Terms offered: Fall 2021, Summer 2021 8 Week Session, Spring 2021, 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: Summer 2020 10 Week Session, Summer 2019 10 Week Session, Summer 2018 10 Week Session
A fast-paced introduction to the Python programming language geared toward students of data science. The course introduces a range of Python objects and control structures, then builds on these with classes on object-oriented programming. The last section of the course is devoted to Python’s system of packages for data analysis. Students will gain experience in different styles of programming, including scripting, object-oriented design, test-driven design, and functional programming. Aside from Python, the course also covers use of the command line, coding and presentation with Jupyter notebooks, and source control with Git and GitHub.
Python Fundamentals for Data Science: Read More [+]

Terms offered: Spring 2017, Fall 2016, Spring 2016
This course provides an introduction to critical and ethical issues surrounding data and society. It blends social and historical perspectives on data with ethics, policy, and case examples to help students develop a workable understanding of current ethical issues in data science. Ethical and policy-related concepts addressed include: research ethics; privacy and surveillance; data and discrimination; and the “black box” of algorithms. Importantly, these issues will be addressed throughout the lifecycle of data--from collection to storage to analysis and application. Course assignments will emphasize researcher and practitioner reflexivity, allowing students to explore their own social and ethical commitments.
Data and Ethics: Read More [+]

Terms offered: Fall 2021, Fall 2020, Fall 2019
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: Spring 2021, Spring 2020, Spring 2019
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: Summer 2021 10 Week Session, Spring 2021, Spring 2020
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 2021, Spring 2019
This course covers the application of 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 (IT) and IT industries. Topics include: economics of information; economics of information goods, services, and platforms; strategic pricing; strategic complements and substitutes; competition models; network industry structure and telecommunications regulation; search and the "long tail"; network cascades and social epidemics; network formation and network structure; peer production and crowdsourcing; interdependent security and privacy.
Information Technology Economics, Strategy, and Policy: Read More [+]

Terms offered: Spring 2021, Spring 2020, Fall 2018
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 2021, Fall 2020, Fall 2019
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: Fall 2020, Fall 2019, Fall 2018
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: Spring 2015, Fall 2014, Spring 2014

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

Terms offered: Spring 2016, Fall 2015, Spring 2015
Individual study of topics in information management and systems under faculty supervision.
Individual Study: Read More [+]

Terms offered: Fall 2021, Spring 2021, Fall 2020
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 2021, Fall 2020, Fall 2019
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 2021, Spring 2020, Spring 2019
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 2021, Spring 2020, Spring 2019
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 2021, Fall 2020, Fall 2019
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 2021, Fall 2020, Fall 2019
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 2021, Fall 2020, Fall 2019
User interface design and human-computer interaction. Examination of alternative design. Tools and methods for design and development. Human computer interaction. Methods for measuring and evaluating interface quality.
User Interface Design and Development: Read More [+]

Terms offered: Spring 2021, Spring 2020, Spring 2019
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 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 2020, Spring 2018, Spring 2016
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 2021, Fall 2020, Fall 2018
This course focuses on managing people in information-intensive firms and industries, such as information technology industries. Topics include managing knowledge workers; managing teams (including virtual ones); collaborating across disparate units, giving and receiving feedback; managing the innovation process (including in eco-systems); managing through networks; and managing when using communication tools (e.g., tele-presence). The course relies heavily on cases as a pedagogical form.
Managing in Information-Intensive Companies: Read More [+]

Terms offered: Spring 2021, Fall 2019, Fall 2018
"Behavioral Economics" is one important perspective on how information impacts human behavior. The goal of this class is to deploy a few important theories about the relationship between information and behavior, into practical settings — emphasizing the design of experiments that can now be incorporated into many 'applications' in day-to-day life. Truly 'smart systems' will have built into them precise, testable propositions about how human behavior can be modified by what the systems tell us and do for us. So let's design these experiments into our systems from the ground up! This class develops a theoretically informed, practical point of view on how to do that more effectively and with greater impact.

Applied Behavioral Economics for Information Systems: Read More [+]

Terms offered: Spring 2021, Spring 2020, Spring 2019
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 2021, Spring 2019, Spring 2018
Application of 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; economics of information goods, services, and platforms; strategic pricing; strategic complements and substitutes; competition models; network industry structure and telecommunications regulation; search and the "long tail"; network cascades and social epidemics; network formation and network structure; peer production and crowdsourcing; interdependent security and privacy.
Information Technology Economics, Strategy, and Policy: Read More [+]

Terms offered: Spring 2019
Information privacy law profoundly shapes how internet-enabled services work. This course translates regulatory demands flowing from the growing field of privacy and security law to those who are creating interesting and transformative internet-enabled services. We focus both on formal requirements of the law and on how technology might accommodate regulatory demands and goals. Topics include: Computer Fraud and Abuse Act, unfair/deceptive trade practices, Electronic Communications Privacy Act, children’s privacy, big data and discrimination, Digital Millennium Copyright Act, intermediary liability issues, ediscovery and data retention, anti-marketing laws, and technical requirements of the European Union-United States Privacy Shield.
Privacy Law for Technologists: 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: Not yet offered
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 2021, Spring 2020, Spring 2019
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 2021, Spring 2020, Fall 2018
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 2021, Fall 2020
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 2021, Spring 2020
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: Fall 2021, Spring 2019, Fall 2016
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.
Applied Natural Language Processing: Read More [+]

Terms offered: Spring 2020, Spring 2019, Spring 2018
Introduction to relational, hierarchical, network, and object-oriented database management systems. Database design concepts, query languages for database applications (such as SQL), concurrency control, recovery techniques, database security. Issues in the management of databases. Use of report writers, application generators, high-level interface generators.
Database Management: Read More [+]

Terms offered: Spring 2021, Spring 2020, Fall 2018
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: Spring 2021, Spring 2020, Fall 2018, Fall 2017
This course covers computational approaches to the task of modeling learning and improving outcomes in Intelligent Tutoring Systems (ITS) and Massive Open Online Courses (MOOCs). We will cover theories and methodologies underpinning current approaches to knowledge discovery and data mining in education and survey the latest developments in the broad field of human learning research. The course is project based; teams will be introduced to online learning platforms and their datasets with the objective of pairing data analysis with theory or implementation. Literature review will add context and grounding to projects.
Machine Learning in Education: Read More [+]

Terms offered: Fall 2021, Fall 2019, Fall 2018
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: Fall 2020, Spring 2015, Spring 2014
How does the design of new educational technology change the way people learn and think? How do we design systems that reflect our understanding of how we learn? This course explores issues on designing and evaluating technologies that support creativity and learning. The class will cover theories of creativity and learning, implications for design, as well as a survey of new educational technologies such as works in computer supported collaborative learning, digital manipulatives, and immersive learning environments.

Technologies for Creativity and Learning: Read More [+]

Terms offered: Spring 2021, Spring 2020, Spring 2019
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 2021, Fall 2020, Fall 2019
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 2021, Fall 2020, Fall 2019
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 2020
Civil society and governments across the world continue to push social media platforms for increased responsiveness to concerns about the abuse of technology. Recent nationally representative surveys reveal a widening trust deficit between the public and private technology companies. This has led to a growing job market for technology policy professionals that can help companies navigate complex issues related to online hate and harassment, and for engineers who understand user-needs for vulnerable communities. This course will provide an opportunity for UC Berkeley graduate students to engage in lectures and guided design exercises aimed at improving the affordances of social media platforms with regard to civil and respectful discourse.
Designing Against Hate: An Exploration of Speech and Affordances on Social Media: Read More [+]

Terms offered: Spring 2021, Spring 2019, Spring 2017
This seminar reviews current literature and debates regarding Information and Communication Technologies and Development (ICTD). This is an interdisciplinary and practice-oriented field that draws on insights from economics, sociology, engineering, computer science, management, public health, etc.
Information and Communications Technology for Development: Read More [+]

Terms offered: Spring 2021, Spring 2015, Fall 2012
New Venture Discovery introduces students to the process of launching an information-intensive venture--a social enterprise, business startup, or venture inside an established organization. It is motivated by the recognition that new enterprises fail more often from lack of customers than flaws in technology or product development. The course takes an iterative, design-oriented and feedback-driven approach to the search process: identifying a problem or need to address, developing a prototype, discovering customers, refining the concept, testing and validating demand, and developing a sustainable business model.
Entrepreneurship: New Venture Discovery: Read More [+]

Terms offered: 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 2021, Spring 2020, Fall 2019
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 Citizen Clinic Practicum: Read More [+]

Terms offered: Fall 2020
This course provides students with real-world experience assisting politically vulnerable organizations and persons around the world to develop and implement sound cybersecurity practices. Students will spend the majority of their credit hours engaging directly with clients under the supervision of Clinic staff. Emphasis will be on research to develop innovative security mitigations in response to threats of political adversaries.
Advanced Citizen Clinic Practicum: Read More [+]

Terms offered: Fall 2021, Spring 2021, Fall 2020
Specific topics, hours, and credit may vary from section to section, year to year.
Special Topics in Information: Read More [+]

Terms offered: Fall 2016, Fall 2015, Fall 2014

Special Topics in Information: Read More [+]

Terms offered: Fall 2021, Fall 2020, Fall 2019
Specific topics, hours, and credit may vary from section to section and year to year.
Special Topics in Management: Read More [+]

Terms offered: Spring 2021, Spring 2020, Spring 2019
Specific topics, hours, and credit may vary from section to section and year to year.
Special Topics in Technology: Read More [+]

Terms offered: Fall 2016, Spring 2016, Fall 2015
Students will build tools to explore and apply theories of information organization and retrieval. Students will implement various concepts covered in the concurrent 202 course through small projects on topics like controlled vocabularies, the semantic web, and corpus analysis. We will also experiment with topics suggested by students during the course. Students will develop skills in rapid prototyping of web-based projects using Python, XML, and jQuery.
Information Organization Laboratory: 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 2021, Spring 2020, Spring 2019
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 2021, Spring 2021, Fall 2020
Colloquia, discussion and readings designed to introduce students to the range of interests of the school.
Doctoral Colloquium: Read More [+]

Terms offered: Fall 2021, Spring 2021, Fall 2020
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 2016, Spring 2015, Spring 2014
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: Summer 2016 8 Week Session, Spring 2016, Fall 2015
Individual study of topics in information management and systems under faculty supervision.
Individual Study: Read More [+]

Terms offered: Fall 2021, Fall 2020, Fall 2019
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 2016, Fall 2015, Spring 2015
Individual study in consultation with the major field adviser, intended to provide an opportunity for qualified students to prepare themselves for the various examinations required of candidates for the Ph.D. degree.
Individual Study for Doctoral Students: Read More [+]