Course Offerings

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.

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.

No description provided.

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.

No description provided.

*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.

Develop fundamental graphic design theory and skills to prepare students for careers in Informatics and related fields.

This course addresses concepts and methods of user experience (UX) research, from identifying users’ problems and needs to evaluating concepts and designs for viability, usability, and satisfaction. It also covers aspects of managing the research process, including recruiting participants, setting up and conducting studies, analyzing qualitative and quantitative data, and disseminating insights. Students will work both individually and as part of a team to complete research exercises and projects. The course includes hands-on practice with several common UX research methods such as observation, interview, survey, focus groups, and expert review. We will also touch on applied topics such as research in enterprises, consulting, and startup organizations, lean/agile techniques, mobile research approaches, and strategies for persuasively communicating findings and product implications.

This course focuses on the unique design practice of (1) representing and organizing information to facilitate perception and understanding (information architecture) and (2) specifying the appropriate mechanisms for accessing and manipulating task and play information (interaction design). This course also explores design patterns appropriate for the HCI professional.

Examine social and psychological experiences of virtual environments and immersive technologies, such as in virtual reality and augmented reality. Through the course students will learn about the immersive technology and the research behind people’s experiences of virtual environments.

Digital Accessibility has become a critical topic for product leaders, developers, UX designers, and usability researchers. This course will explore the legal, ethical, and practical aspects of Accessibility as it relates to creating inclusive products and experiences for persons with disabilities. While Accessibility applies to both the physical and digital world, a large portion of the course will be focused on digital experiences, and those that combine technology with devices and tools.

This course introduces human aspects of AI systems for UX design students. It will provide an overview of AI's psychological and societal implications and the opportunities to design AI-integrated products by applying human-centered design principles.

Introduction to the methodologies and techniques required for designing an ideal user experience with physical objects. Students will use qualitative, quantitative, and anthropometric data to design and iterate projects.

The first half of the course describes interaction design while the second half covers information design. Each student will keep a sketchbook and turn in sketches corresponding to exercises. No sketching experience is required. Each student will participate in a group project developing a prototype of an information artifact such as a website, app, or kiosk. The prototype is usually completed using Figma, which will be taught as part of the course. During the information design part of the course, students will be introduced to Tableau and have the opportunity to create a data visualization in Tableau.

Online communities are important to our cultural, social, and economic lives and especially to how we find and share information. Yet they also threaten our well-being and may undermine critical social institutions as well as the integrity of public discourse. This course is an interdisciplinary inquiry that seeks to understand online communities. It covers the history of online communities from their origins in the pre-Internet to the rise of social media platforms and contemporary challenges and also the social, psychological, and human-computer interaction research that both explains the practical barriers to building an online community and motivates technical and organizational designs that aim to overcome them.