Data Science and Sports Analytics

Sports, Statistics, and Science

Data science is a fast developing science of extracting meaningful information from massive data for better decision making. It is interdisciplinary by nature, involving statistics, computing, and domain knowledge. As an example, sports analytics use all available sports data to make better decisions across the whole spectrum of the sports industry business. Students taking this class will get the chance to:

  • Dig deeper into the intersections of reality and technology through engaging lab sessions
  • Learn the basics of data science computing skills - data manipulation, visualization, and analysis
  • Try their hand at baseball analytics
  • Immerse themselves in the work of a data scientist by web scrapping for sports data

After taking this class, students will have a basic understanding of the essential components of data science as well as the basic computing skills needed to explore this field further independently. They will also have hands on experience in sports analytics - a useful skill for academics, hobbies, and various professions. 


Jun Yan is a Professor of Statistics at the University of Connecticut. He received his Ph.D. in Statistics from University of Wisconsin - Madison in 2003. He was an Assistant Professor at the University of Iowa before joining UConn in 2007. His research interests include survival analysis, clustered data analysis, multivariate dependence, spatial extremes, and statistical computing. He is actively involved in applications and education of data science in public health, environmental sciences and engineering, and sports. He has a special interest in making advanced statistical methods widely accessible via open source software. More info is available at

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At a Glance:

Course Prerequisites:

  • Students interested in this class must have some prior knowledge of pre-calculus and statistics

Sessions Offered:

  • Session 4: July 19th - July 25th

Location: UConn Storrs Campus

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