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Data Science for Beginners - A Curriculum

Azure Cloud Advocates at Microsoft are excited to present a 10-week, 20-lesson curriculum all about Data Science. Each lesson includes pre-lesson and post-lesson quizzes, step-by-step instructions, a solution, and an assignment. Our project-based approach helps you learn by doing, which is a proven way to make new skills stick.

A big thank you 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, 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)

If you want additional translations, the supported languages are listed here

Join Our Community

Are you a student?

Get started with these resources:

Student Hub page: This page offers beginner resources, student packs, and even ways to get a free certification voucher. Bookmark it and check back regularly, as the content is updated at least monthly.
Microsoft Learn Student Ambassadors: Join a global community of student ambassadors—this could be your gateway to Microsoft.

Getting Started

Teachers: We’ve included some suggestions on how to use this curriculum. We’d love your feedback in our discussion forum!

Students: To use this curriculum independently, fork the entire repository and complete the exercises on your own, starting with the pre-lesson quiz. Then, read the lesson and complete the activities. Try to build the projects by understanding the lessons rather than copying the solution code (though the code is available in the /solutions folders for each project-based lesson). Another idea is to form a study group with friends and go through the content together. For further learning, we recommend Microsoft Learn.

Meet the Team

Gif by Mohit Jaisal

🎥 Click the image above to watch a video about the project and the people who created it!

Pedagogy

We’ve designed this curriculum with two key teaching principles: it’s project-based and includes frequent quizzes. By the end of this series, students will have learned the basics of data science, including ethical considerations, data preparation, working with data, data visualization, data analysis, real-world applications of data science, and more.

Additionally, a low-stakes quiz before each lesson helps students focus on the topic, while a post-lesson quiz reinforces retention. This curriculum is designed to be flexible and enjoyable, and it can be completed in full or in part. The projects start small and gradually increase in complexity over the 10-week program.

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 stored 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.

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	Concepts, challenges, and frameworks related to data ethics.	lesson	Nitya
03	Defining Data	Introduction	How data is classified and its common sources.	lesson	Jasmine
04	Introduction to Statistics & Probability	Introduction	Mathematical techniques in 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 using 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 like Pandas. Foundational knowledge of Python programming is recommended.	lesson video	Dmitry
08	Data Preparation	Working With Data	Techniques for cleaning and transforming data to address challenges like 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 data sets and their variables.	lesson	Jen
13	Meaningful Visualizations	Data Visualization	Techniques and guidance for creating visualizations that effectively solve problems and provide insights.	lesson	Jen
14	Introduction to the Data Science lifecycle	Lifecycle	Overview of the data science lifecycle and its first step: acquiring and extracting data.	lesson	Jasmine
15	Analyzing	Lifecycle	Techniques for analyzing data during this phase of the data science lifecycle.	lesson	Jasmine
16	Communication	Lifecycle	Presenting insights from data in a way that decision-makers can easily understand.	lesson	Jalen
17	Data Science in the Cloud	Cloud Data	Introduction to data science in the cloud and its advantages.	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	Real-world projects driven by data science.	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 of 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 with the VS Code Remote - Containers extension:

If this is your first time using a development container, ensure your system meets the prerequisites (e.g., Docker installed) in the getting started documentation.

To use this repository, you can either open the repository in an isolated Docker volume:

Note: 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 method 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 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:

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

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