MS in Data Science Information Sessions & Events | Master of Science in Data Science

Description:

Having one general résumé probably won't get your application seen by human eyes. Join Kay Kimball Gruder from UConn’s Center for Career Readiness and Life Skills as she leads you through this workshop specifically designed for Data Science students. Learn how to optimize your résumé for AI-driven applicant tracking systems that determine how well you match a job description and whether you move forward in the talent pool. Bring a digital copy of your current résumé and be ready to make edits.

Speaker Biography:

Kay Kimball Gruder is the Associate Director for Graduate Student & Postdoc Career Programs and Services at The University of Connecticut. In her role, she leads and manages career service and programming operations that center on the career development of graduate students (master’s and doctoral), international students (undergraduate and graduate), and postdoc fellows. Kay enjoys advocating for career needs in and beyond academic departments, supporting career diversity exploration, and contributing toward greater transparency in career outcomes. She frequently consults with faculty and departments that seek to integrate career milestones within degree programs and curricula, addressing barriers to career success. She also enjoys one-on-one career coaching appointments, providing an attentive space for individuals to pause, reflect, wonder, discover, design, and move forward toward career options and opportunities.

Short Course Description: This is a special two-part sports analytics workshop series. Part one is planned for Fall 2026 and part two is planned for Spring 2027.

Sports Analytics 1: Fundamentals
This course provides an introduction to the tools, data, and statistical concepts used in modern sports analytics. Participants will learn how to access and work with publicly available sports data, including play-by-play, player statistics, and player-tracking data from sources such as nflscrapR and baseballr, as well as data from the NFL Big Data Bowl. The course emphasizes hands-on analysis and visualization using R packages including ggplot2, nflplotR, sportyR, teamcolors, and gganimate. Participants will then use these data to explore fundamental concepts in sports analytics, including expected points and win probability. By the end of the course, participants will have the skills to obtain, visualize, and begin analyzing real-world sports data while developing an understanding of how statistical models can be used to quantify performance and decision-making in sports.

Speaker Biography: Gregory J. Matthews, Ph.D., is a Professor of Statistics in the Department of Mathematics and Statistics at Loyola University Chicago and Director of the Center for Data Science and Consulting. His research focuses on sports analytics, missing data methods, statistical shape analysis, and statistical privacy. He earned his Ph.D. in Statistics from the University of Connecticut in 2011.

Matthews received the 2025 Significant Contributor Award from the American Statistical Association’s Section on Statistics in Sports. He was a finalist in the 2023 NFL Big Data Bowl with Quang Nguyen and has been a four-time finalist and two-time winner (2020 and 2021) of the Carnegie Mellon Sports Analytics Conference Reproducible Research Competition. He also recently helped establish the Journal of Statistics and Data Science in Sports, a diamond open-access journal.

He lives in Chicago with his wife and three children.

MS in Data Science -
Information Sessions & Events

DATA SCIENCE WEBINAR SERIES

Event Name: Data Science Résumé Writing Workshop

DATA SCIENCE CAREER SERIES

Event Name: Sports Analytics I: Fundamentals

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