Azure Cloud Advocates wey dey Microsoft happy to offer 10 weeks, 20 lessons curriculum wey dey all about Data Science. Each lesson get pre-lesson and post-lesson quizzes, written instructions to complete the lesson, solution, plus assignment. Our project-based teaching style dey allow you learn while you dey build, na beta 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, especially 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 | Khmer | 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
You prefer to Clone Locally?
Dis repository get 50+ language translations wey go make di download size plenty. If you wan clone without 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 everything you need to finish di course quick quick.
If you want make we support more translation languages dey listed here
We get Discord learn with AI series wey still dey go on, learn more and join us at Learn with AI Series from 18 - 30 September, 2025. You go 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 even ways to collect free cert voucher. Dis one na page wey you suppose bookmark and dey check time to time as we dey update content every month.
- Microsoft Learn Student Ambassadors Join 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 normal workflows
- Troubleshooting - Solutions to common problems
- Contributing Guide - How to contribute to this project
- For Teachers - Teaching guide and classroom resources
Complete Beginners: You new to data science? Start with our beginner-friendly examples! These easy, well-explained examples go help you understand the basics before you jump enter the full curriculum. Students: to use this curriculum by yourself, fork the whole repo and finish all exercises yourself, start with pre-lecture quiz. Then read the lecture and complete all the other activities. Try create the projects by understanding the lessons instead to just copy the solution code; but dat code dey inside /solutions folders for every project-based lesson. Another idea na to gather study group with your friends and go through content together. For more study, we recommend Microsoft Learn.
Quick Start:
- Check the Installation Guide to set up your environment
- Read the Usage Guide to learn how to work with this curriculum
- Start with Lesson 1 and dey do am step by step
- Join our Discord community for support
Teachers: we don don put some suggestions on how to use dis curriculum. We go like make you give us your feedback for our discussion forum!
Gif by Mohit Jaisal
🎥 Click di image wey dey up for video about di project di people wey create am!
We don choose two teaching principles as we dey build dis curriculum: make e dey project-based and make e get frequent quizzes. By di end of dis series, students go don learn 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.
Plus, low-stakes quiz before class set di mind of di student to learn the topic, while after-class quiz go make dem remember better. Dis curriculum design make e flexible and fun, and e fit be done as whole or just part. Di projects dey start small and dey grow complex as di 10 weeks go finish.
Find our Code of Conduct, Contributing, Translation guidelines. We dey 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 dey for the Quiz-App folder, with 40 total quizzes of three questions each. Dem dey link am from lessons, but quiz app fit run locally or fit deployed for Azure; follow the instructions for
quiz-appfolder. Dem dey localize am little by little.
New to Data Science? We don create special examples directory with simple, well-commented code to help you start:
- 🌟 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
Every example get detailed comments wey explain every step, e perfect for absolute 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 di 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 where e commonly dey come from. | lesson | Jasmine |
| 04 | Introduction to Statistics & Probability | Introduction | Di mathematical methods of probability and statistics to sabi data well. | 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 dey call SQL (pronounced “see-quell”). | lesson | Christopher |
| 06 | Working with NoSQL Data | Working With Data | Introduction to non-relational data, im 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 basic Python programming before. | lesson video | Dmitry |
| 08 | Data Preparation | Working With Data | Topics on data techniques for cleaning and transforming data to handle challenges like missing, inaccurate, or incomplete data. | lesson | Jasmine |
| 09 | Visualizing Quantities | Data Visualization | Learn how to use Matplotlib for visualize bird data 🦆 | lesson | Jen |
| 10 | Visualizing Distributions of Data | Data Visualization | Visualize observations and trends inside interval. | lesson | Jen |
| 11 | Visualizing Proportions | Data Visualization | Visualize 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 the data science lifecycle and im first step of acquiring and extracting data. | lesson | Jasmine |
| 15 | Analyzing | Lifecycle | Dis phase for data science lifecycle focus on techniques to analyze data. | lesson | Jasmine |
| 16 | Communication | Lifecycle | Dis phase for data science lifecycle dey focus on presenting insights from data in 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 dey 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 these steps to open dis sample in a Codespace:
- Click di Code drop-down menu and select the Open with Codespaces option.
- Select + New codespace for bottom of di pane. For more info, check di GitHub documentation.
Follow these steps to open dis repo in container using your local machine and VSCode with di VS Code Remote - Containers extension:
- If dis na your first time to use development container, make sure your system get di pre-reqs (like Docker installed) for the getting started documentation.
To use dis repository, you fit open am in isolated Docker volume:
Note: For under di hood, dis go use Remote-Containers: Clone Repository in Container Volume... command to clone di source code in Docker volume instead of local filesystem. Volumes na di preferred way for keep container data.
Or open locally cloned or downloaded version of di repository:
- Clone dis repository to your local filesystem.
- Press F1 and select Remote-Containers: Open Folder in Container... command.
- Pick di cloned copy of dis folder, wait make container start, then try am.
You fit run dis documentation offline by using Docsify. Fork dis repo, install Docsify for your local machine, then for root folder of dis repo, type docsify serve. Website go run for port 3000 for your localhost: localhost:3000.
Note, notebooks no go render for Docsify, so if you need run notebook, do am separately inside 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 how to solve common issues.
If you jam problem or get any question about how to build AI apps. Join other learners and beta developers for inside talks about MCP. Na community wey dey support, dem welcome questions and dem dey share knowledge freely.
If you get product feedback or you see errors when you dey build, visit:
Disclaimer: Dis document don translate wit AI translation service Co-op Translator. Even though we dey try make am correct, abeg sabi say automated translation fit get errors or mistakes. Di original document for im own language na di main correct source. For important information, make person wey sabi human translation translate am. We no go fit take any blame if pesin misunderstand or misinterpret di translation wey dis one produce.