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Special topics courses

Fall 2022 Offerings

SI 311.030 Sports Analytics with Professor T. Finholt

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.

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 INFO 106).

SI 311.050 SQL & Databases with Professor M. Hess

This course will introduce the students to beginning and intermediate database concepts to prepare the student to use databases as part of a data analysis workflow. The students will learn data modeling, SQL Syntax, understanding how to evaluate different database systems for suitability, how to evaluate and improve the performance of database operations, how to use a database in a multi-step analysis process.

SI 311.149 Privacy & Surveillance with Professor S. Berman

Ubiquitous technology, pervasive data collection, machine learning and artificial intelligence have led to an unprecedented ability for individuals and organizations to watch, observe,  and surveil. This course will examine the intersections of observation, surveillance and privacy from a variety of interdisciplinary perspectives, and, informed by the past and present, critically explore potential future scenarios and outcomes.

SI 311.150  Automotive User Interfaces with Professor J. Rampton

Taught by industry professionals who currently work in the automotive industry, this course provides hands-on experience with the best practices and theory that exist within Automotive User Experience (UX) design space. Students will be asked to create designs of their own to convey their understanding. The primary context for this class will be the in-vehicle digital interfaces, but other devices such as mobile, web, and physical devices (ex. Charging stations, key fobs) will be referenced. This class will not cover exterior automotive design.

Winter 2022 Offerings 

SI 311-001: Social Movements & The Internet with Professor J. Sheng

Over the past few decades, social movements have increasingly relied on social movement participants leveraging internet technologies in mobilization, coordination, and public outreach to assist in their movement goals. How have new online tools such as social media and digital connectivity changed the processes of contemporary social movements? This course examines the ways social movements have adapted to online technologies to critically think about how the internet has altered traditional forms of social movement mobilization. We start with an introduction and review of traditional social movement literatures, building up to recent scholarship that examines how the internet has changed social movements. The last few weeks are focused on different contemporary social movement case studies where the internet played an important role, including the Arab Spring, Black Lives Matter, LGBTQ equality, feminism and the #MeToo movement, recent international social movements, and the internet’s role in the Jan. 6 storming of the U.S. Capitol. Students will be encouraged to think about the ways social movement processes have been accelerated and/or changed due to online technologies. 

SI 311-003: Sports Analytics with Professor T. Finholt

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.

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 INFO 106).