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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 focused on Data Science. Each lesson includes pre-lesson and post-lesson quizzes, step-by-step instructions, solutions, and assignments. This project-based approach helps you learn by doing, ensuring the skills you gain are long-lasting.

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 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'd like additional translations, supported languages are listed here

Join Our Community

Are you a student?

Start with these resources:

Student Hub page This page offers beginner resources, student packs, and even opportunities to get a free certification voucher. Bookmark it and check back regularly as content is updated 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. Share your feedback in our discussion forum!

Students: To use this curriculum independently, fork the repository and complete the exercises, 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 solutions are available in the /solutions folders for each project-based lesson). Another option 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 team behind it!

Pedagogy

This curriculum is built on two key principles: project-based learning and frequent quizzes. By the end of the series, students will understand fundamental concepts of data science, including ethical considerations, data preparation, working with data, data visualization, data analysis, real-world applications, and more.

Additionally, a low-pressure quiz before each class helps students focus on the topic, while a post-class quiz reinforces retention. The curriculum is designed to be flexible and enjoyable, allowing students to complete it in full or in part. Projects start small and gradually become more complex over the 10-week cycle. Find our Code of Conduct, Contributing, Translation guidelines. We appreciate 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 also 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	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:

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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