The doctoral program in Information Science is a research-oriented program in which the student chooses specific fields of specialization, prepares sufficiently in the literature and the research of those fields to pass a qualifying examination, and completes original research culminating in the written dissertation. The degree of Doctor of Philosophy is conferred in recognition of a candidate's grasp of a broad field of learning and distinguished accomplishment in that field through the contribution of an original piece of research revealing high critical ability and powers of imagination and synthesis.
We welcome students from a diverse set of backgrounds; some will be technically educated, some educated in the humanities and social sciences.
The I School typically accepts 3-7 PhD students each year from more than 100 applications. Applications are reviewed by a committee of faculty.
Applicants are evaluated holistically on a number of factors. A strong academic record is important, but not sufficient. A critical factor is the ability to demonstrate a research record and agenda that fit well with specific I School faculty. In a small, interdisciplinary program, it is important that applicants clearly indicate in their Statement of Purpose which faculty member(s) they are interested in researching with, and why.
To be eligible to apply to the PhD in Information Management and Systems program, applicants must meet the following requirements:
A bachelor's degree or its recognized equivalent from an accredited institution.
Superior scholastic record, normally well above a 3.0 GPA.
Indication of appropriate research goals, described in the Statement of Purpose.
For applicants whose academic work has been in a language other than English, the Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS).
Not required: GRE/GMAT. Starting Fall 2021, we no longer require the GRE or GMAT. We recommend you put your time and effort towards the required application materials.
Thank you for considering UC Berkeley for graduate study! UC Berkeley offers more than 120 graduate programs representing the breadth and depth of interdisciplinary scholarship. The Graduate Division hosts a complete list of graduate academic programs, departments, degrees offered, and application deadlines can be found on the Graduate Division website.
Prospective students must submit an online application to be considered for admission, in addition to any supplemental materials specific to the program for which they are applying. The online application and steps to take to apply can be found on the Graduate Division website.
Admission Requirements
The minimum graduate admission requirements are:
A bachelor’s degree or recognized equivalent from an accredited institution;
A satisfactory scholastic average, usually a minimum grade-point average (GPA) of 3.0 (B) on a 4.0 scale; and
Enough undergraduate training to do graduate work in your chosen field.
For a list of requirements to complete your graduate application, please see the Graduate Division’s Admissions Requirements page. It is also important to check with the program or department of interest, as they may have additional requirements specific to their program of study and degree. Department contact information can be found here.
The School of Information is an interdisciplinary school examining the design, organization, and management of information and information systems. The School of Information draws on the expertise not only of its own faculty but of the full Berkeley campus. We encourage students to take full advantage of being at this world-class University and not feel bound by disciplinary boundaries.
The PhD degree program at the School of Information is a research program. Each student is expected to work with his or her adviser to ensure that the program of study includes:
A thorough understanding of research methods and research design.
The ability to review current research critically.
The ability to understand emerging trends from an interdisciplinary perspective.
Expected PhD Timeline:
Semester 1: Identify a faculty adviser
Semesters 1–4: Complete breadth courses; complete major and minor requirements
Semester 4: Complete the preliminary research paper
Semester 5: Complete preliminary exam
Semester 6–8: Complete qualifying exam; advance to candidacy
Four semesters after qualifying exam: Complete dissertation and give presentation
Plus outside courses upon approval of your advisor
Related Courses
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 1.5 hours of lecture per week
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 [+]
Rules & Requirements
Prerequisites: Students should have a working knowledge of the Python programming language
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
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 [+]
Rules & Requirements
Prerequisites: Consent of instructor required for nonmajors
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
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.
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 [+]
Rules & Requirements
Prerequisites:INFO 206A or equivalent, or permission of instructor
Credit Restrictions: Course must be completed for a letter grade to fulfill degree requirements.
Hours & Format
Fall and/or spring: 7.5 weeks - 4 hours of lecture per week
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
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.
Course Objectives: The course objective is to provide students interested in web and mobile Product Design with skills, practice, and experience that will prepare them for careers in product design and design-related roles.
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
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 [+]
Rules & Requirements
Prerequisites: Graduate standing
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
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 [+]
Objectives & Outcomes
Course Objectives: Help students understand systematic human errors and explore potential algorithmic solutions.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
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.
Course Objectives:
INFO234 is a graduate level course in the school's topical area of Information Economics and Policy, and can be taken by the masters and doctoral students to satisfy their respective degree requirements.
Student Learning Outcomes:
Students will learn to identify, describe, and analyze business strategies and public policy issues of particular relevance to the information industry. Students will learn and apply economic tools and principles to analyze phenomena such as platform competition, social epidemics, and peer production, and current policy issues such as network neutrality and information privacy. Through integrated assignments and project work, the students will apply the theoretical concepts and analytic tools learned in lectures and readings to develop and evaluate a business model, product, or service of their choosing, e.g., a start-up idea they are pursuing.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 1.5 hours of lecture per week
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 [+]
Rules & Requirements
Prerequisites:INFO 206B or knowledge of programming and data structures with consent of instructor
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of laboratory per week
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 [+]
Objectives & Outcomes
Student Learning Outcomes: •
Effectively design, execute, and critique experimental and non-experimental methods from statistics, machine learning, and econometrics.
•
Implement basic algorithms on structured and unstructured data, and evaluate the performance of these algorithms on a variety of real-world datasets.
•
Understand the difference between causal and non-causal relationships, and which situations and methods are appropriate for both forms of analysis.
•
Understand the principles, advantages, and disadvantages of different algorithms for supervised and unsupervised machine learning.
Rules & Requirements
Prerequisites:INFO 206B, or equivalent course in Python programming; INFO 271B, or equivalent graduate-level course in statistics or econometrics; or permission of instructor
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
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.
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 [+]
Rules & Requirements
Prerequisites: Introductory programming
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture and 1 hour of laboratory per week
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 [+]
Objectives & Outcomes
Course Objectives: Critique the strengths and weaknesses of the different privacy paradigms Describe the different technical paradigms of privacy that are applicable for systems engineering Implement such privacy paradigms, and embed them in information systems during the design process and the implementation phase Stay updated about the state of the art in the field of privacy engineering
Rules & Requirements
Credit Restrictions: Students will receive no credit for INFO 255 after completing INFO 255. A deficient grade in INFO 255 may be removed by taking INFO 255.
Hours & Format
Fall and/or spring: 15 weeks - 1.5 hours of lecture per week
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 [+]
Rules & Requirements
Prerequisites:INFO 206A and INFO 206B or proficient programming in Python (programs of at least 200 lines of code). Proficient with basic statistics and probabilities
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
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 [+]
Rules & Requirements
Prerequisites:INFO 206B or equivalent college-level course in computer science in Python with a C- or better AND COMPSCI C100/DATA C100/STAT C100 or COMPSCI 189 or INFO 251 or DATA 144 or equivalent college-level course in data science with a C- or better
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
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 [+]
Rules & Requirements
Prerequisites: Familiarity with data structures, algorithms, linear algebra, and probability
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of laboratory per week
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Terms offered: Spring 2024, Spring 2023, Spring 2022
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of seminar per week
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 [+]
Rules & Requirements
Prerequisites: Students are expected to have prior graduate training in machine learning, econometrics, or a related field
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
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 [+]
Rules & Requirements
Repeat rules: Course may be repeated for credit with instructor consent.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
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 [+]
Rules & Requirements
Repeat rules: Course may be repeated for credit when topic changes. Students may enroll in multiple sections of this course within the same semester.
Hours & Format
Fall and/or spring: 8 weeks - 2-8 hours of lecture per week 15 weeks - 1-4 hours of lecture per week
Summer: 10 weeks - 1.5-6 hours of lecture per week
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 [+]
Rules & Requirements
Repeat rules: Course may be repeated for credit when topic changes. Students may enroll in multiple sections of this course within the same semester.
Hours & Format
Fall and/or spring: 8 weeks - 2-8 hours of lecture per week 15 weeks - 1-4 hours of lecture per week
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 [+]
Rules & Requirements
Repeat rules: Course may be repeated for credit when topic changes. Students may enroll in multiple sections of this course within the same semester.
Hours & Format
Fall and/or spring: 8 weeks - 4-8 hours of lecture per week 15 weeks - 2-4 hours of lecture per week
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.
Course Objectives: Experience the practical application of your academic knowledge to real-world professional contexts; Gain insight into an organization and how one might make a valuable contribution; Reflect on the information the experience has provided, to see if it fits within one’s personal value set and work/life manifestos. Try out various professional activities to see when you are in ‘flow’;
Student Learning Outcomes: Assess the organizational culture of a company, governmental body, or non-governmental organization
Connect academic knowledge about information management to real-world professional contexts
Evaluate the effectiveness of a variety of information science techniques when deployed in organizational situations
Integrate the student's own individual professional goals with the organization's needs relevant to the internship or practicum
Reflect critically on the internship or practicum experience
Rules & Requirements
Prerequisites: Consent of a Head Graduate Adviser for the School of Information
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 1 hour of internship per week
Summer: 10 weeks - 1.5 hours of internship per week
Additional Details
Subject/Course Level: Information/Graduate
Grading: Offered for satisfactory/unsatisfactory grade only.
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 [+]
Rules & Requirements
Prerequisites: PhD students only
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of workshop per week
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 [+]
Rules & Requirements
Prerequisites: Ph.D. standing in the School of Information
Hours & Format
Fall and/or spring: 15 weeks - 1 hour of colloquium per week
Additional Details
Subject/Course Level: Information/Graduate
Grading: Offered for satisfactory/unsatisfactory grade only.
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 [+]
Rules & Requirements
Prerequisites: Consent of instructor
Repeat rules: Course may be repeated for credit when topic changes.
Hours & Format
Fall and/or spring: 15 weeks - 2-4 hours of seminar per week
Terms offered: Fall 2019, Spring 2016, Fall 2015
Group projects on special topics in information management and systems. Directed Group Study: Read More [+]
Rules & Requirements
Prerequisites: Consent of instructor
Credit Restrictions: Students will receive no credit for INFO 298 after completing INFOSYS 298.
Repeat rules: Course may be repeated for credit when topic changes. Students may enroll in multiple sections of this course within the same semester.
Hours & Format
Fall and/or spring: 15 weeks - 1-4 hours of directed group study per week
Summer: 8 weeks - 1.5-7.5 hours of directed group study per week
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 [+]
Rules & Requirements
Prerequisites: Consent of instructor. Course must be taken for a letter grade to fulfill degree requirements
Hours & Format
Fall and/or spring: 15 weeks - 1-4 hours of directed group study per week
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 [+]
Rules & Requirements
Prerequisites: Consent of instructor
Repeat rules: Course may be repeated for credit when topic changes. Students may enroll in multiple sections of this course within the same semester.
Hours & Format
Fall and/or spring: 15 weeks - 1-12 hours of independent study per week
Summer: 8 weeks - 2-22.5 hours of independent study per week
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture per week
Additional Details
Subject/Course Level: Information/Professional course for teachers or prospective teachers
Grading: Offered for satisfactory/unsatisfactory grade only.
When you print this page, you are actually printing everything within the tabs on the page you are on: this may include all the Related Courses and Faculty, in addition to the Requirements or Overview. If you just want to print information on specific tabs, you're better off downloading a PDF of the page, opening it, and then selecting the pages you really want to print.
