Information Course Schedule Fall 2024

This course is a survey of technologies that power the user interfaces of web applications on a variety of devices today, including desktop, mobile, and tablet devices. This course will delve into some of the core front-end languages and frameworks (HTML/CSS/JavaScript/React/Redux), as well as the underlying technologies that enable web applications (HTTP, URI, JSON). The goal of this course is to provide an overview of the technical issues surrounding user interfaces powered by the web today, and to provide a solid and comprehensive perspective of the web’s constantly evolving landscape.

This course provides an introduction to ethical and legal issues surrounding data and society, as well as hands-on experience with frameworks, processes, and tools for addressing them in practice. It blends social and historical perspectives on data with ethics, law, policy, and case examples — from Facebook’s “Emotional Contagion” experiment to controversies around search engine and social media algorithms, to self-driving cars — to help students develop a workable understanding of current ethical and legal issues in data science and machine learning. Legal, ethical, and policy-related concepts addressed include: research ethics; privacy and surveillance; bias and discrimination; and oversight and accountability. These issues will be addressed throughout the lifecycle of data — from collection to storage to analysis and application. The course emphasizes strategies, processes, and tools for attending to ethical and legal issues in data science work. Course assignments will emphasize researcher and practitioner reflexivity, allowing students to explore their own social and ethical commitments.

Introduces the data sciences landscape, with a particular focus on learning data science techniques to uncover and answer the questions students will encounter in industry. Lectures, readings, discussions, and assignments will teach how to apply disciplined, creative methods to ask better questions, gather data, interpret results, and convey findings to various audiences. The emphasis throughout is on making practical contributions to real decisions that organizations will and should make.

15 weeks; 3 hours of lecture per week. This course introduces the intellectual foundations of information organization and retrieval: conceptual modeling, semantic representation, vocabulary and metadata design, classification, and standardization, as well as information retrieval practices, technology, and applications, including computational processes for analyzing information in both textual and non-textual formats.

This course introduces the basics of computer programming that are essential for those interested in computer science, data science, and information management. Students will write their own interactive programs (in Python) to analyze data, process text, draw graphics, manipulate images, and simulate physical systems. Problem decomposition, program efficiency, and good programming style are emphasized throughout the course.

The ability to represent, manipulate, and analyze structured data sets is foundational to the modern practice of data science. This course introduces students to the fundamentals of data structures and data analysis (in Python). Best practices for writing code are emphasized throughout the course. This course forms the second half of a sequence that begins with INFO 206A. It may also be taken as a stand-alone course by any student that has sufficient Python experience.

This course will provide an introduction to the field of human-computer interaction (HCI). Students will learn to apply design thinking to user experience (UX) design, prototyping, & evaluation. The course will also cover special topic areas within HCI.

This course gives participants hands-on software product design experience based on current industry practice. The course is project-based with an emphasis on iteration, practice, and critique from experienced industry designers. During the course, participants work iteratively on a series of design projects (both solo and in groups) through a full design process, including developing appropriate design deliverables and gathering feedback. We’ll also cover specific topics, including design and prototyping tools, working with and developing design systems, typical phases and deliverables of the design process, and designing in different contexts (e.g., startups vs. larger companies). There will also be guest lectures from industry experts.

This course focuses on the practice of leadership, collaboration, and people management in contemporary, distributed, information and technology-rich organizations. Not just for potential people managers, we start with the premise that a foundation in leadership, management, and collaboration is essential for individuals in all roles, at any stage of their career. To build this foundation we will take a hybrid approach, engaging literature from disciplines such as social psychology, management, and organizational behavior, as well as leveraging case studies and practical exercises. The course will place a special emphasis on understanding and reacting to social dynamics in workplace hierarchies and teams.

This class is intended for graduate students interested in getting an advanced understanding of judgments and decisions made with predictive algorithms. We will first survey the vast literature on the psychology of how people arrive at judgments and make decisions with the help of statistical information, focused mostly on experimental lab evidence from cognitive and social psychology. Then we will study the burgeoning evidence on how people use statistical algorithms in practice, exploring field evidence from a range of settings from criminal justice and healthcare to housing and labor markets. We will pay special attention to psychological principles that impact the effectiveness and fairness of algorithms deployed at scale. The primary aim is to help students understand systematic human errors and explore potential algorithmic solutions.

This course introduces students to experimentation in data science. Particular attention is paid to the formation of causal questions, and the design and analysis of experiments to provide answers to these questions. This topic has increased considerably in importance since 1995, as researchers have learned to think creatively about how to generate data in more scientific ways, and developments in information technology has facilitated the development of better data gathering.

This course is a survey of technologies that power the user interfaces of web applications on a variety of devices today, including desktop, mobile, and tablet devices. This course will delve into some of the core front-end languages and frameworks (HTML/CSS/JavaScript/React/Redux), as well as the underlying technologies that enable web applications (HTTP, URI, JSON). The goal of this course is to provide an overview of the technical issues surrounding user interfaces powered by the web today, and to provide a solid and comprehensive perspective of the web’s constantly evolving landscape.

Three hours of lecture per week. Letter grade to fulfill degree requirements. Prerequisites: Proficient programming in Python (programs of at least 200 lines of code), proficient with basic statistics and probabilities. This course examines the state-of-the-art in applied Natural Language Processing (also known as content analysis and language engineering), with an emphasis on how well existing algorithms perform and how they can be used (or not) in applications. Topics include part-of-speech tagging, shallow parsing, text classification, information extraction, incorporation of lexicons and ontologies into text analysis, and question answering. Students will apply and extend existing software tools to text-processing problems.

Students will receive no credit for C262 after taking 290 section 4. Three hours of lecture and one hour of laboratory per week. This course explores the theory and practice of Tangible User Interfaces, a new approach to Human Computer Interaction that focuses on the physical interaction with computational media. The topics covered in the course include theoretical framework, design examples, enabling technologies, and evaluation of Tangible User Interfaces. Students will design and develop experimental Tangible User Interfaces using physical computing prototyping tools and write a final project report. Also listed as New Media C262.

Three hours of lecture per week. Introduction to many different types of quantitative research methods, with an emphasis on linking quantitative statistical techniques to real-world research methods. Introductory and intermediate topics include: defining research problems, theory testing, causal inference, probability and univariate statistics. Research design and methodology topics include: primary/secondary survey data analysis, experimental designs, and coding qualitative data for quantitative analysis. No prerequisites, though an introductory course in statistics is recommended.

Three hours of lecture per week. Theory and practice of naturalistic inquiry. Grounded theory. Ethnographic methods including interviews, focus groups, naturalistic observation. Case studies. Analysis of qualitative data. Issues of validity and generalizability in qualitative research.

This course provides students with real-world experience assisting politically vulnerable organizations and persons around the world to develop and implement sound cybersecurity practices. In the classroom, students study basic theories and practices of digital security, intricacies of protecting largely under-resourced organizations, and tools needed to manage risk in complex political, sociological, legal, and ethical contexts. In the clinic, students work in teams supervised by Clinic staff to provide direct cybersecurity assistance to civil society organizations. We emphasize pragmatic, workable solutions that take into account the unique needs of each partner organization.

This class will be aimed primarily towards master’s students but open to Ph.D. students as well. We will begin by evaluating the standard arguments for government intervention (e.g., addressing externalities, promoting competition, etc.), which typically assume that people are thinking carefully and optimizing their decisions towards some stable set of preferences. Then we will explore the evidence on three ways people deviate from those standard assumptions: non-standard beliefs, non-standard preferences, and non-standard decision-making processes. We will work to arrive at a set of psychological principles that improve our understanding of many long standing social problems (e.g., crime, addiction, prejudice) in addition to emerging issues (e.g., algorithms/AI, mental health, cultural differences). We will close the class by addressing the scope for different interventions to address these problems.

Karl Marx wrote, “the philosophers have hitherto only interpreted the world in various ways. The point, however, is to change it.” This discussion-based seminar examines varied political philosophies and the roles they suggest for the potentially transformational work of design, development, governance of built systems. We begin with an examination of liberal democratic capitalism and the imaginaries of technology, communication, and governance that align with it. We then explore design, deployment, and governance practices aligned with visions of economic democracy, direct democracy, socialism, Afrofuturism, Zapatismo, and Native American sovereignties. This is a discussion based seminar with the option of a research paper or project proposal as a final project.

How do you create a concise and compelling User Experience portfolio? Applying the principles of effective storytelling to make a complex project quickly comprehensible is key. Your portfolio case studies should articulate the initial problem, synopsize the design process, explain the key decisions that moved the project forward, and highlight why the solution was appropriate. This course will include talks by several UX hiring managers who will discuss what they look for in portfolios and common mistakes to avoid.

Students should come to the course with a completed project to use as the basis for their case study; they will finish with a completed case study and repeatable process. Although this class focuses on UX, students from related fields who are expected to share examples and outcomes of past projects during the interview process (data science, product management, etc.) are welcome to join.

Peoples and communities around the world will be confronting the challenges of climate change, ecosystem degradation, and biodiversity loss for many decades to come. This course will explore the different ways in which the informatics and computing field can contribute to our individual and collective efforts to mitigate and adapt to the effects of climate change.

Through readings and discussions, students will critically engage with foundational and leading-edge perspectives on diverse topics such as systems thinking for sustainable computing, sustainability in/through design, collapse informatics, fighting climate misinformation and climate anxiety, as well as how knowledge and tools from the fields of machine learning, human-computer interaction, web3, IoT, and remote sensing are being applied to novel solutions in many different settings.

Student-led projects will research the information needs and information seeking behaviors of individuals and communities, both now and into the future, and design information tools and resources to support them in their efforts of climate mitigation, adaptation, advocacy, and education.

This course is designed to give participants a practical overview of the modern lean/agile product management paradigm based on contemporary industry practice. We cover the complete lifecycle of product management, from discovering your customers and users through to sales, marketing and managing teams. We'll take an experimental approach throughout, showing how to minimize investment and output while maximizing the information we discover in order to support effective decision-making. During the course, we'll show how to apply the theory through hands-on collaborative problem-solving activities. There will also be guest lectures from industry experts.

This course explores transformations of the information ecosystem in recent decades. Starting with the origins of the internet, and a theoretical framing of the issues, the focus is on the interaction of technical architecture, public policy and law, corporate business models, and societal norms in reshaping the information environment. In addition to looking at foundational policy such as Section 230 of the Communications Decency Act, the course traces the growth of online misinformation and disinformation, algorithmic amplification, digital advertising, large scale data collection, and growing corporate scale and power — as well as the corresponding debates about content controls, encryption, privacy protection, information security, and antitrust regulation. While the primary case material is from the US (both state and national) the global nature of the internet means that the divergent policy trajectories of the European Union, China, and countries like India are a central theme throughout the course.

One hour colloquium per week. Must be taken on a satisfactory/unsatisfactory basis. Prerequisites: Ph.D. standing in the School of Information. Colloquia, discussion, and readings designed to introduce students to the range of interests of the school.

Topics in information management and systems and related fields. Specific topics vary from year to year. May be repeated for credit, with change of content. May be offered as a two semester sequence.

This course takes a critical approach to the study of games, game design, and gaming culture. Throughout history, humans have learned and expressed themselves through play. Thus, it is in our interest to better understand the socio-technical aspects of games and their role in our societies. The scope of this course includes principles and theories from social psychology, behavioral economics, information science, user interface design, as well as the art and science of game design. The course will explore questions such as: What makes a game? What are the mechanics and rules of compelling games? What is the social role of gaming in society? Why do people play? How are games used as part of storytelling? How do games build (or hinder) the development of community? How is game design different in virtual reality (VR) versus flat-screen environments? How do issues of gender, race and sexuality play out in gaming culture? What can games teach us about learning? What makes a successful, serious game? What makes a game immersive? Can games be considered art?