Azure Cloud Advocates for Microsoft happy to offer 10-week, 20-lesson curriculum wey dey all about Data Science. Each lesson get pre-lesson and post-lesson quizzes, written instructions to complete lesson, solution, plus assignment. Our project-based way to teach dey allow you learn as you dey build, dat na correct way for new skills to 'stick'.
Big thanks to our authors: Jasmine Greenaway, Dmitry Soshnikov, Nitya Narasimhan, Jalen McGee, Jen Looper, Maud Levy, Tiffany Souterre, Christopher Harrison.
🙏 Special thanks 🙏 to our Microsoft Student Ambassador authors, reviewers and content contributors, notably Aaryan Arora, Aditya Garg, Alondra Sanchez, Ankita Singh, Anupam Mishra, Arpita Das, ChhailBihari Dubey, Dibri Nsofor, Dishita Bhasin, Majd Safi, Max Blum, Miguel Correa, Mohamma Iftekher (Iftu) Ebne Jalal, Nawrin Tabassum, Raymond Wangsa Putra, Rohit Yadav, Samridhi Sharma, Sanya Sinha, Sheena Narula, Tauqeer Ahmad, Yogendrasingh Pawar , Vidushi Gupta, Jasleen Sondhi
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| Data Science For Beginners - Sketchnote by @nitya |
Arabic | Bengali | Bulgarian | Burmese (Myanmar) | Chinese (Simplified) | Chinese (Traditional, Hong Kong) | Chinese (Traditional, Macau) | Chinese (Traditional, Taiwan) | Croatian | Czech | Danish | Dutch | Estonian | Finnish | French | German | Greek | Hebrew | Hindi | Hungarian | Indonesian | Italian | Japanese | Kannada | Korean | Lithuanian | Malay | Malayalam | Marathi | Nepali | Nigerian Pidgin | Norwegian | Persian (Farsi) | Polish | Portuguese (Brazil) | Portuguese (Portugal) | Punjabi (Gurmukhi) | Romanian | Russian | Serbian (Cyrillic) | Slovak | Slovenian | Spanish | Swahili | Swedish | Tagalog (Filipino) | Tamil | Telugu | Thai | Turkish | Ukrainian | Urdu | Vietnamese
Prefer to Clone Locally?
This repository get 50+ language translations wey go increase di download size well well. To clone without di translations, use sparse checkout:
Bash / macOS / Linux:
git clone --filter=blob:none --sparse https://github.com/microsoft/Data-Science-For-Beginners.git cd Data-Science-For-Beginners git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'CMD (Windows):
git clone --filter=blob:none --sparse https://github.com/microsoft/Data-Science-For-Beginners.git cd Data-Science-For-Beginners git sparse-checkout set --no-cone "/*" "!translations" "!translated_images"Dis one go give you everytin you need to finish di course with much faster download.
If you want make dem add more translation languages, di ones wey dem dey support dey listed here
We get Discord learn with AI series wey dey go on, learn more and join us for Learn with AI Series from 18 - 30 September, 2025. You go fit get tips and tricks on how to use GitHub Copilot for Data Science.
Start with these resources:
- Student Hub page For dis page, you go find beginner resources, Student packs plus ways to get free cert voucher. Dis na one page wey you for bookmark and come check sometimes as we dey change di content at least every month.
- Microsoft Learn Student Ambassadors Join one global community of student ambassadors, dis fit be your way enter Microsoft.
- Installation Guide - Step-by-step setup instructions for beginners
- Usage Guide - Examples and common workflows
- Troubleshooting - Solutions to common wahala
- Contributing Guide - How to contribute to dis project
- For Teachers - Teaching guidance and classroom resources
Complete Beginners: You be new for data science? Start from our beginner-friendly examples! These simple, well-commented examples go help you understand basics before you enter full curriculum. Students: To use this curriculum by yourself, fork di full repo and finish all di exercises on your own, start with pre-lecture quiz. Then read di lecture and finish di rest activities. Try create di projects by understanding di lessons not just copy di solution code; but di code still dey for the /solutions folders for each project-oriented lesson. Another idea na to form study group with friends make una go through di content together. For more study, we recommend Microsoft Learn.
Quick Start:
- Check di Installation Guide make you set up your environment
- Review di Usage Guide to sabi how to take work with di curriculum
- Start with Lesson 1 and waka through am step by step
- Join our Discord community for support
Teachers: we don included some suggestions on how to use dis curriculum. We go like make una give feedback for our discussion forum!
Gif by Mohit Jaisal
🎥 Click di image wey dey top for see video about di project and di people wey create am!
We don choose two main tings for how we go teach dis curriculum: to make sure say e get project-based learning and sey e get plenty quizzes. By di time we finish dis series, students go don sabi di basic principles of data science, including ethical concepts, how to prepare data, different ways to work with data, how to do data visualization, data analysis, real-world use cases of data science, plus more.
Plus, one low-stakes quiz wey de happen before class go set di student mind for learning di topic, and the second quiz after class go make sure dem remember well-well. Dis curriculum design make am flexible and fun, and you fit do am all or just part. Di projects start small-small then dem go hard as you near di 10 week period.
Find our Code of Conduct, Contributing, Translation guidelines. We dey welcome una constructive feedback!
- Optional sketchnote
- Optional supplemental video
- Pre-lesson warmup quiz
- Written lesson
- For project-based lessons, step-by-step guides on how to build the project
- Knowledge checks
- One challenge
- Supplemental reading
- Assignment
- Post-lesson quiz
About quizzes: All di quizzes dey inside di Quiz-App folder, total of 40 quizzes with three questions each. Dem linked from inside di lessons, but you fit run di quiz app locally or deploy am for Azure; follow di instruction for di
quiz-appfolder. Dem dey localize am small-small.
New for Data Science? We create special examples directory with simple, well-commented code wey go help you start:
- 🌟 Hello World - Your first data science program
- 📂 Loading Data - Learn how to read and explore datasets
- 📊 Simple Analysis - Calculate statistics and find patterns
- 📈 Basic Visualization - Create charts and graphs
- 🔬 Real-World Project - Complete workflow from start to finish
Every example get detailed comments to explain every step, so e perfect for total beginners!
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| Data Science For Beginners: Roadmap - Sketchnote by @nitya |
| Lesson Number | Topic | Lesson Grouping | Learning Objectives | Linked Lesson | Author |
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| 01 | Defining Data Science | Introduction | Learn the basic concepts behind data science and how e relate to artificial intelligence, machine learning, and big data. | lesson video | Dmitry |
| 02 | Data Science Ethics | Introduction | Data Ethics Concepts, Challenges & Frameworks. | lesson | Nitya |
| 03 | Defining Data | Introduction | How data dey classified and di common sources. | lesson | Jasmine |
| 04 | Introduction to Statistics & Probability | Introduction | Di mathematical techniques of probability and statistics to understand data. | lesson video | Dmitry |
| 05 | Working with Relational Data | Working With Data | Introduction to relational data and basics of exploring and analyzing relational data with Structured Query Language, wey dem call SQL (pronounced “see-quell”). | lesson | Christopher |
| 06 | Working with NoSQL Data | Working With Data | Introduction to non-relational data, wetin different types and basics of exploring and analyzing document databases. | lesson | Jasmine |
| 07 | Working with Python | Working With Data | Basics of using Python for data exploration with libraries like Pandas. E good if you sabi Python programming first. | lesson video | Dmitry |
| 08 | Data Preparation | Working With Data | Topics on data techniques for cleaning and transforming data to handle missing, inaccurate, or incomplete data. | lesson | Jasmine |
| 09 | Visualizing Quantities | Data Visualization | Learn how to use Matplotlib to visualize bird data 🦆 | lesson | Jen |
| 10 | Visualizing Distributions of Data | Data Visualization | Visualizing observations and trends inside interval. | lesson | Jen |
| 11 | Visualizing Proportions | Data Visualization | Visualizing discrete and grouped percentages. | lesson | Jen |
| 12 | Visualizing Relationships | Data Visualization | Visualizing connections and correlations between sets of data and their variables. | lesson | Jen |
| 13 | Meaningful Visualizations | Data Visualization | Techniques and guidance for making your visualizations valuable for effective problem solving and insights. | lesson | Jen |
| 14 | Introduction to the Data Science lifecycle | Lifecycle | Introduction to di data science lifecycle and di first step of acquiring and extracting data. | lesson | Jasmine |
| 15 | Analyzing | Lifecycle | Dis phase of di data science lifecycle dey focus on techniques to analyze data. | lesson | Jasmine |
| 16 | Communication | Lifecycle | Dis phase of di data science lifecycle dey focus on presenting di insights from data in a way wey go make am easy for decision makers to understand. | lesson | Jalen |
| 17 | Data Science in the Cloud | Cloud Data | Dis series of lessons introduce data science for cloud and di benefits. | lesson | Tiffany and Maud |
| 18 | Data Science in the Cloud | Cloud Data | Training models using Low Code tools. | lesson | Tiffany and Maud |
| 19 | Data Science in the Cloud | Cloud Data | Deploying models with Azure Machine Learning Studio. | lesson | Tiffany and Maud |
| 20 | Data Science in the Wild | In the Wild | Data science driven projects for real world. | lesson | Nitya |
Follow dis steps to open dis sample inside Codespace:
- Click Code drop-down menu and select di Open with Codespaces option.
- Select + New codespace for di bottom for di pane. For more info, check di GitHub documentation.
Follow dis steps to open dis repo in container using your local machine and VSCode with di VS Code Remote - Containers extension:
- If na your first time to use development container, make sure say your system get all wetin e need (like Docker) by checking di getting started documentation.
To use dis repository, you fit either open di repo in isolated Docker volume:
Note: Under di hood, dis one go use Remote-Containers: Clone Repository in Container Volume... command to clone di source code to Docker volume instead of local filesystem. Volumes na di preferred way to keep container data.
Or open locally cloned or downloaded version of di repository:
- Clone dis repo to your local filesystem.
- Press F1 and select Remote-Containers: Open Folder in Container... command.
- Select di cloned copy of dis folder, wait make container start, then try am out.
You fit run dis documentation offline by using Docsify. Fork dis repo, install Docsify for your local machine, then for di root folder of dis repo, type docsify serve. Di website go dey served on port 3000 on your localhost: localhost:3000.
Note, notebooks no go render with Docsify, so if you need run notebook, do am separately in VS Code with Python kernel.
Our team dey produce other curricula! Check am out:
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You dey get wahala? Check our Troubleshooting Guide for solutions to common problems.
If you jam stuck or get any question about how to build AI apps. Join other learners plus experienced developers for discussions about MCP. Na community wey dey support, where questions dey welcome and knowledge dey share freely.
If you get product feedback or errors wen you dey build, visit:
Disclaimer:
Dis document na AI translation service Co-op Translator wey translate am. Even though we try make e correct, abeg sabi say automated translation fit get some mistake or no correct. Di original document wey dey im own language na di correct one. If na serious tin you dey check, better make professional human translate am. We no go responsible for any wahala or misunderstanding wey fit happen because of this translation.