Data-Science-For-Beginners/translations/pcm

Data Science for Beginners - A Curriculum

Azure Cloud Advocates for Microsoft dey 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 the lesson, solution, and assignment. Our project-based way of teaching dey allow you learn as you dey build, na proven 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

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 | 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

If you want make we add more translation languages, dem dey listed here

Join Our Community

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 get tips and tricks on how to use GitHub Copilot for Data Science.

You be student?

Start with these resources:

• Student Hub page For dis page, you go find beginner resources, Student packs and even ways to get free cert voucher. Na one page you go want bookmark and check from time to time as we dey change content at least every month.
• Microsoft Learn Student Ambassadors Join global community of student ambassadors, dis fit be your way enter Microsoft.

How to Start

📚 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 go help you understand the basics before you dive into the full curriculum. Students: to use this curriculum on your own, fork the whole repo and complete the exercises by yourself, starting with pre-lecture quiz. Then read the lecture and complete the rest activities. Try to create the projects by understanding the lessons instead of just copying the solution code; but that code dey available for /solutions folders inside each project-oriented lesson. Another idea na to form study group with friends and go through the content together. For more study, we recommend Microsoft Learn.

Quick Start:

1. Check the Installation Guide to set up your environment
2. Review the Usage Guide to learn how to work with the curriculum
3. Start with Lesson 1 and work through am one by one
4. Join our Discord community for support

👩‍🏫 For Teachers

Teachers: we don include some suggestions on how to use this curriculum. We go like hear your feedback for our discussion forum!

Meet the Team

Gif by Mohit Jaisal

🎥 Click di image wey dey above for video about di project an di people wey create am!

Pedagogy

We don choose two pedagogy principles wen we dey build dis curriculum: make sure say e base on project and say e get plenty quizzes. By di time dis series finish, students go don learn basic principles of data science, including ethical concepts, data preparation, different ways to work with data, data visualization, data analysis, real-world use cases of data science, and more.

Plus, one low-stakes quiz before class dey set di student mind to learn di topic, while di second quiz after class dey make sure say dem still remember well. Dis curriculum na flexible and fun one and you fit take am complete or part. Di projects start small and dem go dey more complex as di 10 week cycle dey finish.

Find our Code of Conduct, Contributing, Translation guidelines. We dey welcome your constructive feedback!

Each lesson get:

• Optional sketchnote
• Optional supplemental video
• Pre-lesson warmup quiz
• Written lesson
• For project-based lessons, step-by-step guides on how to build di project
• Knowledge checks
• One challenge
• Supplemental reading
• Assignment
• Post-lesson quiz

One note about quizzes: All quizzes dey inside di Quiz-App folder, total 40 quizzes with three questions each. Dem link am from inside di lessons, but di quiz app fit run locally or you fit deploy am to Azure; follow di instruction inside di quiz-app folder. Dem dey slowly dey localize am.

🎓 Beginner-Friendly Examples

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 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

Each example get detailed comments wey explain every step, e 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 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 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 di basics of exploring and analyzing relational data with di Structured Query Language, wey dem also dey call SQL (pronounced “see-quell”).	lesson	Christopher
06	Working with NoSQL Data	Working With Data	Introduction to non-relational data, di different types and di 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 make you sabi Python programming small.	lesson video	Dmitry
08	Data Preparation	Working With Data	Topics on data techniques for cleaning and transforming 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 inside one 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 focus on techniques to analyze data.	lesson	Jasmine
16	Communication	Lifecycle	Dis phase of di data science lifecycle focus on presenting di insights from di 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

GitHub Codespaces

Follow dis steps to open dis sample for Codespace:

1. Click di Code drop-down menu and select di Open with Codespaces option.
2. Select + New codespace for bottom of di pane. For more info, check di GitHub documentation.

VSCode Remote - Containers

Follow dis steps to open dis repo inside container using your local machine and VSCode with di VS Code Remote - Containers extension:

1. If na your first time to use development container, make sure say your system get di pre-reqs (like Docker installed) for di getting started documentation.

To use dis repository, you fit either open di repository inside isolated Docker volume:

Note: Under di hood, dis one go use di Remote-Containers: Clone Repository in Container Volume... command to clone di source code inside 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 repository to your local filesystem.
• Press F1 and select di Remote-Containers: Open Folder in Container... command.
• Select di cloned copy of dis folder, wait for di container to start, and try am out.

Offline access

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 serve for port 3000 for your localhost: localhost:3000.

Note, notebooks no go render via Docsify, so wen you need run notebook, do am separately for VS Code wey dey run Python kernel.

Other Curricula

Our team dey produce other curricula! Check am out:

LangChain

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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

You dey get wahala? Check our Troubleshooting Guide for how to solve common problems.

If you jam gbege or get any question about how to build AI apps. Join other learners and beta developers for talk about MCP. Na beta community wey questions dey welcome and knowledge dey share freely.

If you get product feedback or errors while you dey build, waka go:

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Disclaimer: Dis document na AI translation service Co-op Translator wey translate am. Even though we dey try make am correct, abeg sabi say automated translation fit get some errors or mistakes. The original document wey e dey for im own language na the correct one. If na serious matter, e better make professional human translation do am. We no go responsible for any wahala or wrong understanding wey fit happen because of this translation.