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Special topics courses

Tentative Fall 2025 Offerings

Undergraduate Courses

Coding Without Coding - P. Resnick (SI 211.028 - 3 credits)

In the past, coding for everyday life required learning a programming language, such as python. Not anymore. This course teaches students to use an AI assistant to translate their instructions into code. Potential applications include simulating physical and social processes, generating summaries of open-ended survey questions, or creating games. Intended for students who have not previously completed a college-level programming course.

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.

The Life of Data: Stewardship & Ethics - R. Frank (SI 311.039 - 3 credits)

Students will critically examine the entire data lifecycle, from data production and organization to curation and preservation. Key topics include digital objects, metadata, data repositories, open science, and the labor and responsibilities of data stewards and curators. By the end of the course, students will be equipped to evaluate the ethical implications of data use and implement strategies to maintain and enhance data for diverse stakeholder needs, ensuring sustainable and ethical data practices.

Methods in Information Design and Data Visualization - T. Seiple (SI 311.071 - 3 credits)

Information Design is the practice of displaying and communicating information. Good information design is imperceivable to most people. However, poorly communicated information has caused nuclear accidents, aviation disasters and misled consumers. This course will explore the history, theory, and technical best practices of information design and teach you how to communicate data, statistics, and complicated concepts clearly through visuals.

From The Beginning of Time to AI: A History of Information - K. LaPlant (SI 411.172 - 4 credits)

This course will examine multiple means of conveying, controlling, and disseminating information, looking through the development of information technologies throughout human history. Students will learn about other ways of spreading, creating, storing, and sharing information through the study of global historical developments. We will also examine the past through modern concepts in Information Studies to ask the question: do human actors determine technological innovation, use, and adoption or does technology determine the changes in human life? Projects revolve around using older methods of information technology to think about new information problems.

Graduate Courses

Automation and Optimization for Data Workflows and Gen AI Reports - E. Anusiem (SI 511.018 - 3 credits) 

The course teaches data workflow, data governance and generative artificial intelligence (Gen AI) reporting theories to provide students with more generalized knowledge about how to properly develop and enhance different types of workflows to automate Gen AI reports that abide by appropriate data governance standards.

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.

Libraries, Archives, and Knowledge Environments in Society - R. Frank (SI 511.503 - 3 credits)

Comparative overview of the historical and social contexts of collections and collecting, including the development of libraries, archives, and other knowledge environments; organizational values and cultures; legal, policy and ethical frameworks; programs, services, and functions; impacts on users, organizations, and communities; and contemporary issues facing information professionals in these domains.

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