Automotive User Experience --- 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.

Mathematical Foundations for Applied Data Science --- This course builds and strengthens the mathematical foundations required to succeed in applied data science. The course will review fundamental concepts in statistics, probability, linear algebra, and calculus, and demonstrate how these concepts are applied to core approaches in data analysis.

Interdisciplinary Problem Solving --- "Interdisciplinary Problem Solving" is a course offered at the Law School through the Problem Solving Initiative (PSI). (https://problemsolving.law.umich.edu/) Through a team-based, experiential, and interdisciplinary learning model, small groups of U-M graduate and professional students work with faculty to explore and offer solutions to emerging, complex problems.

Networks --- This course will cover topics in network analysis, from social networks to applications in information networks such as the internet. We will introduce basic concepts in network theory, discuss metric and models, use software analysis tools to experiment with a wide variety of real-world network data, and study applications to areas such as information retrieval. For their final project, the students will apply the concepts learned in class to networks of interest to them.

Special Topics in Information --- Special Topic Offerings: Each section is a unique course. For offering details, see https://www.si.umich.edu/programs/courses/special-topics-courses.

Pervasive Interaction Design --- This course is advanced interaction design course the focuses on designing interactive applications for emerging mobile and context-aware technologies. It follows a similar format to 582 (Interaction Design) in that course is studio-based, consists largely of a seminar-long group project with multiple milestones, and is supplemented by readings and discussion relevant to the topic. Class meetings consist of brief lectures to introduce and frame course concepts, design methods, and technical tools; discussion of readings; in-class design exercises; and group work/lab time. Some programming will be required in order to complete prototyping activities, though students have flexibility in choosing the platforms and languages used. Programming will not be taught in the course.

Advanced Topics in Graphic Design and Communication --- This multidisciplinary, hybrid course is for those students who want/need to deepen and broaden their graphic communication skills developed in SI 520: Graphic Design. This course supports students' further professional development by enhancing their conceptual problem solving skills and technical proficiency through a set of projects.

Choice Architecture --- Designing a system or organization for humans requires understanding not just choice and behavior motivations (good in most cases, we hope), but also learning about the reasons we blunder and make mistakes. Thus, even when incentives are "aligned" with overall system goals, there are many instances where we make poor choices because as human beings, we are all susceptible to a wide array of routine biases that can lead to an equally wide array of unwanted and unintended outcomes and decisions. Our errors are what make us human, but up until now, they have been largely ignored by systems designers, whether these designers make complex public policy, manage a team or design an information system. But knowing how people think, we can become choice architects who design environments that both yield better decision making on the part of users, and achieve behavior that is consistent with overall system goals while gaining a competitive design-edge. The first goal of this course is to inform future information system professionals, designers and managers about human decision rules and their associated biases so that these insights can be incorporated into their design, business or management strategies. Knowledge of these issues can be a significant source of competitive advantage because they are unknown to most information systems professionals and they are not taught in most I-schools. The second goal of this course is to clarify how these results can be leveraged to create original and more effective systems and institutions that meet the designer's goals.

Data Manipulation and Analysis --- This course aims to help students get started with their own data harvesting, processing, aggregation, and analysis. Data analysis is crucial to evaluating and designing solutions and applications, as well as understanding user's information needs and use. In many cases the data we need to access is distributed online among many webpages, stored in a database, or available in a large text file. Often these data (e.g. web server logs) are too large to obtain and/or process manually. Instead, we need an automated way of gathering the data, parsing it, and summarizing it, before we can do more advanced analysis. Therefore, students will learn to use Python and its modules to accomplish these tasks in a 'quick and easy' yet useful and repeatable way. Next, students will learn techniques of exploratory data analysis, using scripting, text parsing, structured query language, regular expressions, graphing, and clustering methods to explore data. R modules will be used to accomplish these tasks. Students will be able to make sense of and see patterns in otherwise intractable quantities of data.

Needs Assessment and Usability Evaluation --- Covers the key concepts of evaluation and a variety of methods used to determine the goals of a system or service, performs organizational analysis, assesses task/technology or service fit, determines ease of learning of new or existing services or systems, determines ease of use, assesses aspects of performance (including information retrieval), and evaluates the success in accomplishing the user/organizational goals. Methods include observation, survey, interviews, performance analysis, evaluation in the design/iteration cycle, usability tests, and assessment of systems in use.