Course Offerings

This is a survey course covering the basics of the informatics professions. We cover some history of informatics as an academic discipline and profession, review some of the significant concepts that students can expect to cover later in the Informatics major and minor, and review the different concentrations available to Informatics students. Assessment in this course is conducted through weekly quizzes and discussion questions; a short, persuasive, group presentation; and a longer-term persuasive essay project with both individual and group components.

This class offers students hands-on practice with Canvas and other digital tools in order to scaffold success in the informatics program. Students will conduct an independent, iterative research project including the following steps: crafting a research question, finding and evaluating sources, and presenting information.

Survey the ethical foundations for informatics, incorporating non-Western and feminist perspectives. Apply these ethical foundations to contemporary problems in informatics. Examine the confrontation of ethical dilemmas in the workplace, including recognizing value trade-offs, affected stakeholders, and potential solutions. Offered on the letter-grade basis only. This course carries the Writing Flag and the Ethics flag Ethics courses are designed to equip students with skills that are necessary for making ethical decisions in their adult and professional lives. Students should therefore expect a substantial portion of their grade to come from assignments involving ethical issues and the process of applying ethical reasoning to real-life situations.

Introduction to computer programming for those without any prior knowledge or experience in computer programming. We will introduce four broad areas related to success in computer programming: language, software engineering concepts, programming environment, and practical know-how.

In this age of information, we repeatedly hear the phrase “Do your research!”, but what does that mean? And more importantly, why does it matter? This course is broken into two parts designed to provide a foundation to begin answering these questions: 1. The first half of the course is an introduction to research, exploring what research is, what research can look like within the field of Informatics, and how Informatics research can be leveraged for social good. 2. The second half of the course dives deeper into “research methodology,” how research is done and some of the most common tools we use to explore the field’s most burning questions. Students will learn about the functions of qualitative and quantitative methods, as well as how the pieces of the research process fit together to explore challenges and potential solutions by applying human-centered values to the intersections of information, people, and technology. This course is held in-person and rather than exams, assignments are designed to encourage students to apply course concepts to their own interests. Students will leave this course empowered as citizens to critically evaluate research in terms of process, ethics, and equity.

We will describe data using visual and numerical descriptions (visualization and summary statistics). We will learn to make predictions and draw inferences using simple and multiple linear regression. We will learn classification using logistic regression. We will learn how to interpret diagnostic plots that accompany linear models. We will practice all these things using R and RStudio, which will be taught as part of the class. Some math and programming is not required but will be helpful in reducing the workload in the class. A statistics course is required but other statistics courses can be substituted for this one. This course counts for the Quantitative Reasoning flag, starting in Fall 2024.

This course is designed to help set students majoring in Informatics up for career success post-graduation. What does career success look like? Well, philosophically many things depending on context. This course, however, focuses on the transition between the last year of college and the first year of a career. The semester is broken into three units as a foundation to begin answering these questions: 1. The Landscape: What opportunities exist for graduates with my skillsets and interests? What do different job titles actually mean? How do I know which path is right for me? How do I find jobs and opportunities I’m interested in and qualified for? 2. The Application & Interview Process: After Unit 1, I know the kinds of positions and career paths I’m interested in post-graduation, but how I do actually get the job/position or accepted to my program of interest? In a sea of hundreds (sometimes thousands) of applicants, how do I show I’m a good fit in an application and an interview? Once I have options, how do I choose what’s right for me? 3. The First Year: How do I make sure my first year is successful? How do I navigate a new professional space? How do I set myself up to build relationships and perform well? How do I apply what I’ve learned in the Informatics Major and at UT to grow as an ethical, equitable leader and information professional? This course is held in-person and rather than exams, assignments are built as concrete materials students can use in their career searches and professional endeavors. Students will leave this course empowered to successfully navigate the Informatics-related job market and professional opportunities

As the culminating experience of the undergraduate Informatics Program, I 379C allows every student to apply their unique skillsets and learnings to a "degree-capping" project that is focused on a real-world problem or initiative. Informatics Capstone projects can take many forms, but typically involve aligning on a specific project and plan with an industry or faculty project sponsor, and then completing the project over the course of the semester. This course is designed to support your capstone journey throughout the semester as you work on your project with your Field Supervisor. Progress in the course is measured through weekly updates and documents submitted directly to Canvas. During the semester, the course meets once per week, and during these sessions we'll focus on items and issues relevant to your capstone experience. You'll have an opportunity to present your work also, through class presentations and the final poster session where your sponsors, faculty, and other students can meet you and discuss your project. Summary of Course Goals 1. Deliver a professional-level project/solution to showcase your knowledge, skills, and abilities. 2. Take direction and feedback from a supervisor working in your applied field of study. 3. Strengthen communication and presentation skills. 4. Manage expectations around project goals, schedule, and deliverables.

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.

Database is the foundation of Data Science. It provides the unique design to store, retrieve, and manage data. Data become the essential gas to power the generative AI. How to model data, encode context, enforce business rules, and achieve efficiency are critical for database design. This course provides the introductory understanding of relational database design with the focus on three parts. The first part is centered around the database design lifecycle by introducing business rules, ER diagram, normalization, and UML chart. The second part talks about database query language SQL by explaining concepts and providing examples. The third part gives you the forward introduction of XML database which is the commonly used NoSQL database. The learning content will be delivered in the variety of exercises including lectures, tutorials, class activities, individual assignments, group assignments, and group projects. This course empathizes peer learning, hands-on practices, forward exploring, and risk taking.

Infrastructure is all around us, even (or perhaps especially) where we do not actively consider or account for it. In this course, students will learn how knowledge infrastructures such as repositories, classification systems, databases, networks, standards, and/or metadata both shape and are shaped by governmental policy, institutional decision making, technical advances, and professional and personal value systems. We consider how infrastructure matters in professional, personal, and political life, and employ infrastructure as a lens to evaluate and understand the legal, ethical, and policy consequences of knowledge work, data science, and information management. In this course, students will employ an infrastructural perspective to evaluate programs, systems, policies, and/or organizations. We will explore the consequences and societal impact of knowledge work at both global and local scales, and consider how infrastructure might be built or refined to support societal or organizational goals such as social justice, privacy, innovation, health, or security. This is primarily a discussion-oriented course, with assessment primarily coming through a multi-stage, semester-long, project oriented around a program evaluation.

Ethnographic research has found application and acceptance across various academic disciplines as well as industries. This course aims to introduce fundamental tenets of ethnographic methodology for investigating sociotechnical systems. Its foundation rests on interdisciplinary perspectives and anthropological insights, while simultaneously aligning with contemporary advancements such as design and speculative ethnography. The role of the future has perpetually held a central position in the utilization and shaping of technologies and information systems. A recurring narrative involves positioning a specific technology or system as "revolutionary" or "the future of" a certain domain. Adopting an ethnographic approach, this course seeks to critically examine sociotechnical imaginaries. Its objective is to glean insights from diverse communities, offering guidance in the construction of futures that are more inclusive, equitable, and diverse.

Ethnographic research has found application and acceptance across various academic disciplines as well as industries. This course aims to introduce fundamental tenets of ethnographic methodology for investigating sociotechnical systems. Its foundation rests on interdisciplinary perspectives and anthropological insights, while simultaneously aligning with contemporary advancements such as design and speculative ethnography. The role of the future has perpetually held a central position in the utilization and shaping of technologies and information systems. A recurring narrative involves positioning a specific technology or system as "revolutionary" or "the future of" a certain domain. Adopting an ethnographic approach, this course seeks to critically examine sociotechnical imaginaries. Its objective is to glean insights from diverse communities, offering guidance in the construction of futures that are more inclusive, equitable, and diverse.

*THIS TOPIC WILL NO LONGER BE OFFERED AFTER SPRING 2025In 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.

Data storytelling is more than sharing data—at its most simple, it’s about designing charts and tables that make sense to the people who will be using them and help those people make better, faster decisions. While making a chart is as easy as a few clicks, doing it well requires much more. There is a science to how our eyes and minds process information as well as an art to making good graphic design choices. This comes together in an effective data presentation when the work is readable, usable, and above all actionable—not just aesthetically pleasing (though we’ll certainly address that too). As information professionals, we are well-positioned to understand and design for the needs of our users, to interrogate our data sources thoughtfully, and to ask future-thinking questions. This course will also draw on elements from cognitive psychology, user experience, data journalism, graphic design, business, and more. This multidisciplinary approach will take us on a grand tour that will touch on many aspects of data analysis and will serve as an excellent introduction to other data-oriented courses in the iSchool master’s program.

Data storytelling is more than sharing data—at its most simple, it’s about designing charts and tables that make sense to the people who will be using them and help those people make better, faster decisions. While making a chart is as easy as a few clicks, doing it well requires much more. There is a science to how our eyes and minds process information as well as an art to making good graphic design choices. This comes together in an effective data presentation when the work is readable, usable, and above all actionable—not just aesthetically pleasing (though we’ll certainly address that too). As information professionals, we are well-positioned to understand and design for the needs of our users, to interrogate our data sources thoughtfully, and to ask future-thinking questions. This course will also draw on elements from cognitive psychology, user experience, data journalism, graphic design, business, and more. This multidisciplinary approach will take us on a grand tour that will touch on many aspects of data analysis and will serve as an excellent introduction to other data-oriented courses in the iSchool master’s program.

Processes, techniques, and technologies that generate inscriptions (ready-to-take data), especially from or about people(s) or culture(s). Contexts, consequences, and history of datafication practices. Purposive intervention with datafication processes, practices, and artifacts.

Processes, techniques, and technologies that generate inscriptions (ready-to-take data), especially from or about people(s) or culture(s). Contexts, consequences, and history of datafication practices. Purposive intervention with datafication processes, practices, and artifacts.

Artificial intelligence (AI) is both a product of and a major influence on society. As AI plays an increasingly important role in society, it is critical to understand both the ethical factors that influence the design of AI and the ethical dimensions of the impacts of AI in society. The goal of this course is to prepare students for the important ethical responsibilities that come with developing systems that may have consequential, even life-and-death, consequences. Students first learn about both the history of ethics and the history of AI, to understand the basis for contemporary, global ethical perspectives (including non-Western and feminist perspectives) and the factors that have influenced the design, development, and deployment of AI-based systems. Students then explore the societal dimensions of the ethics and values of AI. Finally, students explore the technical dimensions of the ethics and values of AI, including design considerations such as fairness, accountability, transparency, power, and agency. Students who perform well in this class will be positioned to take on a leadership role within their organizations and will be able to help guide and steer the design, development, and deployment of AI-based systems in ways that benefit users, other stakeholders, their organizations, and society. The knowledge and skill gained through this course will benefit students throughout their careers, and society as a whole will benefit from ensuring that studenrs are prepared to consider the important ethical dimensions of their work.

This course examines the U.S. communication policy in light of domestic and international structural, economic and technological changes. We will investigate how notions of control, access and expression have changed during the 20th and the 21st centuries, examining communication policies and regulation against a backdrop of technological innovation. The definitions and controversies around what constitutes the public interest intersect with policies for specific media systems including broadcasting, cablecasting, the Internet and social media, among others. The cultural ramifications of communication systems in terms of their impacts on people and on speech are a related domain we will address. At the current moment, issues around privacy, large tech companies and their role in contemporary life, the limits and authority of regulation, and of course social media,AI and ‘big data’ dominate many political and research agendas. Our goal will be to understand the backgrounds and foundations that bring us to these concerns and to frame them in critical ways.