Tentative Fall 2025 Offerings

Undergraduate Courses

Intro to Project Management - J. Poulton (SI 311.021 - 3 credits)

The 14-week course will cover the core fundamentals of project management, including: the project life cycle; project management methodologies (waterfall, agile, scrum, etc.); key project planning and execution concepts (scope, schedule, budget, resources, risk, procurement, quality, communication, stakeholders, and change management); as well as the future of project management and the impact of artificial intelligence. The course will help students develop a project management artifact portfolio by asking them to complete different assignments throughout the semester and contribute to a group final project at the end of the course.

Sports Analytics - T. Finholt (SI 311.030)

In this course students will work with the instructor and with training/coaching personnel in U-M Athletics to address a set of analyses related to athlete health, safety or performance, such as by using data gathered from: tracking devices worn in practice and competition (e.g., Catapult); cameras (e.g., TrackMan); or boxscore and other statistical data (e.g, Pro Football Focus).  These datasets can be large and complex. For example, wearables data typically consist of a hundred records per second with a dozen or so variables per record (e.g., distance traveled, direction of movement, number of explosive movements) – collected longitudinally across up to fifteen athletes per team per season.

*Application Required*

Pre-requisites: Students should have completed (or be currently taking) an introductory level stats course (e.g., STATS 250) and an introductory programming course (e.g., EECS 183, ENGR 101/151 or SI 106).

Data Visualization - R. Serrano Vergel (SI 311.037 - 3 credits)

In an increasingly data-driven world, the ability to visualize data is critical. This course introduces the principles of data visualization, focusing on the Block Model. Through a series of hands-on exercises, students will be able to understand how to map visualization tasks on some useful abstraction, and then how to encode this abstraction by using the Grammar of Graphics, to create intuitive algorithms on Python to visualize data, using graphical representations.  Python is one of the essential languages required in data science. Many data visualization libraries in Python are built to perform numerous functions, contain tools, and have methods to manage and analyze data. However, we do not just learn how to use tools, but we will explore some of the best practices when you need to create effective data visualizations.

Causal Inference - H. Hoover (SI 311.062 - 3 credits)

This course will explore methods to determine cause-effect relationships from data. It covers techniques like matching, instrumental variables, regression discontinuity, and differences-in-differences equipping students to evaluate interventions, policies, and experimental treatments. Emphasis is placed on designing studies and analyzing observational data to draw credible causal conclusions.

Graduate Courses

Concept to Market: Foundations in Product Management - W. Thompson (SI 511.155 - 3 credits)

In this course, we will dive into the details of digital product management, focusing on planning exceptional customer experiences across web, mobile and mixed reality platforms. Through a blend of theoretical concepts and hands-on exercises, participants will uncover the transformative potential of product management in shaping the future of digital innovation.

Writing for User Experience: Content, Design, Strategy - R. Chung (SI 611.104 - 3 credits) 

This course is for students who want to learn how written content enhances user experience (writing as design, content strategy). Effective and professional communication with users, collaborators, and stakeholders will be emphasized, as part of learning how to develop creative written content from idea to implementation. Writing and revising are expected. 

Enforced Prerequisites: SI 582

Computational Social Science - M. Teplitsky (SI 611.127 - 3 credits)

This course introduces students to the growing field of computational social science. This field combines concepts and theories from the social sciences with computational methods. We will focus on posing social science questions using concepts like preferences, norms, emotions, heuristics, group dynamics, and so on. We will then learn to answer such questions using methods including regression, machine learning, natural language processing, simulations, experiments, and surveys. The course consists of lectures, discussions, and a weekly lab session. In the lab sessions students will use Python to analyze large datasets. 

Enforced Prerequisite: SI 506 or waiver