School of Information

Terms offered: Summer 2025 8 Week Session, Spring 2025, Fall 2024, Summer 2024 8 Week Session, Spring 2023, Fall 2022, Spring 2022, Fall 2021, Summer 2021 8 Week Session, Fall 2020
Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership.
Foundations of Data Science: Read More [+]

Terms offered: Spring 2025, Fall 2024, Fall 2022
Lectures and small group discussions focusing on topics of interest, varying from semester to semester.
Directed Group Study for Lower Division Undergraduates: Read More [+]

Terms offered: Fall 2022
This class introduces key issues, concepts, and methodologies of information studies. Students consider questions such as: what does it mean to live in an information society? What are the human and social aspects of the design of technology? How do policy, law, and other social forces affect this? How can technology and data be designed for social good? Students will become familiar with the kinds of research and multidisciplinary methods used in information studies. Students leave the course with tools to understand the politics, economics, and culture of information systems; a nuanced understanding of contemporary case studies involving technological systems in society; and a solid foundation for further study in information science.
Introduction to Information Studies: Read More [+]

Terms offered: Spring 2024, Spring 2023, Spring 2022
Surveying history through the lens of information and information through the lens of history, this course looks across time to consider what might distinguish ours as "the information age" and what that description implies about the role of "information technology" across time. We will select moments in societies' development of information production, circulation, consumption, and storage from the earliest writing and numbering systems to the world of Social Media. In every instance, we'll be concerned with what and when, but also with how and why. Throughout we will keep returning to questions about how information-technological developments affect society and vice versa?
History of Information: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
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.
User Experience Research: Read More [+]

Terms offered: Spring 2024, Spring 2022, Spring 2021
This course applies economic tools and principles, including game theory, industrial organization, information economics, and behavioral economics, to analyze business strategies and public policy issues surrounding information technologies and IT industries. Topics include: economics of information goods, services, and platforms; economics of information and asymmetric information; economics of artificial intelligence, cybersecurity, data privacy, and peer production; strategic pricing; strategic complements and substitutes; competition and antitrust; Internet industry structure and regulation; network cascades, network formation, and network structure.

Information Technology Economics, Strategy, and Policy: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
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/JS/React/Redux), as well as the underlying technologies 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.

Front-End Web Architecture: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
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.
Back-End Web Architecture: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
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.
Natural Language Processing: Read More [+]

Terms offered: Fall 2024, Fall 2022, Fall 2021
This course blends social and historical perspectives on data with ethics, law, policy, and case examples to help students understand 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 emphasize researcher and practitioner reflexivity, allowing students to explore their own social and ethical commitments.
Behind the Data: Humans and Values: Read More [+]

Terms offered: Spring 2024, Fall 2020, Fall 2019
A seminar focusing on topics of current interest. Topics will vary. A seminar paper will be required. Open to students from other departments.
Special Topics in Information: Read More [+]

Terms offered: Fall 2023, Spring 2023, Spring 2015

Directed Group Study for Advanced Undergraduates: Read More [+]

Terms offered: Fall 2023, Spring 2023, Spring 2016
Individual study of topics in information management and systems under faculty supervision.
Individual Study: Read More [+]

Terms offered: Spring 2025, Fall 2024, Spring 2024
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. Course must be taken for a letter grade to fulfill degree requirements.
Research Design and Applications for Data and Analysis: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
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.
Information Organization and Retrieval: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
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.
Social Issues of Information: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
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.
Information Law and Policy: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
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.

Introduction to Programming and Computation: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
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 106. It may also be taken as a stand-alone course by any student that has sufficient Python experience.
Introduction to Data Structures and Analytics: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
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.
Introduction to User Experience Design: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
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.
User Experience Research: Read More [+]

Terms offered: Fall 2024, Fall 2023
This course will give participants hands-on digital product design experience oriented around current industry practice. The course will be project-based with an emphasis on iteration, practice, and critique. During the course, participants will work on a series of design projects through a full design process, including developing appropriate design deliverables, gathering feedback, and iterating on designs.

Product Design Studio: Read More [+]

Terms offered: Spring 2024, Fall 2021, Fall 2020
This course is a graduate-level introduction to HCI research. Students will learn to conduct
original HCI research by reading and discussing research papers while collaborating on a
semester-long research project. Each week the class will focus on a theme of HCI research and
review foundational and cutting-edge research relevant to that theme. The class will focus on the
following areas of HCI research: ubiquitous computing, social computing, critical theory, and
human-AI interaction. In addition to these research topics the class will introduce common
qualitative and quantitative methodologies in HCI research.
Human-Computer Interaction (HCI) Research: Read More [+]

Terms offered: Spring 2024, Spring 2022, Spring 2020
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"?
Concepts of Information: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2021
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, this course is derived from 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.
Leadership and Management: Read More [+]

Terms offered: Fall 2024, Spring 2013, Spring 2011
This class is for graduate students interested in getting an advanced understanding of judgments and decisions made with predictive algorithms. The course will 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 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. Special attention is paid to psychological principles that impact the effectiveness and fairness of algorithms deployed at scale.
Decisions and Algorithms: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
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.
Social Psychology and Information Technology: Read More [+]

Terms offered: Spring 2024, Spring 2022, Spring 2021
This course applies economic tools and principles, including game theory, industrial organization, information economics, and behavioral economics, to analyze business strategies and public policy issues surrounding information technologies and IT industries. Topics include: economics of information goods, services, and platforms; economics of information and asymmetric information; economics of artificial intelligence, cybersecurity, data privacy, and peer production; strategic pricing; strategic complements and substitutes; competition and antitrust; Internet industry structure and regulation; network cascades, network formation, and network structure.

Information Technology Economics, Strategy, and Policy: Read More [+]

Terms offered: Fall 2021, Fall 2019, Fall 2018
The introduction of technology increasingly delegates responsibility to technical actors, often reducing traditional forms of transparency and challenging traditional methods for accountability. This course explores the interaction between technical design and values including: privacy, accessibility, fairness, and freedom of expression. We will draw on literature from design, science and technology studies, computer science, law, and ethics, as well as primary sources in policy, standards and source code. We will investigate approaches to identifying the value implications of technical designs and use methods and tools for intentionally building in values at the outset.
Technology and Delegation: Read More [+]

Terms offered: Fall 2024, Spring 2024, Fall 2022
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.
Experiments and Causal Inference: Read More [+]

Terms offered: Spring 2025, Spring 2023, Spring 2022
The design and presentation of digital information. Use of graphics, animation, sound, visualization software, and hypermedia in presenting information to the user. Methods of presenting complex information to enhance comprehension and analysis. Incorporation of visualization techniques into human-computer interfaces. Course must be completed for a letter grade to fulfill degree requirements.
Information Visualization and Presentation: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
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.
Applied Machine Learning: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
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/JS/React/Redux), as well as the underlying technologies 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.

Front-End Web Architecture: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
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.
Back-End Web Architecture: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
The course overviews a broad number of paradigms of privacy from a technical point of view. The course is designed to assist system engineers and information systems professionals in getting familiar with the subject of privacy engineering and train them in implementing those mechanisms. In addition, the course is designed to coach those professionals to critically think about the strengths and weaknesses of the different privacy paradigms. These skills are important for cybersecurity professionals and enable them to effectively incorporate privacy-awareness in the design phase of their products.
Privacy Engineering: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2021
This course examines the use of natural language processing as a set of methods for exploring and reasoning about text as data, focusing especially on the applied side of NLP — using existing NLP methods and libraries in Python in new and creative ways. 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.
Applied Natural Language Processing: Read More [+]

Terms offered: Spring 2025, Spring 2024, Fall 2022
This course will cover the principles and practices of managing data at scale, with a focus on use cases in data analysis and machine learning. We will cover the entire life cycle of data management and science, ranging from data preparation to exploration, visualization and analysis, to machine learning and collaboration, with a focus on ensuring reliable, scalable operationalization.
ensuring reliable, scalable operationalization.
Data Engineering: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
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.
Natural Language Processing: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
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.
Theory and Practice of Tangible User Interfaces: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
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 discussions, in addition to lectures and readings.
Interface Aesthetics: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
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, casual 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.
Quantitative Research Methods for Information Systems and Management: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
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.
Qualitative Research Methods for Information Systems and Management: Read More [+]

Terms offered: Spring 2024, Spring 2021, Spring 2019
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.
Big Data and Development: Read More [+]

Terms offered: Spring 2025, Fall 2024, Spring 2024
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.
Public Interest Cybersecurity: The Cybersecurity Clinic Practicum: Read More [+]

Terms offered: Spring 2025, Fall 2024, Spring 2024
Specific topics, hours, and credit may vary from section to section, year to year.
Special Topics in Information: Read More [+]

Terms offered: Fall 2024, Spring 2024, Fall 2023
Specific topics, hours, and credit may vary from section to section and year to year.
Special Topics in Management: Read More [+]

Terms offered: Fall 2024, Fall 2023, Spring 2023
Specific topics, hours, and credit may vary from section to section and year to year.
Special Topics in Social Science and Policy: Read More [+]

Terms offered: Spring 2025, Spring 2024, Fall 2023
Specific topics, hours, and credit may vary from section to section and year to year.
Special Topics in Technology: Read More [+]

Terms offered: Prior to 2007
Specific topics, hours, and credit may vary from section to section, year to year.
Special Topics in Information: Read More [+]

Terms offered: Fall 2016, Summer 2016 10 Week Session, Spring 2016
This course is designed to help School of Information graduate students maximize their internship, practicum, or independent research experiences.

Information Management Practicum: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
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.
Doctoral Research and Theory Workshop: Read More [+]

Terms offered: Fall 2024, Fall 2023, Spring 2023
Colloquia, discussion and readings designed to introduce students to the range of interests of the school.
Doctoral Colloquium: Read More [+]

Terms offered: Spring 2025, Fall 2024, Spring 2024
Topics in information management and systems and related fields. Specific topics vary from year to year.
Seminar: Read More [+]

Terms offered: Fall 2019, Spring 2016, Fall 2015
Group projects on special topics in information management and systems.
Directed Group Study: Read More [+]

Terms offered: Spring 2022, Spring 2016, Spring 2015
The final project is designed to integrate the skills and concepts learned during the Information School Master's program and helps prepare students to compete in the job market. It provides experience in formulating and carrying out a sustained, coherent, and significant course of work resulting in a tangible work product; in project management, in presenting work in both written and oral form; and, when appropriate, in working in a multidisciplinary team. Projects may take the form of research papers or professionally-oriented applied work.
Directed Group Work on Final Project: Read More [+]

Terms offered: Fall 2023, Summer 2016 8 Week Session, Spring 2016
Individual study of topics in information management and systems under faculty supervision.
Individual Study: Read More [+]

Terms offered: Spring 2025, Spring 2024, Fall 2021
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.
Teaching Assistance Practicum: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
This fast-paced course gives students fundamental Python knowledge necessary for advanced work in data science. Students gain frequent practice writing code, building to advanced skills focused on data science applications. We introduce a range of Python objects and control structures, then build on these with classes on object-oriented programming. A major programming project reinforces these concepts, giving students insight into how a large piece of software is built and experience managing a full-cycle development project. The last section covers two popular Python packages for data analysis, Numpy and Pandas, and includes an exploratory data analysis.
Introduction to Data Science Programming: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
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. Course must be taken for a letter grade to fulfill degree requirements.
Research Design and Applications for Data and Analysis: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
This course provides students with a foundational understanding of classical statistics within the broader context of data science. Topics include exploratory analysis and descriptive statistics, probability theory and the foundations of statistical modeling, estimators, hypothesis testing, and classical linear regression. Causal inference and reproducibility issues are treated briefly. Students will learn to apply the most common statistical procedures correctly, checking assumptions and responding appropriately when they appear violated; to evaluate the design of a study and how the variables being measured relate to research questions; and to analyze real-world data using the open-source language R.
Statistics for Data Science: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
Storing, managing, and processing datasets are foundational processes in data science. This course introduces the fundamental knowledge and skills of data engineering that are required to be effective as a data scientist. This course focuses on the basics of data pipelines, data pipeline flows and associated business use cases, and how organizations derive value from data and data engineering. As these fundamentals of data engineering are introduced, learners will interact with data and data processes at various stages in the pipeline, understand key data engineering tools and platforms, and use and connect critical technologies through which one can construct storage and processing architectures that underpin data science applications.
Fundamentals of Data Engineering: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
Machine learning is a rapidly growing field at the intersection of computer science and statistics concerned with finding patterns in data. It is responsible for tremendous advances in technology, from personalized product recommendations to speech recognition in cell phones. This course provides a broad introduction to the key ideas in machine learning. The emphasis will be on intuition and practical examples rather than theoretical results, though some experience with probability, statistics, and linear algebra will be important. Course must be taken for a letter grade to fulfill degree requirements.
Applied Machine Learning: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
Visualization enhances exploratory analysis as well as efficient communication of data results. This course focuses on the design of visual representations of data in order to discover patterns, answer questions, convey findings, drive decisions, and provide persuasive evidence. The goal is to give you the practical knowledge you need to create effective tools for both exploring and explaining your data. Exercises throughout the course provide a hands-on experience using relevant programming libraries and software tools to apply research and design concepts learned.
Data Visualization: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
The capstone course will cement skills learned throughout the MIDS program – both core data science skills and “soft skills” like problem-solving, communication, influencing, and management – preparing students for success in the field. The centerpiece is a semester-long group project in which teams of students propose and select project ideas, conduct and communicate their work, receive and provide feedback (in informal group discussions as well as formal class presentations), and deliver compelling presentations along with a Web-based final deliverable. Includes relevant readings, case discussions, and real-world examples and perspectives from panel discussions with leading data science experts and industry practitioners.
Capstone: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
This is a multidisciplinary graduate course that synthesizes data management, data economy, and machine learning & AI strategy and research, product innovation, business and enterprise technology strategy, industry analysis, organizational decision-making and data-driven leadership into one course offering. The course provides strategic thinking tools, analytical frameworks, and real-world case examples to help students explore and investigate modern data applications and opportunities in multiple domains and industries. Students are required to participate in weekly sessions and write response pieces as well as a final paper and presentation evaluating one defining application or emerging technology in machine learning/AI end-to-end.
Modern Data Applications: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
Intro to the legal, policy, and ethical implications of data, including privacy, surveillance, security, classification, discrimination, decisional-autonomy, and duties to warn or act. Examines legal, policy, and ethical issues throughout the full data-science life cycle — collection, storage, processing, analysis, and use — with case studies from criminal justice, national security, health, marketing, politics, education, employment, athletics, and development. Includes legal and policy constraints and considerations for specific domains and data-types, collection methods, and institutions; technical, legal, and market approaches to mitigating and managing concerns; and the strengths and benefits of competing and complementary approaches.
Behind the Data: Humans and Values: Read More [+]

Terms offered: Spring 2025, Fall 2024, Spring 2024
This course surveys privacy mechanisms applicable to systems engineering, with a particular focus on the inference threat arising due to advancements in artificial intelligence and machine learning. We will briefly discuss the history of privacy and compare two major examples of general legal frameworks for privacy from the United States and the European Union. We then survey three design frameworks of privacy that may be used to guide the design of privacy-aware information systems. Finally, we survey threat-specific technical privacy frameworks and discuss their applicability in different settings, including statistical privacy with randomized responses, anonymization techniques, semantic privacy models, and technical privacy mechanisms.
Privacy Engineering: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
This course introduces students to experimentation in the social sciences. 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 have facilitated the development of better data gathering. Key to this area of inquiry is
the insight that correlation does not necessarily imply causality. In this course, we learn how to
use experiments to establish causal effects and how to be appropriately skeptical of findings
from observational data.
Experiments and Causal Inference: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
This course provides learners hands-on data management and systems engineering experience using containers, cloud, and Kubernetes ecosystems based on current industry practice. The course will be project-based with an emphasis on how production systems are used at leading technology-focused companies and organizations. During the course, learners will build a body of knowledge around data management, architectural design, developing batch and streaming data pipelines, scheduling, and security around data including access management and auditability. We’ll also cover how these tools are changing the technology landscape.

Machine Learning Systems Engineering: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
This course teaches the underlying principles required to develop scalable machine learning pipelines for structured and unstructured data at the petabyte scale. Students will gain hands-on experience in Apache Hadoop and Apache Spark.
Machine Learning at Scale: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
Understanding language is fundamental to human interaction. Our brains have evolved language-specific circuitry that helps us learn it very quickly; however, this also means that we have great difficulty explaining how exactly meaning arises from sounds and symbols. This course is a broad introduction to linguistic phenomena and our attempts to analyze them with machine learning. We will cover a wide range of concepts with a focus on practical applications such as information extraction, machine translation, sentiment analysis, and summarization.
Natural Language Processing with Deep Learning: Read More [+]

Terms offered: Spring 2025, Fall 2024
This course focuses on the practical aspects of LLMs to enable students to be effective and responsible users of generative AI technologies. The course has three parts. Introduction section covers the historical aspects, key technical ideas and learnings all the way to Transformer architectures and various LLM training aspects. The Practical Aspects and Techniques section, students learn how to train, deploy, and use LLMs; and discuss core concepts like prompt tuning, quantization, and parameter efficient fine-tuning, and explore use case patterns. Finally, a discussion of challenges & opportunities offered by Generative AI, which includes highlighting critical issues like bias and inclusivity, fake information, safety, and some IP issue
Generative AI: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
A continuation of DATASCI 203, this course trains data science students to apply more advanced methods from regression analysis and time series models. Central topics include linear regression, causal inference, identification strategies, and a wide-range of time series models that are frequently used by industry professionals. Throughout the course, we emphasize choosing, applying, and implementing statistical techniques to capture key patterns and generate insight from data. Students who successfully complete this course will be able to distinguish between appropriate and inappropriate techniques given the problem under consideration, the data available, and the given timeframe.
Statistical Methods for Discrete Response, Time Series, and Panel Data: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
This course introduces the theoretical and practical aspects of computer vision, covering both classical and state of the art deep-learning based approaches. This course covers everything from the basics of the image formation process in digital cameras and biological systems, through a mathematical and practical treatment of basic image processing, space/frequency representations, classical computer vision techniques for making 3-D measurements from images, and modern deep-learning based techniques for image classification and recognition.

Computer Vision: Read More [+]

Terms offered: Summer 2024, Spring 2024, Fall 2023
Specific topics, may vary from section to section, year to year.
Special Topics: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
This course explores the most important elements beyond technology that shape the playing field on which cybersecurity problems emerge and are managed. The course emphasizes how ethical, legal, and economic frameworks enable and constrain security technologies and policies. It introduces some of the most important macro-elements (such as national security considerations and interests of nation-states) and micro-elements (such as behavioral economic insights into how people understand and interact with security features). Specific topics include policymaking, business models, legal frameworks, national security considerations, ethical issues, standards making, and the roles of users, government, and industry.
Beyond the Code: Cybersecurity in Context: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
This course focuses on both mathematical and practical foundations of cryptography. The course discusses asymmetric and symmetric cryptography, Kerchkoff’s Principle, chosen and known plaintext attacks, public key infrastructure, X.509, SSL/TLS (https), and authentication protocols. The course will include an in-depth discussion of many different cryptosystems including the RSA, Rabin, DES, AES, Elliptic Curve, and SHA family cryptosystems. This course also introduces advanced topics of applied cryptography, including a brief introduction to homomorphic encrypted computation and secure multi-party computation to protect sensitive data during arbitrary computation, cryptocurrency and its cryptographic building blocks, and quantum computing.
Cryptography for Cyber and Network Security: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
The course presents the challenges, principles, mechanisms and tools to make software secure. We will discuss the main causes of vulnerabilities and the means to avoid and defend against them. The focus is on secure programming practice, including specifics for various languages, but also covering system-level defenses (architectural approaches and run-time enforcement). We will also apply software analysis and vulnerability detection tools in different scenarios.
Software Security: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
This course is designed to provide students with the foundational math and programming skills required to be successful in the Master of Information and Cybersecurity (MICS) program. Upon completion of this course, students will be able to write programs in Python and will gain experience reading and interpreting C programs. Students will receive a comprehensive overview of algebraic principles and will explore quantitative concepts needed for cryptography. Additionally, this course will prepare students to apply logical thinking and decompose complex problems to create programmatic solutions.
Programming Fundamentals for Cybersecurity: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
Machine learning is a rapidly growing field at the intersection of computer science and statistics concerned with finding patterns in data. It is responsible for tremendous advances in technology, from personalized product recommendations to speech recognition in cell phones. This course provides a broad introduction to the key ideas in machine learning, with a focus on applications and concepts relevant to cybersecurity. The emphasis will be on intuition and practical examples rather than theoretical results, though some experience with probability, statistics, and linear algebra will be important.
Applied Machine Learning for Cybersecurity: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
Introduction to networking and security as applied to networks. Exercises cover network programming in a language of the student's choice, understanding and analyzing packet traces using tools like wireshark and mitmproxy, as well as applying security principles to analyze and determine network security. After this course, the student will have a fundamental understanding of networking, TLS and security as it applies to networked systems.
Network Security: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
This survey of operating system security compares approaches to security taken among several modern operating systems. The course will teach how to conceptualize design issues, principles, and good practices in securing systems in today’s increasingly diverse and complex computing ecosystem, which extends from things and personal devices to enterprises, with processing increasingly in the cloud. We will approach operating systems individually and then build on them so that students learn techniques for establishing trust across a set of interoperating systems.
Operating System Security: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
Security and privacy systems can be made more usable by designing them with the user in mind, from the ground up. In this course, you will learn many of the common pitfalls of designing usable privacy and security systems, techniques for designing more usable systems, and how to evaluate privacy and security systems for usability. Through this course, you will learn methods for designing software systems that are more secure because they minimize the potential for human error.
Usable Privacy and Security: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
This course offers valuable perspective for both the non-technical business manager and the technical cybersecurity or IT manager. It is the vital connector between the technical world of threats, vulnerabilities, and exploits, and the business world of board-level objectives, enterprise risk management, and organizational leadership. Now more than ever, managers have a need and responsibility to understand cyber risk. Just as financial risks and other operational risks have to be effectively managed within an organization, cyber risk has to be managed. It spans far beyond information technology, with broad implications in the areas of organizational behavior, financial risk modeling, legal issues, and executive leadership.
Managing Cyber Risk: Read More [+]

Terms offered: Spring 2025, Fall 2024, Spring 2024
This course surveys privacy mechanisms applicable to systems engineering, with a particular focus on the inference threat arising due to advancements in artificial intelligence and machine learning. We will briefly discuss the history of privacy and compare two major examples of general legal frameworks for privacy from the United States and the European Union. We then survey three design frameworks of privacy that may be used to guide the design of privacy-aware information systems. Finally, we survey threat-specific technical privacy frameworks and discuss their applicability in different settings, including statistical privacy with randomized responses, anonymization techniques, semantic privacy models, and technical privacy mechanisms.
Privacy Engineering: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
Cybersecurity is a primary national security and public policy concern. The government, military and private sector have various roles and responsibilities with regard to the protection of the cyber domain. In this course, students critically evaluate these roles and responsibilities, the manner in which government networks, systems, and data are secured, and the ability of national and international cybersecurity strategies and partnerships to mitigate the security risks introduced by society’s increased reliance on information.
New Domains of Competition: Cybersecurity and Public Policy: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
This course will focus on understanding key areas within Security Operations from a management perspective. Upon completion of this course, students will understand implementation and maintenance best practices for security operations services such as incident response, internal investigations, security analysis, threat intelligence and digital forensics. Students will not only get hands-on experience within each discipline but will also understand how to recruit and train others within a security operations center or security team.
Security Operations: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
Web applications play a vital role in every modern organization. If an organization does not properly test its web applications to identify security flaws, adversaries may be able to compromise these applications damaging functionality and accessing sensitive data. The focus of this course is on developing practical web application security testing skills required to assess a web application's security posture and convincingly demonstrate the business impact of discovered vulnerabilities, if exploited. The course includes both lectures and a variety of demonstrations and hands-on exercises in finding web application security vulnerabilities. During the course, students learn about assessment tools and methodologies.
Web Application Security Assessment: Read More [+]

Terms offered: Fall 2024, Spring 2024, Fall 2023
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.
Public Interest Cybersecurity: The Cybersecurity Clinic Practicum: Read More [+]

Terms offered: Fall 2022, Summer 2022, Fall 2021
Specific topics, may vary from section to section, year to year.
Special Topics: Read More [+]

Terms offered: Spring 2025, Fall 2024, Summer 2024
This capstone course will cement skills and knowledge learned throughout the Master of Information and Cybersecurity program: core cybersecurity technical skills, understanding of the societal factors that impact the cybersecurity domain and how cybersecurity issues impact humans, and professional skills such as problem-solving, communication, influencing, collaboration, and group management – to prepare students for success in the field. The centerpiece is a semester-long group project in which teams of students propose and select a complex cybersecurity issue and apply multi-faceted analysis and problem-solving to identify, assess, and manage risk and deliver impact.
Capstone: Read More [+]