Course Offerings

The class focuses on developing problem solving skills using Python as a programming language. Starting from procedural function development, we also explore object-oriented techniques, and discuss simple data structures that are often used in software development. The students usually do a few programming assignments, take a midterm, and submit a final project.

This class explores various data science models, both traditional and the state of the art techniques. The course is designed to provide mathematical and computational basis such as Linear Algebra, Optimization techniques, and probabilistic modeling for different types of machine learning models. The goal of the class is provide a foundational basis for data science techniques. The class focuses on PSETs and a final data science project.

Learning key data wrangling maneuvers in abstract and implementations in SQL, Excel, R Tidyverse, and Python Pandas. Maneuvers in data transformations include Nest, Pivot, Mutate (inc. separate/unite), Group/Summarize and Rectangling. Projects include working with "wild caught" data datasets (usually CSV or JSON) and computational notebook environments (e.g., iPython, Jupyter, Rmarkdown, Quarto). Fall 2024 has changes from previous syllabus now that we have Database Design and Introduction to Programming. Nonetheless, the previous syllabus is still useful as it links to course materials that show the teaching approach and type of assignments. http://howisonlab.github.io/datawrangling/#Schedule_of_classes

What does it really mean to be user-centered? How do we practice user-centered design in a professional and methodical manner? What research findings can we rely on to help us improve user experiences? This is a readings/discussion course that examines in depth what we know about people (that is, what does scientific research actually tell us) and how can we apply this knowledge in the real-world of experience design. We examine human psychology, from physical ergonomics to cultural dispositions, stopping off on cognition and social analyses en route, so as to have a holistic, robust perspective on what it means to understand users. The readings are complemented with an examination of methods e.g., what is a cognitive walkthrough and how do you do it reliably? what are the limitations of heuristic evaluations? The goal is to give you a solid grounding in the practices of user-centered thinking, regardless of your area of application, and prepare you for professional level contributions in the user-experience world. There is no teamwork, all students deliver individual term papers and design critique diaries. There are also no pre-requisites -- technical or theoretical, the class is open to all.

Explore common data collection, management, and sharing practices in information technology and emerging technologies, such as search engines and AI systems. Students will read papers and engage in discussions about the pros and cons of established data practices and learn about the three main components of responsible data management: 1) consent and ownership, 2) privacy and anonymity, and 3) broader impact. Students will also practice how to collect data, make data-driven decisions, and design data-driven products through group projects as UX designers, researchers, and data scientists. The course will bring in interdisciplinary perspectives with guest speakers from archive science, engineering, and respponsible AI, to provide a holistic view of broader data ecosystems and infrastructures.

In this course, we will work to understand and address the challenges of misinformation, disinformation, and strategic manipulation in online environments. First, we will work to develop a deep understanding of the problem space. We will read and discuss existing research (both historical and contemporary) on how and why misinformation and disinformation spread. Next, we will explore the process, both personal and interpersonal, by which these issues can be approached and addressed in our own lives. This will involve reflecting on our own presuppositions, beliefs, and biases about information; and doing a project in which we apply the principles of Human-Centered Design to investigate different design directions for addressing misleading information. Students will gain important contextual knowledge and hands-on design experience that they can take into future professional domains (from education to policy to technology), where they can contribute to building more trustworthy information systems.

Accessible UX provides students working (or planning to work) in any area of UX, Digital Product Management, or Development with key skills and insights into the current accessibility landscape, in addition to specific guidelines and WCAG conformance specifications. The course is divided into foundational and tactical modules. The first half of the course provides a comprehensive overview of Accessibility and its importance. The second half of the course involves evaluating real-world applications and websites per the WCAG guidelines, producing Accessibility reports, planning studies (with persons with disabilities), and designing for accessibility. Course Goals 1. Become proficient in recognizing accessibility issues in key domains 2. Understand successful team and organizational behaviors in Accessibility 3. Learn how Accessible UX and Development is accomplished 4. Evaluate Web and App experiences using the WCAG framework from W3.org/WAI

The purpose of this course is to provide theoretical and practical foundations for information professionals who wish to design and evaluate search systems and services, taking user-centered approaches. This course explores search user interfaces, search behavior, search interaction, search user experience, search as learning, search for creativity, and research methods for understanding information behavior and evaluating search systems. Students will learn search behavior across various contexts, including academic and professional settings, everyday life, and digital learning environments. Students will gain insights into how people interact with, use, and evaluate information in a variety of application areas, such as web search engines, domain-specific search systems, digital libraries, social search platforms, and generative AI-based systems.

Introduction to combining human and machine intelligence to benefit people and society. Explore cutting-edge research on a number of subjects related to human-AI interaction, including the psychological and societal impacts of AI as well as design guidelines and methods for human-centered AI.