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.

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.

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The class explores the principles of relational database design, and SQL as a query language in depth.

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

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).

Practical skills and understandings required to effectively work with open source software and understand the projects that build them. Includes git-based collaboration as well as conceptual understanding of licenses, security, technical and social processes in open source development. Class projects involve working with digital trace data from open source repositories.

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.

Introduction to the emerging field of Explainable Artificial Intelligence (XAI) from the perspectives of a developer and end-user. Students will gain hands-on experience with some of the most commonly used explainability techniques and algorithms.

Leveraging Text Mining, Natural Language Processing, and Computational Linguistics to address real-world textual data challenges, including document processing, keyword extraction, question answering, translation, summarization, sentiment analysis, search, recommendation, and information extraction. Each week, classes include (a) Theory and Methods for NLP concepts and (b) Lab Tutorials for practical application with Python on multilingual text datasets.

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.

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.

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.