Data Science for Beginners - A Curriculum

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

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

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

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.

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 this app, for 40 total quizzes of three questions each. They are linked from within the lessons, but the quiz app can be run locally; follow the instruction in the quiz-app folder. They are gradually being localized.

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.

Help Wanted!

If you would like to translate all or part of the curriculum, please follow our Translations guide

Other Curricula

Our team produces other curricula! Check out:

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