction](../4-Data-Science-Lifecycle/14-Introduction/README.md) 19 | - [Analyzing](../4-Data-Science-Lifecycle/15-analyzing/README.md) 20 | - [Communication](../4-Data-Science-Lifecycle/16-communication/README.md) 21 | - Data Science in the Cloud 22 | - [Introduction](../5-Data-Science-In-Cloud/17-Introduction/README.md) 23 | - [Low Code](../5-Data-Science-In-Cloud/18-Low-Code/README.md) 24 | - [Azure](../5-Data-Science-In-Cloud/19-Azure/README.md) 25 | - Data Science in the Wild 26 | - [DS In The Wild](../6-Data-Science-In-Wild/README.md) 27 | -------------------------------------------------------------------------------- /docsifytopdf.js: -------------------------------------------------------------------------------- 1 | module.exports = { 2 | contents: ['docs/_sidebar.md'], // array of "table of contents" files path 3 | pathToPublic: 'pdf/readme.pdf', // path where pdf will stored 4 | pdfOptions: { 5 | margin: { top: '100px', bottom: '100px' } 6 | }, // reference: https://github.com/GoogleChrome/puppeteer/blob/master/docs/api.md#pagepdfoptions 7 | removeTemp: true, // remove generated .md and .html or not 8 | emulateMedia: 'print', // mediaType, emulating by puppeteer for rendering pdf, 'print' by default (reference: https://github.com/GoogleChrome/puppeteer/blob/master/docs/api.md#pageemulatemediamediatype) 9 | }; 10 | -------------------------------------------------------------------------------- /ds-for-beginners.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/ds-for-beginners.gif -------------------------------------------------------------------------------- /ds-for-beginners.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/ds-for-beginners.png -------------------------------------------------------------------------------- /images/favicon.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/images/favicon.png -------------------------------------------------------------------------------- /index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | Data Science for Beginners 7 | 8 | 9 | 11 | 12 | 13 | 14 | 15 | 16 |
17 | 24 | 25 | 26 | 27 | 28 | -------------------------------------------------------------------------------- /package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "ds-for-beginners", 3 | "version": "1.0.0", 4 | "description": "Data Science for Beginners - A Curriculum", 5 | "main": "index.js", 6 | "scripts": { 7 | "convert": "node_modules/.bin/docsify-to-pdf" 8 | }, 9 | "repository": { 10 | "type": "git", 11 | "url": "git+https://github.com/microsoft/Data-Science-For-Beginners.git" 12 | }, 13 | "keywords": [ 14 | "machine", 15 | "learning", 16 | "ml", 17 | "ai", 18 | "curriculum" 19 | ], 20 | "author": "Jen Looper and team", 21 | "license": "MIT", 22 | "bugs": { 23 | "url": "https://github.com/microsoft/Data-Science-For-Beginners/issues" 24 | }, 25 | "homepage": "https://github.com/microsoft/Data-Science-For-Beginners#readme", 26 | "devDependencies": { 27 | "docsify-to-pdf": "0.0.5" 28 | } 29 | } 30 | -------------------------------------------------------------------------------- /quiz-app/.gitignore: -------------------------------------------------------------------------------- 1 | node_modules 2 | -------------------------------------------------------------------------------- /quiz-app/LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 Arpan Adhikari 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /quiz-app/babel.config.js: -------------------------------------------------------------------------------- 1 | module.exports = { 2 | presets: [ 3 | '@vue/cli-plugin-babel/preset' 4 | ] 5 | } 6 | -------------------------------------------------------------------------------- /quiz-app/dist/css/app.67375a05.css: -------------------------------------------------------------------------------- 1 | html{font-family:Avenir,Helvetica,Arial,sans-serif;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale;color:#252d4a}nav{background-color:#252d4a;padding:1em;margin-bottom:20px}nav a{color:#fff;text-align:right}ul{list-style-type:none;margin:0;padding:0;overflow:hidden}li{float:left}.title{color:#fff;font-weight:700;font-size:x-large;float:right}.link{display:list-item}.message,h1,h2,h3{text-align:center}.error{color:red}.complete{color:green}.card{width:60%;border:solid #252d4a;border-radius:5px;margin:auto;padding:1em}.btn{min-width:50%;text-align:center;cursor:pointer;margin-bottom:5px;width:50%;font-size:16px;color:#fff;background-color:#252d4a;border-radius:5px;padding:5px;justify-content:flex-start;align-items:center}.ans-btn{justify-content:center;display:flex;margin:4px auto} -------------------------------------------------------------------------------- /quiz-app/dist/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/quiz-app/dist/favicon.ico -------------------------------------------------------------------------------- /quiz-app/dist/index.html: -------------------------------------------------------------------------------- 1 | quizzes
-------------------------------------------------------------------------------- /quiz-app/dist/routes.json: -------------------------------------------------------------------------------- 1 | { 2 | "routes": [ 3 | { 4 | "route": "/*", 5 | "serve": "/index.html" 6 | } 7 | ] 8 | } 9 | -------------------------------------------------------------------------------- /quiz-app/package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "quizzes", 3 | "version": "0.1.0", 4 | "private": true, 5 | "scripts": { 6 | "serve": "vue-cli-service serve", 7 | "build": "vue-cli-service build", 8 | "lint": "vue-cli-service lint" 9 | }, 10 | "dependencies": { 11 | "core-js": "^3.6.5", 12 | "vue": "^2.6.11", 13 | "vue-i18n": "^8.22.2", 14 | "vue-router": "^3.4.9" 15 | }, 16 | "devDependencies": { 17 | "@vue/cli-plugin-babel": "~4.5.0", 18 | "@vue/cli-plugin-eslint": "~4.5.0", 19 | "@vue/cli-service": "~4.5.0", 20 | "babel-eslint": "^10.1.0", 21 | "eslint": "^6.7.2", 22 | "eslint-plugin-vue": "^6.2.2", 23 | "vue-template-compiler": "^2.6.11" 24 | }, 25 | "eslintConfig": { 26 | "root": true, 27 | "env": { 28 | "node": true 29 | }, 30 | "extends": [ 31 | "plugin:vue/essential", 32 | "eslint:recommended" 33 | ], 34 | "parserOptions": { 35 | "parser": "babel-eslint" 36 | }, 37 | "rules": {} 38 | }, 39 | "browserslist": [ 40 | "> 1%", 41 | "last 2 versions", 42 | "not dead" 43 | ] 44 | } 45 | -------------------------------------------------------------------------------- /quiz-app/public/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/Data-Science-For-Beginners/1824bfe1a72f5d5f2947f571cfbb2e442e30f924/quiz-app/public/favicon.ico -------------------------------------------------------------------------------- /quiz-app/public/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | <%= htmlWebpackPlugin.options.title %> 9 | 10 | 11 | 14 |
15 | 16 | 17 | 18 | -------------------------------------------------------------------------------- /quiz-app/public/routes.json: -------------------------------------------------------------------------------- 1 | { 2 | "routes": [ 3 | { 4 | "route": "/*", 5 | "serve": "/index.html" 6 | } 7 | ] 8 | } 9 | -------------------------------------------------------------------------------- /quiz-app/src/assets/translations/en/index.js: -------------------------------------------------------------------------------- 1 | import en0 from "./group-1.json"; 2 | import en1 from "./group-2.json"; 3 | import en2 from "./group-3.json"; 4 | 5 | import en3 from "./group-4.json"; 6 | 7 | import en4 from "./group-5.json"; 8 | 9 | import en5 from "./group-6.json"; 10 | 11 | 12 | const quiz = { 13 | 0: en0[0], 14 | 1: en1[0], 15 | 2: en2[0], 16 | 3: en3[0], 17 | 4: en4[0], 18 | 5: en5[0], 19 | }; 20 | 21 | export default quiz; 22 | -------------------------------------------------------------------------------- /quiz-app/src/assets/translations/es/index.js: -------------------------------------------------------------------------------- 1 | import es0 from './group-1.json'; 2 | import es1 from './group-2.json'; 3 | import es2 from './group-3.json'; 4 | 5 | import es3 from './group-4.json'; 6 | 7 | import es4 from './group-5.json'; 8 | 9 | import es5 from './group-6.json'; 10 | 11 | const quiz = { 12 | 0: es0[0], 13 | 1: es1[0], 14 | 2: es2[0], 15 | 3: es3[0], 16 | 4: es4[0], 17 | 5: es5[0], 18 | }; 19 | 20 | export default quiz; 21 | -------------------------------------------------------------------------------- /quiz-app/src/assets/translations/fr/index.js: -------------------------------------------------------------------------------- 1 | import fr0 from "./group-1.json"; 2 | //import fr1 from "./group-2.json"; 3 | 4 | const quiz = { 5 | 0: fr0[0], 6 | //1: fr1[0], 7 | }; 8 | 9 | export default quiz; 10 | -------------------------------------------------------------------------------- /quiz-app/src/assets/translations/index.js: -------------------------------------------------------------------------------- 1 | import englishQuizzes from './en/'; 2 | import frenchQuizzes from './fr/'; 3 | import spanishQuizzes from './es/'; 4 | 5 | const messages = { 6 | en: englishQuizzes, 7 | fr: frenchQuizzes, 8 | es: spanishQuizzes, 9 | }; 10 | export default messages; 11 | -------------------------------------------------------------------------------- /quiz-app/src/main.js: -------------------------------------------------------------------------------- 1 | import Vue from 'vue'; 2 | import App from './App.vue'; 3 | Vue.config.productionTip = false; 4 | import router from './router'; 5 | 6 | import VueI18n from 'vue-i18n'; 7 | Vue.use(VueI18n); 8 | 9 | const i18n = new VueI18n({ 10 | locale: 'en', 11 | fallbackLocale: 'en', 12 | }); 13 | 14 | new Vue({ i18n, router, render: (h) => h(App) }).$mount('#app'); 15 | -------------------------------------------------------------------------------- /quiz-app/src/router/index.js: -------------------------------------------------------------------------------- 1 | import Vue from 'vue'; 2 | import Router from 'vue-router'; 3 | import Home from '@/views/Home.vue'; 4 | import Quiz from '@/components/Quiz.vue'; 5 | import NotFound from '@/views/NotFound.vue'; 6 | Vue.use(Router); 7 | 8 | const router = new Router({ 9 | mode: 'history', 10 | base: process.env.BASE_URL, 11 | routes: [ 12 | { 13 | path: '/', 14 | name: 'home', 15 | component: Home, 16 | }, 17 | { 18 | path: '/quiz/:id', 19 | name: 'Quiz', 20 | component: Quiz, 21 | }, 22 | { 23 | path: '/:pathMatch(.*)*', 24 | name: 'NotFound', 25 | component: NotFound, 26 | meta: { title: 'Not Found' }, 27 | }, 28 | ], 29 | }); 30 | 31 | router.beforeEach((to, from, next) => { 32 | next(); 33 | }); 34 | export default router; 35 | 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-------------------------------------------------------------------------------- 1 | ## शिक्षकों के लिए 2 | 3 | क्या आप इस पाठ्यक्रम का उपयोग अपनी कक्षा में करना चाहेंगे? कृपया निःसंकोच करें! 4 | 5 | वास्तव में, आप इसे GitHub क्लासरूम का उपयोग करके GitHub के भीतर ही उपयोग कर सकते हैं। 6 | 7 | ऐसा करने के लिए, इस रेपो का प्रतिरूप करें। आपको प्रत्येक पाठ के लिए एक रेपो बनाने की आवश्यकता होगी, इसलिए आपको प्रत्येक फ़ोल्डर को एक अलग रेपो में निकालने की आवश्यकता होगी। इस तरह, [GitHub Classroom](https://classroom.github.com/classrooms) प्रत्येक पाठ को अलग से उठा सकता है। 8 | 9 | ये [पूरे निर्देश](https://github.blog/2020-03-18-set-up-your-digital-classroom-with-github-classroom/) से आपको पता चल जाएगा कि आप अपनी कक्षा कैसे सेट अप करें। 10 | 11 | ## रेपो का इस प्रकार उपयोग करना 12 | 13 | यदि आप इस रेपो का उपयोग करना चाहते हैं, जैसा कि यह वर्तमान में खड़ा है, बिना गिटहब क्लासरूम का उपयोग किए, यह भी किया जा सकता है। आपको अपने छात्रों के साथ संवाद करने की आवश्यकता होगी कि कौन सा पाठ एक साथ काम करना है। 14 | 15 | एक ऑनलाइन प्रारूप (ज़ूम, टीम, या अन्य) में आप क्विज़ के लिए ब्रेकआउट रूम बना सकते हैं, और छात्रों को सीखने के लिए तैयार होने में मदद करने के लिए सलाह दे सकते हैं। फिर छात्रों को प्रश्नोत्तरी के लिए आमंत्रित करें और एक निश्चित समय पर अपने उत्तर 'मुद्दों' के रूप में जमा करें। आप असाइनमेंट के साथ भी ऐसा ही कर सकते हैं, यदि आप चाहते हैं कि छात्र खुले में मिलकर काम करें। 16 | 17 | यदि आप अधिक निजी प्रारूप पसंद करते हैं, तो अपने छात्रों से पाठ्यक्रम, पाठ दर पाठ, निजी रिपो के रूप में अपने स्वयं के गिटहब रेपो में फोर्क करने के लिए कहें, और आपको पहुंच प्रदान करें। फिर वे क्विज़ और असाइनमेंट को निजी तौर पर पूरा कर सकते हैं और उन्हें आपके क्लासरूम रेपो पर मुद्दों के माध्यम से आपको सबमिट कर सकते हैं। 18 | 19 | ऑनलाइन कक्षा प्रारूप में इस काम को करने के कई तरीके हैं। कृपया हमें बताएं कि आपके लिए सबसे अच्छा क्या काम करता है! 20 | 21 | ## कृपया हमें अपने विचार दें! 22 | 23 | हम इस पाठ्यक्रम को आपके और आपके छात्रों के लिए कारगर बनाना चाहते हैं। कृपया हमें चर्चा बोर्डों में प्रतिक्रिया दें! 24 | --------------------------------------------------------------------------------