Data-Science-For-Beginners/translations/pcm

Data Science for Beginners - Kurrikulum

Azure Cloud Advocates for Microsoft happy to offer one 10-week, 20-lesson kurrikulum wey dey all about Data Science. Every lesson get pre-lesson and post-lesson quizzes, written instructions to finish the lesson, solution, and one assignment. Our project-based way of teaching dey make you learn as you dey build — na proven way make new skills dem “stick”.

Plenty 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

🌐 Support for Many Languages

Supported via GitHub Action (Automatic & 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 wan make more translation, the supported languages dey listed here

Join Our Community

We get one Discord "Learn with AI" series wey dey go on — find out more and join us for Learn with AI Series from 18 - 30 September, 2025. You go collect tips and tricks on how to use GitHub Copilot for Data Science.

You be student?

Start with these resources:

• Student Hub page On this page, you go find beginner resources, Student packs and even ways to get free cert voucher. Dis na one page wey you go wan bookmark and check from time to time as we dey change content at least every month.
• Microsoft Learn Student Ambassadors Join one global community of student ambassadors — this fit be your way into Microsoft.

How to Start

📚 Documentation

• Installation Guide - Step by step instructions to set up your environment for beginners
• Usage Guide - Examples and common workflows
• Troubleshooting - Solutions to common problems
• Contributing Guide - How to contribute to this project
• For Teachers - Guidance for teachers and classroom resources

👨‍🎓 For Students

Complete Beginners: You new for data science? Start with our beginner-friendly examples! These simple, well-commented examples go help you sabi the basics before you dive enter the full curriculum. Students: If you wan use this curriculum by yourself, fork the whole repo and finish the exercises on your own, start with the pre-lecture quiz. Then read the lecture and complete the rest of the activities. Try to build the projects by understanding the lessons instead of just copying the solution code; that solution code still dey for the /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. Read the Usage Guide to learn how to work with the curriculum
3. Start with Lesson 1 and follow am one by one
4. Join our Discord community for support

👩‍🏫 For Teachers

Teachers: we don don put small suggestions for how to use this curriculum in [for-teachers.md]. We go happy to get your feedback in our discussion forum!

Meet di Team

Gif by Mohit Jaisal

🎥 Klik the image wey dey above for video about the project the people wey create am!

How we dey teach

We don choose two pedagogy tenets wen we dey build this curriculum: make am project-based and make e include frequent quizzes. By the end of this series, students go don learn basic principles of data science, including ethical concepts, how to prepare data, different ways to work with data, data visualization, data analysis, real-world use cases of data science, and more.

In addition, small low-stakes quiz before class dey set the student mind to learn topic, while second quiz after class dey help make dem remember more. This curriculum design make am flexible and fun and people fit take am finish or in part. The projects start small and dem dey become more complex by the end of the 10 week cycle.

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

Wetin each lesson get:

• Sketchnote wey no compulsory
• Extra video wey no compulsory
• Small warmup quiz before lesson
• Lesson wey dem write
• For project-based lessons, step-by-step guides wey show how to build the project
• Small knowledge checks
• One challenge
• Extra reading material
• Assignment
• Quiz wey dem go do after lesson

Small note about quizzes: All quizzes dey inside the Quiz-App folder, total 40 quizzes of three questions each. Dem dey linked from inside the lessons, but the quiz app fit run locally or dem fit deploy am to Azure; follow the instruction in the quiz-app folder. Dem dey localize dem small-small.

🎓 Examples for Beginners

You new for Data Science? We don create special examples directory wey get simple, well-commented code to help you get started:

• 🌟 Hello World - Na your first data science program
• 📂 Loading Data - Learn how to read and check datasets
• 📊 Simple Analysis - Do statistics calculations and find patterns
• 📈 Basic Visualization - Make charts and graphs
• 🔬 Real-World Project - Full workflow from start to finish

Every example get detailed comments wey explain each step, e make am perfect for total 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 dey classified and where e dey come from.	lesson	Jasmine
04	Introduction to Statistics & Probability	Introduction	Math techniques for probability and statistics to help understand data.	lesson video	Dmitry
05	Working with Relational Data	Working With Data	Intro 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	Intro to non-relational data, different 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. E good to get basic Python programming knowledge first.	lesson video	Dmitry
08	Data Preparation	Working With Data	Topics on techniques to clean and transform data to handle 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 to make your visualizations useful 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, make you check 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 na your first time to use a development container, make sure your system meet 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 fit 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 go be served on port 3000 on your localhost: localhost:3000.

Note, notebooks no go render via Docsify, so when you need to run a notebook, do that separately in VS Code wey dey run a Python kernel.

Other Curricula

Our team dey produce other curricula! Make you check:

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

How You Fit Take Get Help

You dey face wahala? Abeg check our Troubleshooting Guide make you find how to solve common wahala.

If you jam problem or you get any question about how to build AI apps, join other learners and experienced developers for discussion about MCP. Na supportive community wey dey welcome questions and where people dey share knowledge for free.

If you get product feedback or you see error while you dey build, visit:

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Abeg note: Dis document don translate wit AI translation service [Co-op Translator] (https://github.com/Azure/co-op-translator). Even though we dey try make am correct, abeg sabi say automated translation fit get mistakes or inaccuracies. The original document for im own language na the main, correct source wey you suppose trust. If na important information, better make person wey sabi human professional translator do am. We no go dey liable for any misunderstanding or wrong interpretation wey fit come from using this translation.