microsoft/Data-Science-For-Beginners là một trong những repo tập trung dữ liệu mà TopGit theo dõi, hiện có 37.2k sao, viết chủ yếu bằng Jupyter Notebook. 10 Weeks, 20 Lessons, Data Science for All!
Tóm tắt dựng từ metadata GitHub của chính dự án — chưa có bài review TopGit. Trang sẽ tự động cập nhật khi bài review đầy đủ được xuất bản.
VÌ SAO CHƯA CÓ REVIEW
TopGit viết bài đầy đủ cho repo có nhiều sao nhất và được yêu cầu nhiều nhất. Trang này là snapshot trong thời gian chờ — xem README gốc ở tab READ ME.
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 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
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 | 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
Prefer to Clone Locally?
This repository includes 50+ language translations which significantly increases the download size. To 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"
This gives you everything you need to complete the course with a much faster download.
If you wish to have additional translations languages supported are listed here
Join Our Community
We have a Discord learn with AI series ongoing, learn more and join us at Learn with AI Series from 18 - 30 September, 2025. You will get tips and tricks of using GitHub Copilot for Data Science.
Are you a student?
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.
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: New to data science? Start with our beginner-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 recommend Microsoft Learn.
Quick Start:
Check the Installation Guide to set up your environment
Review the Usage Guide to learn how to work with the curriculum
Start with Lesson 1 and work through sequentially
Join our Discord community for support
👩🏫 For Teachers
Teachers: we have included some suggestions on how to use this curriculum. We'd love your feedback in our discussion forum!
Meet the Team
Gif by Mohit Jaisal
🎥 Click the image above for a video about the project the folks who created it!
Pedagogy
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 our Code of Conduct, Contributing, 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 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 the quiz-app folder. They are gradually being localized.
🎓 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 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 👈
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:
Click the Code drop-down menu and select the Open with Codespaces option.
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:
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:
LangChain
Azure / Edge / MCP / Agents
Generative AI Series
Core Learning
Copilot Series
Getting Help
Encountering issues? Check our Troubleshooting 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:
microsoft/Data-Science-For-Beginners thuộc nhóm Data trên TopGit, cùng 6 topic GitHub. Trang Trending và Topics liệt kê các repo cùng số sao và cùng ngôn ngữ để so sánh.
Đọc thêm về microsoft/Data-Science-For-Beginners ở đâu?
Trang TopGit này là một snapshot — tab "Readme" hiển thị nguyên văn README của repo (đã bỏ link, giữ ảnh). Repo GitHub ở github.com/microsoft/Data-Science-For-Beginners là nguồn chính thức.
microsoft/Data-Science-For-Beginners có phải mã nguồn mở không?
Có — microsoft/Data-Science-For-Beginners phát hành theo license MIT, nghĩa là mã nguồn mở để đọc, fork và (tùy license) tái sử dụng. Mã: github.com/microsoft/Data-Science-For-Beginners.
microsoft/Data-Science-For-Beginners có website riêng không?
TopGit chưa ghi nhận URL trang chủ cho microsoft/Data-Science-For-Beginners. Phần README ở tab phía trên thường có link demo, hoặc xem mô tả GitHub của repo.
microsoft/Data-Science-For-Beginners dùng license gì?
microsoft/Data-Science-For-Beginners phát hành theo license MIT. Nên mở file LICENSE trên GitHub để xác nhận — license metadata đôi khi lệch với thực tế dự án.
microsoft/Data-Science-For-Beginners là gì?
microsoft/Data-Science-For-Beginners (microsoft/Data-Science-For-Beginners) là dự án Jupyter Notebook trên GitHub. Theo mô tả gốc: 10 Weeks, 20 Lessons, Data Science for All!
Vì sao microsoft/Data-Science-For-Beginners được xếp vào nhóm Data?
TopGit xếp microsoft/Data-Science-For-Beginners vào nhóm Data dựa trên GitHub topics và mô tả của repo (gắn thẻ: "data-analysis", "data-science", "data-visualization"). Việc phân loại dựa trên metadata thật của repo, không phải đoán theo cảm tính biên tập.
Đọc đầy đủ README ở tab phía trên.
Muốn nghe thêm một ý kiến về Data-Science-For-Beginners?
Hỏi một AI đọc được trang này — một cú bấm là có ngay nhận định về Data-Science-For-Beginners.
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