Course Offerings
| Number | Title | Instructor Description | Program | Skills & Topics |
|---|---|---|---|---|
| I 301 | Introduction to Informatics | 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. | Undergraduate |
|
| I 302 | Academic Success in the Digital University | 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. | Undergraduate |
|
| I 303 | Ethical Foundations for Informatics | Undergraduate | ||
| I 304 | Programming for Informatics | 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. | Undergraduate |
|
| I 305 | Research Methods for Informatics | 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. | Undergraduate |
|
| I 306 | Statistics for Informatics | 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. | Undergraduate |
|
| I 310 | Topics in Introductory Informatics | Undergraduate | ||
| I 310 | Topics in Introductory Informatics: Research Methods for Informatics | Undergraduate | ||
| I 310C | Introduction to Cultural Heritage Informatics | In this class, students will first learn some fundamentals of cultural heritage informatics and be introduced to the major kinds of institutions in this space: galleries, libraries, archives, and museums. Students will also see case studies of how fundamental concepts like access or metadata get used in contemporary examples. | Undergraduate |
|
| I 310D | Introduction to Human-Centered Data Science | Introduction to the theory and practice of data science through a human-centered lens, with emphasis on how design choices influence algorithmic results. Students will gain comfort and facility with fundamental principles of data science including (a) Programming for Data Science with Python (b) Data Engineering (c) Database Systems (d) Machine Learning and (e) Human centered aspects such as privacy, bias, fairness, transparency, accountability, reproducibility, interpretability, and societal implications. Each week’s class is divided into two segments: (a) Theory and Methods, a concise description of theoretical concept in data science, and (b) Tutorial, a hands-on session on applying the theory just discussed to a real-world task on publicly available data. We will use Python for programming and cover Python basics in the beginning of the course. For modules related to databases, we will use PostGre SQL. |
Undergraduate |
|
| I 310J | Introduction to Social Justice Informatics | Undergraduate | ||
| I 310M | Introduction to Health Informatics | Undergraduate | ||
| I 310S | Introduction to Social Informatics | An introduction to sociotechnical perspectives on information systems, their effects, and how we intervene to make them better. | Undergraduate |
|
| I 310U | Introduction to User Experience Design | This course introduces students to foundational knowledge, methods, and skills for designing human-centered user experience (UX) around interactive systems. Students will become familiar with user research, concept generation, design methods, and user evaluation. In addition, students will also learn how to collaborate in a team setting, communicate design rationales, and present compelling narratives about their work. The class will be structured with lectures, hands-on design activities such as design critiques, projects, and presentations. | Undergraduate |
|
| I 320 | Topics in Informatics: Applied Cybersecurity Foundations | The Texas Cybersecurity Clinic is a two-semester sequence that first equips students with the technical and business skills of an entry-level cybersecurity analyst (semester 1) and then partners them in (supervised) teams with a small local business, municipal government, nonprofit to render pro bono cybersecurity services (semester 2). During the first semester, students will learn key cybersecurity defense concepts and skills, such as vulnerability assessment, network configuration and security, access controls, authorization techniques, responding to a cyberattack, business planning, and penetration testing. Students will also learn how to form an effective cybersecurity operations team and communicate with organization and business leaders and employees about essential cybersecurity controls and functions. By the conclusion of this course, students will be prepared to work within their assigned teams to assess, design, and render a cybersecurity improvement project plan for their client organization next semester. | Undergraduate |
|
| I 320 | Topics in Informatics: Applied Cybersecurity Clinic Practicum | The Texas Cybersecurity Clinic is a two-semester sequence that first equips students with the technical and business skills of an entry-level cybersecurity analyst (semester 1) and then partners them in (supervised) teams with a Central Texas-based small business, municipal government, or nonprofit to render pro bono cybersecurity services (semester 2). During the first semester, students will learn key cybersecurity defense concepts and skills, such as vulnerability assessment, network configuration and security, access controls, authorization techniques, responding to a cyberattack, business planning, and penetration testing. Students will also learn how to form an effective cybersecurity operations team and communicate with organization leaders and employees about essential cybersecurity controls and functions. During the second semester, students work within their assigned teams to assess, design, and render a cybersecurity improvement project plan for their designated client organization, building cybersecurity capacity and bolstering the client organization’s ability to recover from a cyber incident long-term. | Undergraduate |
|
| I 320 | Topics in Informatics: Blockchain, Web3, and the Internet Computer | Undergraduate | ||
| I 320 | Topics in Informatics: Designing for Healthcare | Undergraduate | ||
| I 320 | Topics in Informatics: Evaluation of Interactive Systems | Undergraduate | ||
| I 320C | Topics in Cultural Heritage Informatics: Knowledge Equity and Digital Environments | This course we will explore the concepts and values of open knowledge and knowledge equity and how they intersect with the ongoing evolution of digital environments. Open knowledge can be described as information that is freely available to the public to use and redistribute. Knowledge equity extends beyond information access and use to also include what is valued as knowledge, whom that knowledge represents, and who creates it. | Undergraduate | |
| I 320C | Topics in Cultural Heritage Informatics | Undergraduate | ||
| I 320C | Topics in Cultural Heritage Informatics: Preservation of Difficult Histories | Undergraduate | ||
| I 320C | Topics in Cultural Heritage Informatics: Archives As Data | This course introduces digital archival collections that can be accessed and used as data for research and inquiry. Topics will focus on the transformation, analysis, and interpretation of digital cultural heritage in archival contexts, including digitization, web archiving, software emulation, and data archiving. From text messages, Spotify playlists, to the President's tweets--how are digital traces collected, preserved and managed by archives? What are the ethics of managing digital archives and making them accessible to researchers, the public, and machines? | Undergraduate |
|
| I 320D | Topics in Human-Centered Data Science | Undergraduate | ||
| I 320D | Topics in Human-Centered Data Science: Database Design | The class explores the principles of relational database design, and SQL as a query language in depth. | Undergraduate |
|
| I 320D | Topics in Human-Centered Data Science: Data Engineering | Principles and practices in Data Engineering. Emphasis on the data engineering lifecycle and how to build data pipelines to collect, transform, analyze and visualize data from operational systems. This is a hands-on and highly interactive course. Students will learn analytical data modeling techniques for organizing and querying data. They will learn how to transform data into dimensional models, how to build data products, and how to visualize the data. We will also examine the various roles data engineers can have in an organization and career paths for data professionals | Undergraduate |
|
| I 320D | Topics in Human-Centered Data Science: Applied Machine Learning with Python | This course will cover relevant fundamental concepts in machine learning (ML) and how they are used to solve real-world problems. Students will learn the theory behind a variety of machine learning tools and practice applying the tools to real-world data such as numerical data, textual data (natural language processing), and visual data (computer vision). Each class is divided into two segments: (a) Theory and Methods, a concise description of an ML concept, and (b) Lab Tutorial, a hands-on session on applying the theory just discussed to a real-world task on publicly available data. We will use Python for programming. By the end of the course, the goals for the students are to: 1. Develop a sense of where to apply machine learning and where not to, and which ML algorithm to use 2. Understand the process of garnering and preprocessing a variety of “big” real-world data, to be used to train ML systems 3. Characterize the process to train machine learning algorithms and evaluate their performance 4. Develop programming skills to code in Python and use modern ML and scientific computing libraries like SciPy and scikit-learn 5. Propose a novel product/research-focused idea (this will be an iterative process), design and execute experiments, and present the findings and demos to a suitable audience (in this case, the class). | Undergraduate |
|
| I 320D | Topics in Human-Centered Data Science: Open Source Software Development | Undergraduate | ||
| I 320D | Topics in Human-Centered Data Science: Data Visualization | This course offers students in Information Science a comprehensive exploration into the theories, techniques, and tools of data visualization. It is designed to equip students with the skills to effectively communicate complex information visually, enabling data analysis and decision-making. Through a combination of lectures, hands-on projects, and case studies, students will learn how to design and implement effective and aesthetically appealing data visualizations for a variety of data types and audiences. Upon successful completion of this course, students will be able to: • Understand the principles and psychology of visual perception and how they influence data visualization. • Critically evaluate the effectiveness of different data visualization techniques for varying data types and user needs. • Master the use of leading data visualization tools and libraries such as D3.js, or Tableau. • Develop interactive dashboards and reports that effectively communicate findings to both technical and non-technical audiences. • Apply design principles to create visually appealing, accurate, and accessible data visualizations. | Undergraduate |
|
| I 320D | Topics in Human-Centered Data Science: Explainable AI | Undergraduate | ||
| I 320D | Topics in Human-Centered Data Science: Text Mining and NLP Essentials | Undergraduate | ||
| I 320D | Topics in Human-Centered Data Science: Data Science for Biomedical Informatics | This course lays the foundation for data science education targeting health informatics students interested in learning more broadly about biomedical informatics. No previous coding experience is required. The students will be introduced to basic concepts and tools for data analysis. The focus is on hands-on practice and enjoyable learning. The course will use python as the programming language, and Jupyter Notebooks as the development environment (our “home base”) for the examples, tutorials, and assignments. We use Jupyterlab Notebooks because they are both the industry standard and a nice way to load, visualize, and analyze data and describe our findings in one environment. We will also learn GitHub to document changes and backup our work and, eventually, for use as a collaboration tool. Hands-on data analysis, final projects, and associated presentations will be mandatory for the completion of the course. The outcome for the class is that each student will have a GitHub repository with all of their work (Jupyter notebooks, data, etc.), including a final project that will be presented to the class. Specific topics to be covered include GitHub, Linux/Unix File system, Jupyter Notebooks, Python Programming, and Data Visualization. | Undergraduate | |
| I 320D | Topics in Human-Centered Data Science: Human-Centered Social Network Analysis | Undergraduate | ||
| I 320D | Topics in Human-Centered Data Science: Fine Tuning Open-Source Large Language Models | This course offers an introduction to Fine-Tuning Open-Source Large Language Models (LLMs) through project-based applications and real-world examples. The course will begin with a foundational understanding of Natural Language Processing (NLP), focusing on Text Preprocessing techniques such as Tokenization and Vectorization. A basic overview of Large Language Models will be provided, covering the fundamental structure and architecture of commonly used Open-Source Frameworks. The course will then focus on three key methods for fine-tuning LLMs: Self-Supervised, Supervised and Reinforcement Learning. Each method will be explored through both theoretical explanations and practical group-based projects, applying these concepts to real-world examples. Students will engage in hands-on projects to strengthen their understanding of how to customize and optimize LLMs for specific tasks or domains. | Undergraduate | |
| I 320J | Topics in Social Justice Informatics: Misinformation, Justice, and Design | 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. | Undergraduate |
|
| I 320J | Topics in Social Justice Informatics: Technologies and Information in the Global South | Undergraduate | ||
| I 320J | Topics in Social Justice Informatics: Understanding Disability and Accessibility | 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. | Undergraduate | |
| I 320J | Topics in Social Justice Informatics | Undergraduate | ||
| I 320J | Topics in Social Justice Informatics: Design For Social Impact | This class explores how to make arguments about and through design. The first half focuses on values, criticism, ethics, and analysis of technology, the latter portion aims to help a soon-to-graduate technologist envision positive social impact in a mission-driven enterprise. Students will practice synthesizing ethical tech considerations – as they will have to do for the rest of their careers – and combining this with an organizational mindset. Through exercises, role-playing, discussions, guest lectures from activist technologists, and wide-ranging readings, students will practice connecting broader implications of their designs with technical choices. Design for Social Impact seeks to arm students with diverse ways of reflecting on their authorial relationship to technology, drawing from art and design to political science and anthropology. Course participants will be encouraged to focus on areas of personal interest, enumerating the social, political, and economic parameters of particular technical systems: parameters that are as important as power consumption, usability, or efficiency. | Undergraduate |
|
| I 320J | Topics in Social Justice Informatics: Applied Social Justice Research | Undergraduate | ||
| I 320M | Topics in Health Informatics | Undergraduate | ||
| I 320M | Topics in Health Informatics: Public Health Informatics | Undergraduate | ||
| I 320M | Topics in Health Informatics: Machine Learning for Population Health Management | Undergraduate | ||
| I 320M | Topics in Health Informatics: Research Design and Analysis in Health Informatics | Undergraduate | ||
| I 320M | Topics in Health Informatics: Consumer Health Informatics | The course is designed for undergraduate students who are interested in understanding, analyzing, designing, evaluating, or developing technologies to serve the health needs of general consumers. It covers the concept of consumer health informatics, health behavior theories, health information seeking and information retrieval, various forms of consumer health systems, and the design and evaluation of such systems. | Undergraduate |
|
| I 320M | Topics in Health Informatics: Data Science for Biomedical Informatics | Undergraduate | ||
| I 320S | Topics in Social Informatics: Civic Engagement and Technology | Undergraduate | ||
| I 320S | Topics in Social Informatics: Online Communities | Undergraduate | ||
| I 320S | Topics in Social Informatics: Data and Society | Explore common data collection, management, and sharing practices around information technology and emerging technologies such as AI. Students will gain hands on experiences with collecting, analyzing, and managing user data in ethical and responsible manners. Students will design data-driven systems that are centered around user consent, transparency, and social responsibilities. | Undergraduate | |
| I 320S | Topics in Social Informatics: Technologies and Information in the Global South | Undergraduate |