Data Science for Beginners - A Curriculum

Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-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:

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 complete the exercises on your own, starting with a pre-lecture quiz, then reading the lecture completing 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 go through the content together. For further study, we recommend Microsoft Learn by watching the videos mentioned below.

Pedagogy

We have chosen two pedagogical tenets while building this curriculum: ensuring that it is project-based that it includes frequent quizzes. By the end of this series, students will have …

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 fun can be taken in whole or in part. The projects start small become increasingly complex by the end of the 12 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 48 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

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.
02	Data Science Ethics	Introduction	Data Ethics Concepts, Challenges & Frameworks.		Nitya Narasimhan
03	Defining Data	Introduction	How data is classified, and its common sources.
04	Introduction to Statistics & Probability	Introduction	The mathematical techniques of probability and statistics to understand data.
05	Working with Spreadsheets	Working With Data	Using spreadsheets as a source of data for exploration and analysis.
06	Working with Relational Databases	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”)
07	Working with NoSQL Data	Working With Data	Introduction to non-relational data, its various types and the basics of exploring and analyzing document databases.
08	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.
09	Data Preparation	Working With Data	Topics on data wrangling, which are techniques for cleaning and transforming the data to handle challenges of missing, inaccurate, or incomplete data.
10	Visualizing Quantities	Data Visualization	Learn how to use Matplotlib to visualize bird data 🦆	Quantities	Jen
11	Visualizing Distributions of Data	Data Visualization	Visualizing observations and trends within an interval.		Jen
12	Visualizing Proportions	Data Visualization	Visualizing discrete and grouped percentages.		Jen
13	Visualizing Relationships	Data Visualization	Visualizing connections and correlations between sets of data and their variables.		Jen
14	Meaningful Visualizations	Data Visualization	Techniques and guidance for making your visualizations valuable for effective problem solving and insights.		Jen
15	Capturing	Lifecycle	Introduction to the data science lifecycle and its first step of acquiring and extracting data
16	Processing	Lifecycle	This phase of the data science lifecycle focuses on techniques to classify and summarize data.
17	Analyzing	Lifecycle	This phase of the data science lifecycle focuses on exploring and discovering the data and applying techniques such as statistical analysis, predictive analysis and regression.
18	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.
19	Maintaining	Lifecycle	This phase of the data science lifecycle focuses on preparing the data into a consistent format to be analyzed.
20	Data Science in the Cloud	Cloud Data	This series of lessons introduces data science in the cloud and its benefits.
21	Data Science in the Cloud	Cloud Data	Training models with Azure Machine Learning Studio
22	Data Science in the Cloud	Cloud Data	Deploying models with Azure Machine Learning Studio
23	Data Science in the Wild	In the Wild	Data science driven projects in science and sociology
24	Data Science in the Wild	In the Wild	Data science driven projects in science and sociology

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.

PDF

A PDF of all of the lessons can be found here