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Course Description

Have you ever wondered how the seemingly two unrelated fields of math and sports intertwine? Or how simple data analyses can help lead underdogs to win the World Series or a National Championship? Sports analytics is a new and exciting field in the world of athletics.

Through collecting our own data and that of our favorite athletes and teams, we will learn how to make better-informed decisions to give an edge to athletic performance. This class will be tailored to those who are curious-minded, passionate, and love challenging themselves in fun ways.

This course will give you a behind the scenes look at how data helps drive some of the greatest success stories in sports. We will explore how the data process (collection, organization, analyzation, and visualization) can help athletes maximize their performance and teams win more games. Find out more as you dive into the statistics of your chosen sport. You will take your passion for sports and merge it with statistical analysis to have an unbiased view on performance on the field and court. Learn to use Python to analyze team performance in sports while discovering a variety of techniques. You will be empowered to explore your own ideas about sports team performances, test them out using the data, and become a producer of sports analytics.

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Enroll Now - Select a section to enroll in
Section Title
Sports Analytics - Session 1
Type
Classroom - In Person
Days
Su, M, T, W, Th, F
Time
9:00AM to 4:00PM
Dates
Jun 22, 2025 to Jul 04, 2025
Schedule and Location
Contact Hours
84.0
Delivery Options
Classroom - In Person  
Section Notes

This course is located on Duke University’s campus in Durham, NC. Participants should plan to arrive on day one, with courses beginning on day two. Both residential and commuter students are expected to attend orientation on day one, before courses begin. Time and location information about orientation, arrivals and departures, and other travel and program details, will be provided closer to the start date of the program.

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