Chad Shaw

In this project-based course, student teams will complete semester-long data science research or analysis projects selected from a variety of disciplines and industries. Students will also learn best practices in data science. Cross-list: DSCI 435, DSCI 535, COMP 549. Mutually Exclusive: Cannot register for COMP 449 if student has credit for COMP 549/COMP 559/DSCI 535. Repeatable for Credit.

In this project-based course, student teams will complete semester-long data science research or analysis projects selected from a variety of disciplines and industries. Students will also learn best practices in data science. Cross-list: DSCI 535. Mutually Exclusive: Cannot register for COMP 549 if student has credit for COMP 449/DSCI 435. Repeatable for Credit.

In this project-based course, student teams will complete semester-long data science research or analysis projects selected from a variety of disciplines and industries. Students will also learn best practices in data science. Cross-list: DSCI 535, COMP 449, COMP 549. Mutually Exclusive: Cannot register for DSCI 435 if student has credit for COMP 549/DSCI 535. Repeatable for Credit.

In this project-based course, student teams will complete semester-long data science research or analysis projects selected from a variety of disciplines and industries. Students will also learn best practices in data science. Cross-list: COMP 549. Mutually Exclusive: Cannot register for DSCI 535 if student has credit for COMP 449/DSCI 435. Repeatable for Credit.

This course provides 1-3 credit hours of credit for STAT majors who wish to pursue a research project of mutual interest to the student and a faculty member in a selected area of statistical specialization. The student will conduct independent research under the faculty member’s direction. Repeatable for Credit.

Correlation is not causation, but what exactly is causation? In this course we will explore the framework statistical science has formalized to approach causation. We will examine the potential outcomes concept, counterfactual reasoning and directed acyclic graph (DAG) models. The course will cover key theorems and results in the field as well as practical estimation and inferential techniques. The course will address instrumental variables as well as exploratory use of causal methods for model building and design of studies. We will survey applications through case studies in various disciplines. After taking this course students will be able to construct causal models, estimate causal effects and distinguish what data are relevant and irrelevant for causal analysis. Students will also be introduced to software techniques using R. Recommended Prerequisite(s): The course is open to all graduate students, but students should be aware that this is a graduate level course in statistics. Students should be performing graduate level research in their respective fields and/or have a background in statistical inference and research methods.

Research course for graduate level research in probability and statistics. This course provides 1-15 hours of credit for students who wish to pursue a statistical research project of mutual interest to the student and a faculty member. The student will conduct independent research under the faculty member’s direction. Repeatable for Credit. Repeatable for Credit.