Learning Data Science Through Games and Applications
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. Important principles of data science will be elaborated through interactive games and real applications.
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 real applications such as sports analytics, a useful skill for academics, hobbies, and various professions.
Session 1A: June 14th - July 2nd
MWF 11:00am - 1:30pm EST
(no on-campus element)
Lectures will be co-led by Professors Jun Yan and his colleagues (Prof. Haim Bar and HaiYing Wang) and lab sessions will be led by graduate assistants. Students taking this class will get the chance to:
- program in their favorite language to run hide-and-seek simulations;
- understand most important principles in making inferences;
- learn the basics of data science computing skills - data manipulation, visualization, and analysis;
- practice on real applications with data from climate change or sports.
Meet the Professors
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, climate change, ecology, and sports. He has a special interest in making advanced statistical methods widely accessible via open source software. Dr. Yan is a fellow of the American Statistical Association and is the Editor of the Journal of Data Science.
More info is available at http://merlot.stat.uconn.edu/~jyan/.
Haim Bar is an Associate Professor in Statistics at the University of Connecticut. He received his Ph.D. in statistics from Cornell University in 2012. He received his M.Sc. in statistics in 2010 (Cornell University) and an M.Sc. in computer science in 2002 (Yale University). He received his bachelor degree in mathematics (Cum Laude) in 1993, at the Hebrew University in Jerusalem.
His professional interests include statistical modeling, shrinkage estimation, high throughput applications in biology (e.g., genomics), Bayesian statistics, variable selection, and machine learning.
From 1995 to 1997, he was with Motorola, Israel, as a computer programmer in the Wireless Access Systems Division. From 1997 until 2003 he worked for MicroPatent, LLC, where he held the position of Director of Software Development. In 2003 he moved to Ithaca, NY, and worked as a Principal Scientist at ATC-NY. Prior to coming to UConn, he worked at the Cornell Statistical Consulting Unit (CSCU) and the Department of Statistical Science at Cornell, as a consultant and lecturer.
HaiYing Wang is an Assistant Professor in the Department of Statistics at the University of Connecticut. He was an Assistant Professor in the Department of Mathematics and Statistics at the University of New Hampshire from 2013 to 2017. He obtained his Ph.D. from the Department of Statistics at the University of Missouri in 2013, and his M.S. from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences in 2006. His research interests include informative subdata selection for big data, model selection, model averaging, measurement error models, and semi-parametric regression.