Paul Sabin

Paul Sabin
  • Lecturer in Statistics and Data Science & Sr. Sports Analytics Fellow, Wharton AI & Analytics Initiative

Contact Information

  • office Address:

    Academic Research Building
    265 South 37th Street
    Philadelphia, PA 19104

Research Interests: Sports Analytics, Bayesian Statistics

Links: Personal Website

Teaching

Past Courses

  • STAT4010 - Sports Analytics

    This course would introduce undergraduate students to the growing field of sports analytics, while allowing them to implement and integrate their knowledge base by exploring real sports data sets to solve real problems. While the context will be sports related, the skills and techniques gained will be widely applicable and generalizable with applications in diverse areas. Prerequisites: Must be a declared Statistics Concentrator or Business Analytics Concentrator or Statistics Minor or Data Science Minor. Permission from the Instructor is required. An interest in sports is highly recommended.

  • STAT4420 - Intro Bayes Data Analys

    The course will introduce data analysis from the Bayesian perspective to undergraduate students. We will cover important concepts in Bayesian probability modeling as well as estimation using both optimization and simulation-based strategies. Key topics covered in the course include hierarchical models, mixture models, hidden Markov models and Markov Chain Monte Carlo. A course in probability (STAT 4300 or equivalent); a course in statistical inference (STAT 1020, STAT 1120, STAT 4310 or equivalent); and experience with the statistical software R (at the level of STAT 4050 or STAT 4700) are recommended.

  • STAT5440 - Applied Bayesian Modeling

    This is a 5000-level graduate course that focuses on the application of statistical techniques from a Bayesian perspective. It is designed for high-level senior undergraduate students who have completed course 4420, as well as for graduate students from various non-statistics fields interested in applying Bayesian methods to their research. The curriculum begins with a refresher on Bayesian statistical principles, followed by practical applications using established software like Stan for model sampling. Critical subjects included in the course are Bayesian model selection, Stan programming, BRMS, variational Bayes methods, regression and mixed effects models, hierarchical structures, dynamic linear models, survival analysis, Gaussian processes, and explorations in nonparametric Bayesian approaches.

  • STAT6130 - Regr Analysis For Bus

    This course provides the fundamental methods of statistical analysis, the art and science if extracting information from data. The course will begin with a focus on the basic elements of exploratory data analysis, probability theory and statistical inference. With this as a foundation, it will proceed to explore the use of the key statistical methodology known as regression analysis for solving business problems, such as the prediction of future sales and the response of the market to price changes. The use of regression diagnostics and various graphical displays supplement the basic numerical summaries and provides insight into the validity of the models. Specific important topics covered include least squares estimation, residuals and outliers, tests and confidence intervals, correlation and autocorrelation, collinearity, and randomization. The presentation relies upon computer software for most of the needed calculations, and the resulting style focuses on construction of models, interpretation of results, and critical evaluation of assumptions.

  • STAT7010 - Modern Data Mining

    Modern Data Mining: Statistics or Data Science has been evolving rapidly to keep up with the modern world. While classical multiple regression and logistic regression technique continue to be the major tools we go beyond to include methods built on top of linear models such as LASSO and Ridge regression. Contemporary methods such as KNN (K nearest neighbor), Random Forest, Support Vector Machines, Principal Component Analyses (PCA), the bootstrap and others are also covered. Text mining especially through PCA is another topic of the course. While learning all the techniques, we keep in mind that our goal is to tackle real problems. Not only do we go through a large collection of interesting, challenging real-life data sets but we also learn how to use the free, powerful software "R" in connection with each of the methods exposed in the class. Prerequisite: two courses at the statistics 4000 or 5000 level or permission from instructor.

  • STAT9270 - Bayesian Statistics

    This graduate course will cover the modeling and computation required to perform advanced data analysis from the Bayesian perspective. We will cover fundamental topics in Bayesian probability modeling and implementation, including recent advances in both optimization and simulation-based estimation strategies. Key topics covered in the course include hierarchical and mixture models, Markov Chain Monte Carlo, hidden Markov and dynamic linear models, tree models, Gaussian processes and nonparametric Bayesian strategies.

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