Information Course Schedule Spring 2026

Three hours of lecture per week. Methods and concepts of creating design requirements and evaluating prototypes and existing systems. Emphasis on computer-based systems, including mobile system and ubiquitous computing, but may be suitable for students interested in other domains of design for end-users. Includes quantitative and qualitative methods as applied to design, usually for short-term term studies intended to provide guidance for designers. Students will receive no credit for 114 after taking 214.

This course is a survey of web technologies that are used to build back-end systems that enable rich web applications. Utilizing technologies such as Python, FastAPI, Docker, RDBMS/NoSQL databases, and Celery/Redis, this class aims to cover the foundational concepts that drive the web today. This class focuses on building APIs using microservices that power everything from content management systems to data engineering pipelines that provide insights by processing large amounts of data. The goal of this course is to provide an overview of the technical issues surrounding back-end systems today and to provide a solid and comprehensive perspective of the web’s constantly evolving landscape.

This course introduces students to natural language processing and exposes them to the variety of methods available for reasoning about text in computational systems. NLP is deeply interdisciplinary, drawing on both linguistics and computer science, and helps drive much contemporary work in text analysis (as used in computational social science, the digital humanities, and computational journalism). We will focus on major algorithms used in NLP for various applications (part-of-speech tagging, parsing, coreference resolution, machine translation) and on the linguistic phenomena those algorithms attempt to model. Students will implement algorithms and create linguistically annotated data on which those algorithms depend.

This course introduces students to the data sciences landscape, focusing on learning how to apply data science techniques to uncover, enrich, and answer the questions you will encounter and originate in the industry. After an introduction to data science and an overview of the course, students will explore decision-making in organizations and big data’s emerging role in guiding tactical and strategic decisions. 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 in ways that change minds and behaviors.

This course is designed to be an introduction to the topics and issues associated with information and information technology and its role in society. Throughout the semester we will consider both the consequence and impact of technologies on social groups and on social interaction and how society defines and shapes the technologies that are produced. Students will be exposed to a broad range of applied and practical problems, theoretical issues, as well as methods used in social scientific analysis. The four sections of the course are: 1) theories of technology in society, 2) information technology in workplaces 3) automation vs. humans, and 4) networked sociability.

This course uses examples from various commercial domains — retail, health, credit, entertainment, social media, and biosensing/quantified self — to explore legal and ethical issues including freedom of expression, privacy, research ethics, consumer protection, information and cybersecurity, and copyright. The class emphasizes how existing legal and policy frameworks constrain, inform, and enable the architecture, interfaces, data practices, and consumer facing policies and documentation of such offerings; and, fosters reflection on the ethical impact of information and communication technologies and the role of information professionals in legal and ethical work.

This course addresses concepts and methods of user experience research, from understanding and identifying needs, to evaluating concepts and designs, to assessing the usability of products and solutions. We emphasize methods of collecting and interpreting qualitative data about user activities, working both individually and in teams, and translating them into design decisions. Students gain hands-on practice with observation, interview, survey, focus groups, and expert review. Team activities and group work are required during class and for most assignments. Additional topics include research in enterprise, consulting, and startup organizations, lean/agile techniques, mobile research approaches, and strategies for communicating findings.

Three hours of lecture per week. Prerequisites: Graduate standing. As it's generally used, "information" is a collection of notions, rather than a single coherent concept. In this course, we'll examine conceptions of information based in information theory, philosophy, social science, economics, and history. Issues include: How compatible are these conceptions; can we talk about "information" in the abstract? What work do these various notions play in discussions of literacy, intellectual property, advertising, and the political process? And where does this leave "information studies" and "the information society"?

Succeeding in today’s distributed, technology-rich companies demands effectively leading and influencing others. This dynamic, interactive course is designed for ambitious professionals who are looking to have an impact, whether just starting out or already managing teams. Drawing heavily from social psychology and organizational behavior, this course goes beyond theory to develop real-world approaches. Students will engage with case studies and hands-on exercises to build the practical, research-backed skills needed to drive business impact with leadership and influence. A key focus is understanding workplace hierarchies and social dynamics, equipping students to thrive in complex, organizations — whether in tech or beyond.

This course introduces cultural analytics, an interdisciplinary field at the intersection of humanities, social sciences, and data science. Students explore how computational methods and digital archives are used to study culture across time and scale. Emphasis is placed on multiscale analysis — from close reading to distant reading — and on ethical, responsible engagement with large-scale cultural data. The course addresses challenges of data validity, the role of collaboration across disciplines, and the societal impact of cultural analytics, including its use in heritage preservation and disaster response. Students gain both technical tools and critical frameworks for studying culture in the digital age.

Discusses application of social psychological theory and research to information technologies and systems; we focus on sociological social psychology, which largely focuses on group processes, networks, and interpersonal relationships. Information technologies considered include software systems used on the internet such as social networks, email, and social games, as well as specific hardware technologies such as mobile devices, computers, wearables, and virtual/augmented reality devices. We examine human communication practices, through the lens of different social psychology theories, including: symbolic interaction, identity theories, social exchange theory, status construction theory, and social networks and social structure theory.

This course explores how visual representations reveal patterns and structure in complex data by leveraging human visual perception. Students critically examine the field of information visualization, analyzing why certain methods succeed or fail based on usability and adoption. Through hands-on projects and analysis, the course emphasizes how to present information clearly, accurately, and effectively to communicate insights and support data-driven decision-making.

Provides a theoretical and practical introduction to modern techniques in applied machine learning. Covers key concepts in supervised and unsupervised machine learning, including the design of machine learning experiments, algorithms for prediction and inference, optimization, and evaluation. Students will learn functional, procedural, and statistical programming techniques for working with real-world data.

This course is a survey of web technologies that are used to build back-end systems that enable rich web applications. Utilizing technologies such as Python, Flask, Docker, RDBMS/NoSQL databases, and Spark, this class aims to cover the foundational concepts that drive the web today. This class focuses on building APIs using micro-services that power everything from content management systems to data engineering pipelines that provide insights by processing large amounts of data. The goal of this course is to provide an overview of the technical issues surrounding back-end systems today, and to provide a solid and comprehensive perspective of the web’s constantly evolving landscape.

This course introduces students to natural language processing and exposes them to the variety of methods available for reasoning about text in computational systems. NLP is deeply interdisciplinary, drawing on both linguistics and computer science, and helps drive much contemporary work in text analysis (as used in computational social science, the digital humanities, and computational journalism). We will focus on major algorithms used in NLP for various applications (part-of-speech tagging, parsing, coreference resolution, machine translation) and on the linguistic phenomena those algorithms attempt to model. Students will implement algorithms and create linguistically annotated data on which those algorithms depend.

This course will cover new interface metaphors beyond desktops (e.g., for mobile devices, computationally enhanced environments, tangible user interfaces) but will also cover visual design basics (e.g., color, layout, typography, iconography) so that we have systematic and critical understanding of aesthetically engaging interfaces. Students will get a hands-on learning experience on these topics through course projects, design critiques, and discussion, in addition to lectures and readings. Two hours of lecture per week.

As new sources of digital data proliferate in developing economies, there is the exciting possibility that such data could be used to benefit the world’s poor. Through a careful reading of recent research and through hands-on analysis of large-scale datasets, this course introduces students to the opportunities and challenges for data-intensive approaches to international development. Students should be prepared to dissect, discuss, and replicate academic publications from several fields including development economics, machine learning, information science, and computational social science. Students will also conduct original statistical and computational analysis of real-world data.

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.

In this hands-on workshop, students will strengthen their skills in strategic thinking, product design and research, leadership, and cross-functional collaboration. Each week we’ll explore real-world challenges drawn from students’ ongoing projects, which may range from team and organizational dynamics to early start-up ideas or capstone projects. Through discussions, peer feedback, and faculty-led readings and research reviews, students will engage with the latest developments in the field, and gain practical hands-on experience as leaders.

This course provides a foundation in the principles, architectures, and applications of generative artificial intelligence (GenAI). It explores how large language models (LLMs) work by guiding students through the complete model lifecycle: from core building blocks like embedding representations, attention mechanisms, and transformer architectures to adaptation techniques including in-context learning, instruction tuning, parameter-efficient fine-tuning (PEFT), and reinforcement learning from human feedback (RLHF). 

The course also explores advanced topics such as retrieval-augmented generation (RAG), agentic reasoning systems, and multimodal learning that extends beyond text to incorporate vision and other data modalities. 

Programming assignments, research paper readings, and a team-based final project will help students develop the technical proficiency to design, implement, and evaluate GenAI pipelines using Hugging Face, LangChain, and other open-source LLM APIs, while critically assessing their capabilities, limitations, and responsible use in real-world applications.

This course will run as a design studio where students design new products as a way to explore the principles, processes, and outcomes of interaction design. Each week, students will complete both in-class and take-home projects that apply concepts and frameworks to real problem spaces. Class sessions will simulate professional design practice through weekly critiques, peer feedback, and design reviews. Students will be expected to present their work every week, engage actively in discussion, and refine their designs based on input.

Assignments will range from rapid, low-fidelity exercises to more developed interaction prototypes, giving students practice across the spectrum of design methods. The course culminates in a final project during the last weeks, where students bring together the skills, approaches, and insights developed throughout the semester. By the end of the course, students will have built a portfolio project and gained experience working within the collaborative, iterative culture of interaction design.

This course brings students together as a product team to apply data science and analytics skills to nonprofit and academic research projects. Students gain hands-on experience working with real-world data, using both foundational and advanced techniques — such as machine learning, data engineering, and online experiments — to generate actionable insights and solutions.

The Global Rights Innovation Lab Clinic (GRIL) offers students a unique opportunity to integrate digital technologies into legal advocacy. Our particular field of application is human rights, but this approach is broadly transferable to other areas of law and client-facing work in the fields of data science, public policy, and other social sciences.

GRIL students utilize data-driven and technological advancements for groundbreaking legal advocacy strategies. Serving organizational clients — grassroots organizations, national and international public interest and human rights groups, GRIL provides advocacy support and strategies to forge new pathways to address human rights challenges. GRIL clients want to harness data analysis, data science, and visualization to advance human rights investigations, litigation before national and international courts, or social justice policy advocacy.

The Global Rights Innovation Lab Clinic (GRIL) offers students a unique opportunity to integrate digital technologies into legal advocacy. Our particular field of application is human rights, but this approach is broadly transferable to other areas of law and client-facing work in the fields of data science, public policy, and other social sciences.

GRIL students utilize data-driven and technological advancements for groundbreaking legal advocacy strategies. Serving organizational clients — grassroots organizations, national and international public interest and human rights groups, GRIL provides advocacy support and strategies to forge new pathways to address human rights challenges. GRIL clients want to harness data analysis, data science, and visualization to advance human rights investigations, litigation before national and international courts, or social justice policy advocacy.

The Global Rights Innovation Lab Clinic (GRIL) offers students a unique opportunity to integrate digital technologies into legal advocacy. Our particular field of application is human rights, but this approach is broadly transferable to other areas of law and client-facing work in the fields of data science, public policy, and other social sciences.

GRIL students utilize data-driven and technological advancements for groundbreaking legal advocacy strategies. Serving organizational clients — grassroots organizations, national and international public interest and human rights groups, GRIL provides advocacy support and strategies to forge new pathways to address human rights challenges. GRIL clients want to harness data analysis, data science, and visualization to advance human rights investigations, litigation before national and international courts, or social justice policy advocacy.

This course examines how generative artificial intelligence (AI) is reshaping how products are designed, and how students can evolve their UX practice to thrive in this rapidly changing landscape. They will learn to critically evaluate AI tools to identify their value in UX workflows, articulate informed perspectives on emerging debates in UX for AI (e.g., how AI is redefining design and product roles), and make thoughtful decisions about when to delegate tasks to AI versus keeping them human-led.

Learning activities include hands-on use and structured evaluation of leading AI-enabled design tools (e.g., Figma Make, Lovable, V0), analysis and discussion of current academic and industry literature (e.g., how generative AI challenges the foundations of human-centered design), and conversations with industry experts building AI products. The course emphasizes cultivating an intentional exploration mindset — learning by researching, building, testing, and reflecting — to align emerging AI capabilities with human-centered design values.

An intensive weekly discussion of current and ongoing research by Ph.D. students with a research interest in issues of information (social, legal, technical, theoretical, etc.). Our goal is to focus on critiquing research problems, theories, and methodologies from multiple perspectives so that we can produce high-quality, publishable work in the interdisciplinary area of information research. Circulated material may include dissertation chapters, qualifying papers, article drafts, and/or new project ideas. We want to have critical and productive discussion, but above all else we want to make our work better: more interesting, more accessible, more rigorous, more theoretically grounded, and more like the stuff we enjoy reading.

Discussion, reading, preparation, and practical experience under faculty supervision in the teaching of specific topics within information management and systems. Does not count toward a degree.