Introduction to Sports Analytics

This is our page for the Introduction to Sports Analytics online course. You can check out beginner to advanced topics, which we regularly publish here.

This course provides a range of lectures that cover beginner to intermediate level topics on sports analytics. If you're a Data Analyst or Data Scientist who's looking to apply your skills to sports, then this is a great primer for you.

Course Modules

Lecture 1: Introduction to Sports Analytics

Learning Objectives

Understand the role and importance of analytics in modern sports.
Trace the history and key milestones in the development of sports analytics.

Lecture 2: Introduction to Data Types in Sports Analytics

Learning Objectives

Differentiate between types of data and how they’re used in analyses.
Understand basic methods for collecting and managing sports data.

Lecture 3: Introduction to Statistics and Sports Analytics

Learning Objectives

Understand and apply foundational statistical concepts commonly used in sports analysis.
Identify and calculate key sports metrics in hockey, soccer, and basketball.
Build basic statistical models to analyze real-world sports data.

Lecture 4: Introduction to Tools and Programming for Sports Analytics

Learning Objectives

Identify key tools and programming languages used in sports analytics, such as Excel, R, Python, SQL, and visualization software.
Perform basic data manipulation and analysis tasks using Excel and a programming language (Python or R).
Create simple visualizations to communicate sports data insights effectively.

Lecture 5: Overview of Data Visualization

Learning Objectives

Understand what makes sports data visualization unique and how to approach it differently than other domains.
Understand how to select the right tools and visualization types for different sports data contexts.
Create dashboards and advanced visualizations tailored to sports analytics.

Lecture 6: Overview of Data Storytelling for Sports

Learning Objectives

Understand the definition of data storytelling.
Be able to translate an analysis into a narrative.
Understand the different ways in which you can create a data story.

Lecture 7: Building Team, Player and Game Performance Reports

Learning Objectives

Understand the key concepts behind analyzing team, player, and game performance in sports.
Identify commonly used metrics for evaluating teams, players, and games.
Conduct a basic analysis of a team, player, or game using a sample dataset.

Subscribe to our newsletter to get the latest and greatest content on all things Data, AI and Hockey!