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Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson curriculum all about Data Science. Each lesson includes pre-lesson and post-lesson quizzes, written instructions to complete the lesson, a solution, and an assignment. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.

Hearty thanks to our authors:Jasmine Greenaway,Dmitry Soshnikov,Nitya Narasimhan,Jalen McGee,Jen Looper,Maud Levy,Tiffany Souterre,Christopher Harrison.

🙏 Special thanks 🙏 to ourMicrosoft 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

Data Science For Beginners -Sketchnote by@nitya

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If you wish to have additional translations languages supported are listedhere

We have a Discord learn with AI series ongoing, learn more and join us atLearn with AI Series from 18 - 30 September, 2025. You will get tips and tricks of using GitHub Copilot for Data Science.

Get started with the following resources:

• Student Hub page In this page, you will find beginner resources, Student packs and even ways to get a free cert voucher. This is one page you want to bookmark and check from time to time as we switch out content at least monthly.
• Microsoft Learn Student Ambassadors Join a global community of student ambassadors, this could be your way into Microsoft.

• Installation Guide - Step-by-step setup instructions for beginners
• Usage Guide - Examples and common workflows
• Troubleshooting - Solutions to common issues
• Contributing Guide - How to contribute to this project
• For Teachers - Teaching guidance and classroom resources

Complete Beginners: New to data science? Start with ourbeginner-friendly examples! These simple, well-commented examples will help you understand the basics before diving into the full curriculum.Students: to use this curriculum on your own, fork the entire repo and complete the exercises on your own, starting with a pre-lecture quiz. Then read the lecture and complete the rest of the activities. Try to create the projects by comprehending the lessons rather than copying the solution code; however, that code is available in the /solutions folders in each project-oriented lesson. Another idea would be to form a study group with friends and go through the content together. For further study, we recommendMicrosoft Learn.

Quick Start:

1. Check theInstallation Guide to set up your environment
2. Review theUsage Guide to learn how to work with the curriculum
3. Start with Lesson 1 and work through sequentially
4. Join ourDiscord community for support

Teachers: we haveincluded some suggestions on how to use this curriculum. We'd love your feedbackin our discussion forum!

Gif byMohit Jaisal

🎥 Click the image above for a video about the project the folks who created it!

We have chosen two pedagogical tenets while building this curriculum: ensuring that it is project-based and that it includes frequent quizzes. By the end of this series, students will have learned basic principles of data science, including ethical concepts, data preparation, different ways of working with data, data visualization, data analysis, real-world use cases of data science, and more.

In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 10 week cycle.

Find ourCode of Conduct,Contributing,Translation guidelines. We welcome your 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
• A challenge
• Supplemental reading
• Assignment
• Post-lesson quiz

A note about quizzes: All quizzes are contained in the Quiz-App folder, for 40 total quizzes of three questions each. They are linked from within the lessons, but the quiz app can be run locally or deployed to Azure; follow the instruction in thequiz-app folder. They are gradually being localized.

New to Data Science? We've created a specialexamples directory with simple, well-commented code to help you get started:

• 🌟Hello World - Your first data science program
• 📂Loading Data - Learn 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

Each example includes detailed comments explaining every step, making it perfect for absolute beginners!

👉Start with the examples 👈

Data Science For Beginners: Roadmap -Sketchnote by@nitya

Follow these steps to open this sample in a Codespace:

1. Click the Code drop-down menu and select the Open with Codespaces option.
2. Select + New codespace at the bottom on the pane.For more info, check out theGitHub documentation.

Follow these steps to open this repo in a container using your local machine and VSCode using the VS Code Remote - Containers extension:

1. If this is your first time using a development container, please ensure your system meets the pre-reqs (i.e. have Docker installed) inthe getting started documentation.

To use this repository, you can either open the repository in an isolated Docker volume:

Note: Under the hood, this will use the Remote-Containers:Clone Repository in Container Volume... command to clone the source code in a Docker volume instead of the local filesystem.Volumes are the preferred mechanism for persisting container data.

Or open a locally cloned or downloaded version of the repository:

• Clone this repository to your local filesystem.
• Press F1 and select theRemote-Containers: Open Folder in Container... command.
• Select the cloned copy of this folder, wait for the container to start, and try things out.

You can run this documentation offline by usingDocsify. Fork this repo,install Docsify on your local machine, then in the root folder of this repo, typedocsify serve. The website will be served on port 3000 on your localhost:localhost:3000.

Note, notebooks will not be rendered via Docsify, so when you need to run a notebook, do that separately in VS Code running a Python kernel.

Our team produces other curricula! Check out:

Encountering issues? Check ourTroubleshooting Guide for solutions to common problems.

If you get stuck or have any questions about building AI apps. Join fellow learners and experienced developers in discussions about MCP. It's a supportive community where questions are welcome and knowledge is shared freely.

If you have product feedback or errors while building visit: