
Overview
Sports analytics has emerged as a field of research with increasing popularity propelled, in part, by the real-world success illustrated by the best-selling book and motion picture, Moneyball. Analysis of team and player performance data has continued to revolutionize the sports industry on the field, court, and ice as well as in living rooms among fantasy sports players and online sports gambling.
Drawing from real data sets in Major League Baseball (MLB), the National Basketball Association (NBA), the National Hockey League (NHL), the English Premier League (EPL-soccer), and the Indian Premier League (IPL-cricket), you’ll learn how to construct predictive models to anticipate team and player performance.
On this Sports Performance Analytics course offered by Coursera in partnership with University of Michigan, you’ll also replicate the success of Moneyball using real statistical models, use the Linear Probability Model (LPM) to anticipate categorical outcomes variables in sports contests, explore how teams collect and organize an athlete’s performance data with wearable technologies, and how to apply machine learning in a sports analytics context.
This introduction to the field of sports analytics is designed for sports managers, coaches, physical therapists, as well as sports fans who want to understand the science behind athlete performance and game prediction. New Python programmers and data analysts who are looking for a fun and practical way to apply their Python, statistics, or predictive modeling skills will enjoy exploring courses in this series.
Applied Learning Project
Learners will apply methods and techniques learned to sports datasets to generate their own results rather than relying on the data processing performed by others. As a consequence the learner will be empowered to explore their own ideas about sports team performance, test them out using the data, and so become a producer of sports analytics rather than a consumer.
What You Will Learn:
- Understand how to construct predictive models to anticipate team and player performance.
- Engage in a practical way to apply their Python, statistics, or predictive modeling skills.
- Understand the science behind athlete performance and game prediction.
Skill You Will Gain:
- Data Analysis
- Python Programming
- Sports Analytics
Get more details
Visit programme websiteProgramme Structure
Courses include:
- Foundations of Sports Analytics: Data, Representation, and Models in Sports
- Moneyball and Beyond
- Prediction Models with Sports Data
- Wearable Technologies and Sports Analytics
- Introduction to Machine Learning in Sports Analytics
Check out the full curriculum
Visit programme websiteKey information
Duration
- Part-time
- 4 months
- Flexible
Start dates & application deadlines
Language
Delivered
Disciplines
Sport and Exercise Science Data Analytics View 13 other Short Courses in Sport and Exercise Science in United StatesExplore more key information
Visit programme websiteWhat students do after studying
Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
Intermediate level
- Recommended experience: Learners should have some familiarity with Python before starting this course. We recommend the Python for Everybody Specialization.
Make sure you meet all requirements
Visit programme websiteTuition Fee
-
International
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 4 months. -
National
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 4 months.
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Funding
Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project.