Data-Science-For-Beginners/translations/en

Data Science for Beginners - A Curriculum

Azure Cloud Advocates at Microsoft are excited to present a 10-week, 20-lesson curriculum focused on Data Science. Each lesson includes pre-lesson and post-lesson quizzes, detailed instructions, solutions, and assignments. This project-based approach ensures you learn effectively by building and applying your skills.

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

🙏 A big thank you 🙏 to our Microsoft Student Ambassador authors, reviewers, and contributors, including 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

🌐 Multi-Language Support

Supported via GitHub Action (Automated & Always Up-to-Date)

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 | Korean | Lithuanian | Malay | Marathi | Nepali | Norwegian | Persian (Farsi) | Polish | Portuguese (Brazil) | Portuguese (Portugal) | Punjabi (Gurmukhi) | Romanian | Russian | Serbian (Cyrillic) | Slovak | Slovenian | Spanish | Swahili | Swedish | Tagalog (Filipino) | Tamil | Thai | Turkish | Ukrainian | Urdu | Vietnamese

If you'd like additional translations, supported languages are listed here

Join Our Community

We are hosting a Discord Learn with AI series from September 18 - 30, 2025. Join us to learn tips and tricks for using GitHub Copilot in Data Science. Learn more and join here.

Are you a student?

Get started with these resources:

• Student Hub page: Find beginner resources, student packs, and even ways to get a free certification voucher. Bookmark this page and check back regularly for updated content.
• Microsoft Learn Student Ambassadors: Join a global community of student ambassadors and explore opportunities to connect with Microsoft.

Getting Started

📚 Documentation

• 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

👨‍🎓 For Students

Complete Beginners: If you're new to data science, start with our beginner-friendly examples! These simple, well-commented examples will help you grasp the basics before diving into the full curriculum. Students: To use this curriculum independently, fork the repository and complete the exercises starting with the pre-lecture quiz. Read the lecture and complete the activities. Try to build the projects by understanding the lessons rather than copying the solution code (available in the /solutions folders for each project-based lesson). Alternatively, form a study group with friends and go through the content together. For further study, we recommend Microsoft Learn.

Quick Start:

1. Follow the Installation Guide to set up your environment.
2. Check out the Usage Guide to learn how to navigate the curriculum.
3. Begin with Lesson 1 and progress sequentially.
4. Join our Discord community for support.

👩‍🏫 For Teachers

Teachers: We've included suggestions on how to use this curriculum effectively. Share your feedback in our discussion forum!

Meet the Team

Gif by Mohit Jaisal

🎥 Click the image above to watch a video about the project and the team behind it!

Pedagogy

We have selected two key educational principles while designing this curriculum: ensuring it is project-based and includes frequent quizzes. By the end of this series, students will have learned fundamental concepts of data science, including ethical considerations, data preparation, various methods of working with data, data visualization, data analysis, real-world applications of data science, and more.

Additionally, a low-pressure quiz before each class helps students focus on the topic, while a second quiz after class reinforces their learning. This curriculum is designed to be flexible and enjoyable, and can be completed in full or in part. The projects start small and gradually become more complex by the end of the 10-week cycle.

Check out our Code of Conduct, Contributing, and Translation guidelines. We welcome your constructive feedback!

Each lesson includes:

• 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 located in the Quiz-App folder, with a total of 40 quizzes, each containing three questions. They are linked within the lessons, but the quiz app can be run locally or deployed to Azure; follow the instructions in the quiz-app folder. Localization is ongoing.

🎓 Beginner-Friendly Examples

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

• 🌟 Hello World - Your first data science program
• 📂 Loading Data - Learn how to read and explore datasets
• 📊 Simple Analysis - Calculate statistics and identify 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 ideal for absolute beginners!

👉 Start with the examples 👈

Lessons

Data Science For Beginners: Roadmap - Sketchnote by @nitya

Lesson Number	Topic	Lesson Grouping	Learning Objectives	Linked Lesson	Author
01	Defining Data Science	Introduction	Learn the basic concepts behind data science and how it’s related 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 is classified and its common sources.	lesson	Jasmine
04	Introduction to Statistics & Probability	Introduction	The 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 the basics of exploring and analyzing relational data with the Structured Query Language, also known as SQL (pronounced “see-quell”).	lesson	Christopher
06	Working with NoSQL Data	Working With Data	Introduction to non-relational data, its various types and the 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 such as Pandas. Foundational understanding of Python programming is recommended.	lesson video	Dmitry
08	Data Preparation	Working With Data	Topics on data techniques for cleaning and transforming the data to handle challenges of 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 within an 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 the data science lifecycle and its first step of acquiring and extracting data.	lesson	Jasmine
15	Analyzing	Lifecycle	This phase of the data science lifecycle focuses on techniques to analyze data.	lesson	Jasmine
16	Communication	Lifecycle	This phase of the data science lifecycle focuses on presenting the insights from the data in a way that makes it easier for decision makers to understand.	lesson	Jalen
17	Data Science in the Cloud	Cloud Data	This series of lessons introduces data science in the cloud and its 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 in the real world.	lesson	Nitya

GitHub Codespaces

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 the GitHub documentation.

VSCode Remote - Containers

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) in the 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 the Remote-Containers: Open Folder in Container... command.
• Select the cloned copy of this folder, wait for the container to start, and try things out.

Offline access

You can run this documentation offline by using Docsify. Fork this repo, install Docsify on your local machine, then in the root folder of this repo, type docsify 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.

Other Curricula

Our team produces other curricula! Check out:

Azure / Edge / MCP / Agents

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Generative AI Series

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Core Learning

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Copilot Series

Getting Help

Having issues? Check out our Troubleshooting Guide for solutions to common problems.

If you're stuck or have questions about building AI applications, join:

For product feedback or errors while building, visit:

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Disclaimer:
This document has been translated using the AI translation service Co-op Translator. While we aim for accuracy, please note that automated translations may include errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is advised. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.