phd in information systems management

RESEARCH AT THE LEADING EDGE

Doctoral Studies in Information Systems & Management

Ph.D. Studies in Information Systems & Management

The doctoral program in Information Systems & Management at Carnegie Mellon University's Heinz College prepares students with a deep understanding of the technical and organizational aspects of information systems.

At Heinz, we live and work at the critical nexus of information technology and public policy. Our Ph.D. in Information Systems & Management was created to train scholars to conduct innovative research that cuts across disciplines in order to address significant challenges in IT theory, strategy, management, and design as it relates to business and policy settings. 

Heinz College Ph.D. students enjoy close partnerships with faculty as they explore the complex and exciting interconnectedness of information systems, public policy, and management. Upon graduating, our Ph.D.s receive desirable placements at academic institutions, government agencies, and consulting firms.

KEY RESEARCH AREAS

Doctoral students take on a broad range of topics and problems, but some key areas of strength at Heinz College include:

As technology enables most content to be digitized, it is also upending business models, competition, and policy needs. From electronic health records, to streaming music and videos, to online social networks, digitization is rapidly affecting every part of the user experience, generating new jobs, and displacing old ones. Our faculty is working on a variety of projects under this broad umbrella. Some major projects are examining the role of social networks, online piracy, digital distribution, impact of mobile, the role of online education, and so on. Faculty and students use variety of methods like field experiments, analytical and structural models to study these questions.

Michael D. Smith and Rahul Telang are world recognized experts on the media industry and copyright policies who also head the IDEA research center.

Beibei Li is an expert on social media, mobile marketing, and understanding individuals’ online and offline decision making. 

Pedro Ferreira works on how people use technology in media and education, and is an expert on running randomized experiments.

Ramayya Krishnan  applies operations research tools to a variety of problems in this domain.

Our world-renowned faculty extensively works with both private firms and policy makers.  

We have multiple research centers like  IDEA ,  LARC , and  iLab  which collect large quantities of data to examine these issues.

Growth of big data has offered opportunities for development and application of novel statistical and computational methods for solving societal problems such as crime, policing, fraud detection, health care and more. To be able to use this data requires cutting edge work on developing new methods and machine learning algorithms. Heinz College has some of the top faculty who work at the intersection of machine learning and public policy.

Some key faculty members working in this space are Leman Akoglu , George Chen , Jeremy Weiss , and David Choi . Each of them is working on problems that intersect the need to use Machine Learning method to solve critical societal problems.

We also offer a joint degree in Machine Learning and Public Policy.

Data security and privacy has increasingly become a complex issue that goes beyond mere technology. Faculty at Heinz College are working on understanding users’ security and privacy decisions using economics, behavioral economics, and data analytics frameworks. This leading edge research is at the forefront of designing better tools and better regulations.

Alessandro Acquisti is an expert on economics of privacy and has done path-breaking work in this space.

Rahul Telang ’s work illustrates that firms may not do enough to protect user data, and highlights how we should design our policies.

Leman Akoglu uses large-scale data to understand our security and privacy vulnerabilities.

This group also works closely with faculty from CyLab , an interdisciplinary research center. This work is highly influential, widely cited, and extensively funded.

This group's research is motivated by information technology's important role in improving health care for patients, hospitals, and doctors. Technology is extensively used in detecting outbreaks, in providing superior quality of care at lower costs, and in prevention of medical errors.

Rema Padman studies IT adoption in hospitals and physical practices. 

Rahul Telang studies the role of electronic health records.

Martin Gaynor is a world-renowned expert on health policy and examines how technology can help improve policy outcomes.

Amelia Haviland examines the role of insurance policies and how they affect patient welfare.

As in other domains, our work on health care and IT is highly influential and has led to significant publications and extensive funding.

Ph.D. Curriculum

The pre-dissertation stage of the Ph.D. in Information Systems & Management is structured around two sets of requirements: coursework and preliminary papers.

Coursework is designed to build methodological skills, modeling competence, and substantive depth.

Preliminary papers illustrate your ability to produce effective research that exhibits your readiness to begin the dissertation.

• A three-semester   Ph.D. Seminar Series   focusing on the research process
• Two semesters of   Advanced Electives offering depth in specialized fields
• Quantitative Methods Cluster   of courses in statistics, econometrics, and machine learning
• Two semesters of coursework in   Social and Policy Sciences
• Concentration Area Requirement , combining research and courses to support your research agenda and long-term professional objectives
• Two semester-long  Technology Classes

Admission to candidacy means that all requirements of the Ph.D. program preliminary to the dissertation have been fulfilled. In addition to satisfying all coursework requirements, you must also meet the following research requirements:

• First- and second-year Research Papers meeting current Ph.D. requirements
• Dissertation focused on Information Systems topic as per judgment of Ph.D. committee

While fulfilling these requirements, you'll work closely with the faculty to develop individualized programs of study and research that meet your goals.

PhD In Management Information Systems

Program Overview

The PhD in Business Administration with a focus in Management Information Systems is a five-year full-time program. Consistently ranked among the best information systems PhD programs worldwide, the program is known for its cutting-edge research and support from actively publishing faculty.

The program prepares future information systems academics by providing strong foundations in a broad range of methods spanning psychometrics, econometrics, computational, design, and qualitative and by providing a strong emphasis in theory development to address important business and societal problems.

Given that information systems are ubiquitous and influence every aspect of life — individuals’ personal and work life, their transactions and interactions, organizational processes, outcomes, and interorganizational relationships, online platforms, markets, governments and society — the information systems field is broad and interdisciplinary and affords research opportunities across a diverse range of topics.

The research approach in the program is problem-focused, theory-based, and method-inclusive (i.e., all methods are welcome and no one single method is favored). Our PhD program provides you with significant individual flexibility, while at the same time ensuring you acquire the necessary conceptual and methodological skills to become a scholarly leader in our field.

Priority deadline: January 4

Applications after January 4 will also be considered until spots are filled

• C.Herman and Mary Virginia Terry Distinguished Chair of Business Administration, UGA Distinguished Research Professor and Professor , Department of Management Information Systems

Why a PhD in MIS?

There are five compelling reasons to join our program:

Research Productivity

We are among the most research-productive groups, consistently ranked in the top 10 or top 15 departments worldwide in publications in the top two IS journals ( MIS Quarterly and Information Systems Research ). Further, several of our faculty have won multiple research grants and awards for outstanding research.

Internationally Renowned Faculty

Our faculty includes a former president of the Association for Information Systems (Richard Watson), two Leo Award winners — the highest award in the field (Richard Watson and Elena Karahanna) — and three AIS Fellows (Richard Watson, Hugh Watson and Elena Karahanna).

Editorial Appointments

Our faculty includes current and former senior editors at MIS Quarterly , Information Systems Research , and the Journal of the Association for Information Systems , associate editors at MIS Quarterly , Information Systems Research , Management Science , and the Journal of the Association for Information Systems , and editorial board members of the Journal of Management Information Systems and Strategic Management Journal , among others.

Weekly Research Seminars

In these weekly seminars top scholars from around the world present and discuss their research. The PhD students have the opportunity to interact and discuss their research with these scholars in a meeting after the seminar.

Student Focus

Our culture is collaborative and supportive and one in which we view our students as junior colleagues. Students are provided extensive mentoring, support, and personal attention given our one-to-one faculty-student ratio. Evidence of the quality of mentoring is the outstanding placement of doctoral students and the plethora of journal papers co-authored with our faculty (over 100 publications in the past 10 years). Students can work with multiple faculty, not just their dissertation chair as they develop as scholars.

Typical Course Sequence

• MIST 9700 : IS Research Fundamentals
• MIST 9770 : Research Methods
• MIST 9760 : Foundational IS Theories and Emerging IS Phenomena
• MIST 9780 : Workshop & MTP
• Multivariate Statistics
• MIST 9750 : User Behavior and Technology Innovation 1
• MIST 9777 : Big Data Research Methods
• MGMT 9620 : Econometrics for Strategic Management
• GRSC 7770 : Teaching Seminar
• MIST 8990 : Directed Study
• First Year Exam (May)
• First Year Summer Paper due (beginning of Fall Semester)
• MIST 9790 : Combining Machine Learning and Econometrics
• MGMT 9610 : Introduction to SEM
• MIST 9710 : Digital Strategy and Digital Innovation 1
• MIST 9740 : Qualitative Research Methods
• Research Methods Elective
• Written & Oral Prelims
• Second Year Summer Paper due (beginning of Fall Semester)
• MIST 9000 : Doctoral Research
• Dissertation Proposal Defense
• MIST 9300 : Doctoral Dissertation 
• MIST 9300 : Doctoral Dissertation
• MIST 9300 : Doctoral Dissertation
• Dissertation Defense 2
• MIST 9750 and MIST 9710 are offered every other year. Some incoming PhD student cohorts will take MIST 9750 in their first year and MIST 9710 in their second year and others will take MIST 9750 in their second year and MIST 9710 in their first year.  ↩︎
• Dissertation defense occurs in the spring of their fifth year.  ↩︎

Our PhD graduates are placed in top research universities around the world.

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The School of Information is UC Berkeley’s newest professional school. Located in the center of campus, the I School is a graduate research and education community committed to expanding access to information and to improving its usability, reliability, and credibility while preserving security and privacy.

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The School of Information offers four degrees:

The Master of Information Management and Systems (MIMS) program educates information professionals to provide leadership for an information-driven world.

The Master of Information and Data Science (MIDS) is an online degree preparing data science professionals to solve real-world problems. The 5th Year MIDS program is a streamlined path to a MIDS degree for Cal undergraduates.

The Master of Information and Cybersecurity (MICS) is an online degree preparing cybersecurity leaders for complex cybersecurity challenges.

Our Ph.D. in Information Science is a research program for next-generation scholars of the information age.

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The School of Information's courses bridge the disciplines of information and computer science, design, social sciences, management, law, and policy. We welcome interest in our graduate-level Information classes from current UC Berkeley graduate and undergraduate students and community members.  More information about signing up for classes.

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Research by faculty members and doctoral students keeps the I School on the vanguard of contemporary information needs and solutions.

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Ph.D. Admissions

Next start date: August 2025

Application Deadline: December 3, 2024, 8:59 pm PST

We welcome students from a diverse set of backgrounds; some will be technically educated, some educated in the humanities and social sciences.

All application materials must be received by the deadline. We encourage you to apply early. The I School’s Ph.D. program does not accept applications for spring term admissions.

Admissions 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. Read more about our decision to drop the GRE/GMAT requirement .

Selection Criteria

The I School accepts 3–7 Ph.D. 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.

Application Requirements

We encourage you to check out our Ph.D. Admissions FAQ for information about commonly asked application questions.

(1) Statement of Purpose & Personal History Essay

The Statement of Purpose and Personal History are two separate essays.

The Statement of Purpose should describe your aptitude and motivation for doctoral study in your area of specialization, including your preparation for this field of study, your academic plans and research interests, and your future goals. Please be sure to identify in your Statement of Purpose which faculty member(s) you are interested in researching with, and why. We expect that candidates are able to demonstrate a research record and agenda that fit well with specific I School faculty.

For additional guidance, please review the Graduate Division's Statement of Purpose Guide .

In addition to explaining how your personal experiences have influenced your decision to pursue graduate studies, your Personal History Essay may include any relevant information describing barriers to accessing higher education that you have overcome, efforts you have made to advance equitable access to higher education for women, racial minorities, and other groups historically underrepresented in higher education, or research that you have undertaken that focuses on underserved populations or related issues of inequality.

For additional guidance, please review the Graduate Division’s Personal Statement Guide . There is no minimum length for the Personal History Essay.

These two essays are used in part to evaluate the candidate’s writing skills. Pursuant to UC Berkeley policy, the statements must be written by the candidate her or himself. For admitted students, application materials must comply with the Code of Student Conduct .

Both essays should be uploaded as PDF documents, as part of the online application .

(2) Three Letters of Recommendation

Ph.D. applicants should provide letters which speak directly to their ability and potential to perform academic research at the doctoral level. Recommenders must submit their letters online; please follow the instructions in the online application .

(3) Current Curriculum Vitae

Please upload a current curriculum vitae (C.V.) as a PDF document as part of the online application .

(4) College Transcripts

As part of the online application, upload copies of the official transcripts or academic records for all university-level studies you have completed abroad and at U.S. institutions. Be sure to include a current transcript from every post-secondary school that you have attended, including community colleges, summer sessions, and extension programs.

Each transcript should be uploaded as a separate PDF document; please refer to the instructions on the online application .

Applicants who completed their undergraduate degree in a recognized academic institution outside the United States are required to upload a copy of their degree conferral certificate. If a degree conferral certificate has not yet been obtained, please upload a provisional certificate. Applicants who have not yet graduated from undergrad are not required to submit a provisional certificate at this time. For specific questions, please contact the School of Information at [email protected] .

(5) TOEFL or IELTS Scores

UC Berkeley Graduate Division requires that applicants who received their degrees in countries other than the U.S., U.K., Australia, or English-speaking Canada submit TOEFL (Test of English as a Foreign Language) or IELTS (International English Language Testing System) scores. This includes applicants with degrees from Bangladesh, Burma, Nepal, India, Pakistan, Latin America, the Middle East, North Africa, the People’s Republic of China, Taiwan, Japan, Korea, Southeast Asia, and most European countries. Only applicants who have completed a full year of U.S. university-level coursework with a grade of B or better are exempt from this requirement.

For students taking the TOEFL, UC Berkeley Graduate Division requires that your most recent score be at least 90 on the Internet-based version of the TOEFL.

For students taking the IELTS, UC Berkeley Graduate Division requires that your most recent score be at least 7.0 out of 9.0 on the IELTS Academic test.

UC Berkeley Graduate Division does not accept TOEFL ITP Plus for Mainland China, IELTS Indicator, or Duolingo scores. For more information, see  Graduate Division’s Evidence of English Language Proficiency .

Submitting Scores

To be valid, the TOEFL or IELTS must have been taken within the past 18 months: for applicants for Fall 2025 admission, test scores taken before June 2023 will not be accepted. Please have your test scores sent directly to UC Berkeley by the testing authorities prior to application submission, and at the latest, by the application deadline. It may take 10-15 days for official score reports to transfer to our system. For the TOEFL exam, the school code for UC Berkeley is 4833, and the department code for the I School is 99.

For the IELTS exam, please submit an electronic report from the testing center; no institution code is required. Here is the Graduate Division’s office address for identification purposes: University of California, Berkeley, Graduate Division, Sproul Hall Rm 318, MC 5900, Berkeley, CA 94720.

More information: TOEFL website ; IELTS website

(6) Application Fee

(submitted with the online application)

• Fee for domestic applicants: $135.
• Fee for international applicants: $155.

Application Fee Waiver : The I School is pleased to offer application fee waivers to eligible Ph.D. applicants. Prior to submitting your application, please complete our Application Fee Waiver request form , and we will contact you within two business days with further instructions.

All application materials must be received by the application deadline. Applications will be reviewed throughout December and January, and admissions decisions will be released by early February.

Please don’t hesitate to contact us with questions or for additional guidance: [email protected] or (510) 664-4742.

*Test Report Form must be sent directly from IELTS. IELTS Indicator scores are not accepted.

Computer Ownership Requirement

We require that students own a computer. No particular configuration or operating system is required. However, students will be expected to complete assignments using office productivity software (e.g., Microsoft Office, OpenOffice, etc.), web browsers, etc., and should own a computer capable of running such software. More specific guidance will be provided upon acceptance to the program.

Contact the admissions team with questions about the Ph.D. program or application.

Ph.D. Applicant Feedback Program

The I School Ph.D. Applicant Feedback Program is a student-run initiative that aims to assist underrepresented students with their application essays and C.V. as they apply to the UC Berkeley School of Information Ph.D. program.

More Information

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Department of Technology, Operations, and Statistics | Doctoral Program in Information Systems

Doctoral program in information systems.

Authoritative curriculum information can be found exclusively in the University Bulletin. All other content, including this page, is for informational purpose only. You can find the curriculum for this program on this page of the Bulletin.

• Overview of the Doctoral Program in Information Systems

Program Requirements

Doctoral Courses

• Doctoral Students and their Research
• Information Systems Faculty

Overview of the IS Doctoral Program

Mission: To educate and train scholars who will produce first-rate IS research and who will succeed as faculty members in first-rate universities. We offer tracks in technical perspectives on IS, economic perspectives on IS, and organizational/management perspectives on IS. Admissions and performance: We enroll an average of three students each year out of more than 100 highly qualified applicants. Students enrolling typically have GMATS over 700 or GREs over 1400. International students typically have TOEFLs higher than 640. Our students are highly competitive within Stern and nationally. Recently our students have received school-wide awards as "outstanding doctoral students." They have won acceptance at doctoral consortia sponsored by the Academy of Management and the International Conference on Information Systems. And they have won national dissertation research competitions.

Advising and evaluation: The IS doctoral program faculty director advises all first-year doctoral students. During the first year students have many opportunities to get to know the research interests of all departmental faculty. By the beginning of the second year, students have selected a concentration advisor who will guide them through the comprehensive exam process and up to the thesis stage. By the middle of the third year students will have selected a thesis advisor. Each year every student submits a statement of intellectual progress to his/her advisor. All faculty meet to review the progress of all students in a day-long meeting each year. At this time, the student's intellectual progress is reviewed and plans for the following year are considered. The results of this review include a formal letter to the student assessing the previous year's work and offering guidance for the following year's work. All students take a comprehensive written and oral exam at the end of the second year. Students defend their thesis proposal by March of their fourth year and defend their completed dissertation at the end of the fourth year or during the fifth year.

Research and interaction with faculty: The heart of the IS doctoral program is immersion in a community of researchers. Every student has a formal research apprenticeship with one or more faculty members each year. Every student participates in formal and informal research seminars each week with departmental faculty and visitors. Every student presents research in progress and works toward producing publishable papers, usually with a faculty co-author. Students learn to be researchers by doing research. They learn to be research colleagues by working with others and critiquing their research.

Placement record: In the past ten years, our graduates have accepted faculty positions at such schools as University of California at Berkeley, Hong Kong University of Science & Technology, University of Maryland, University of Minnesota, University of Texas at Austin, the University of British Columbia, National University of Singapore, The Wharton School and the University of Cambridge, UK.   Please click on the links on the right to learn how to apply, to attend an information session, and to contact the Stern School Doctoral Office. 

Natalia Levina Coordinator, Information Systems Doctoral Program IOMS Department

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All students take a common core of courses during their first year which provides an overview of the major research areas in IS and the fundamental knowledge necessary for specialized course work in the second year. In the second year students take specialized course work in one of three concentrations: technical perspectives, economic perspectives, behavioral/managerial perspectives.  

Mandatory Breadth Courses (3)

• Behavioral Research Methods
• Micro-economics
• Technical Foundations
• Each student is required to take 1 Probability and 1 Statistics course, from a list of approved courses.
• Technical Research in IS
• Economics Research in IS
• Behavioral/Managerial Research in IS
• Research Apprenticeship

YEAR TWO - Each student chooses one concentration track

Technical Track:

• A programming requirement, may be satisfied in a variety of ways
• Honors Analysis of Algorithms
• Artificial Intelligence
• Optimization
• Database Systems
• Machine Learning/Data Mining
• Other courses based on student's interest
• Research apprenticeship

Economics Track:

• Mathematical Methods for Economists
• Econometrics
• Game Theory
• Students will take elective courses in the Stern Economics Department, at the Graduate School of Arts and Sciences, in Operations Management, Statistics, or at Courant as specified in consultation with the advisor

Behavioral/Managerial Track:

• Any two of the following four Stern Management Department Courses
• Organizational Behavior
• Managerial Cognition
• Organizational Theory
• At least one research methods or statistics course beyond the first year courses.
• Students may take doctoral level courses in Psychology, Sociology, Political Science, Public Policy, History, Education, or Law.
• Electives in the area of interest
• Thesis research
• Teaching apprenticeship (in year 3 or 4)
• Teaching one course (in year 3 or 4)
• INFO-GB.3345 (B20.3345)  Doctoral Seminar in Digital Economics  (offered in Spr 2012) This course introduces students to scientific paradigms and research perspectives related to the economics of information technologies. Topics in 2012 include information goods, piracy, digital rights management, network economics, sponsored search auctions, user-generated content, contagion in networks, technological innovation, IT productivity, the digital commons and online privacy.  
• INFO-GB.3382 (B20.3382)  Research Seminar on IT and Organizations: Social Perspectives (offered in Spr 2012) The course introduces students to sociological and organizational literature on the role of Information Technology in organizations and society.  
• INFO-GB.3383 (B20.3383)  Networks, Crowds & Markets   
• INFO-GB.3386 (B20.3386)  Technical Foundations of IS  
• INFO-GB.3355 (B20.3355)  Behavioral Research Methods  
• INFO-GB.3391 (B20.3391)  Research Seminar in Data Science   (offered in Spr 2012) In this course we will take a deep dive into selected topics in data science. The focus will be two-fold. First, we will read textbook segments, classic papers, and new research, with the goal of understanding research in data science. Second, we will study the actual practical application of data science methods to extract knowledge from large-scale data. We will cover topics such as machine learning, data mining, information retrieval, text classification, sentiment analysis, similarity analysis, network analysis, graphical models, Bayesian models, topic models, model evaluation, crowd-sourcing and micro-outsourcing, massive-scale data processing, reducing data for analytic purposes, and more. The selection of which topics are covered in a particular semester will be based on: (i) the current research and business environments, (ii) the research interests of the IS faculty, and (iii) the interests of the students in that semester. We also will discuss applications that are of current interest, such as recommender systems, social-network marketing, online advertising, Mechanical Turking, and more.

Questions about the PhD Program in Information Systems?

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Management Information Systems

The Ph.D. program in Management Information Systems is NOW accepting applications for Fall 2012.

The MIS doctoral program will not accept applications in 2020.

Due to our large MIS PhD class enrollment in Fall 2019, we do not expect to be able to enroll students in Fall 2020.

“I am interested in information technology (IT) and its application in organizations. I have an interest in research, teaching about, and in helping to invent a future around, applications of IT.”

If that sounds like you, keep reading; this may be the first step towards an exciting and promising career!

Ph.D. versus MS

Deciding between getting a Ph.D. or an MS? (Note: a master's degree is not required in order to pursue a Ph.D.) Typical differences between working in academia with a Ph.D. versus working in industry with an MS:

• Individuals with a Ph.D. create knowledge; individuals with an MS apply knowledge (e.g., created by those with a Ph.D.).
• A Ph.D. enables an individual to choose and work in the areas and issues that pique his or her interest. An individual with an MS typically works on problems that are important to others (e.g., his or her supervisor).
• Individuals with a Ph.D. get paid for learning new things; individuals with an MS typically pay to learn new things (e.g., by taking advanced classes).
• Individuals with a Ph.D. have many more opportunities to teach undergraduate and/or graduate classes.

What is Management Information Systems?

MIS faculty teach and research about the application of information systems in organizations. This includes the study of social networks, cloud computing, IT consumerization, virtual reality, negotiation systems, collaboration technologies, office automation, electronic payments, strategic information systems, electronic commerce, collective intelligence, tele-medicine, electronic markets, social media, information requirements analysis, systems development methods, enterprise resource planning systems, systems implementation, adoption, and diffusion, mobile computing, and much more. The information technologies and systems we teach and research transform people’s lives, jobs and, for industry after industry, business models, products, supply chains, and distribution channels.

We are also interested in the implications of those technologies for people and society; personal privacy, infrastructure dependency, security, safeguarding of intellectual property, and IT-related stress all capture the interest of MIS faculty. So too do the political implementations of the internet, the evolution of the field, and IT in developing nations. And that’s just today! Ours is one of the most dynamic fields; the area your dissertation will focus on in several years may not have even been conceived of today.

In addition to furthering your knowledge about information systems, you will be given the opportunity to develop the research skills and mindset necessary to be a successful scholar. In our seminars, as well as those you will take in other disciplines, you will learn about research methods, data analysis approaches, and theories applicable to your research. You will also gain necessary teaching expertise and experience.

MIS Faculty Spotlight

Please follow the links below in order to get an idea of the depth and breadth of research being pursued by our MIS faculty.

*Faculty noted with an asterisk have been recognized as being in the top 3% of MIS researchers.

You are also most welcome to contact Randolph Cooper , the MIS Ph.D. coordinator. Before doing so please look over the materials on this web site. Many of your questions will be answered here.

Student Initial Placements

One hundred percent of our Ph.D. graduates who desired academic employment were placed in one of the following academic institutions.

Arizona State University Baylor College of Medicine Dankook University (South Korea) INCAE (Costa Rica) Iowa State University Kennesaw State University Louisiana State University Murray State University Ohio University Penn State University Southern University – New Orleans Texas Christian University Texas Southern University U. Tunku Abdul Rahman (Malaysia) University of Alaska – Anchorage

University of Central Missouri University of Colorado – Denver University of Georgia University of Houston University of Houston – Clear Lake University of Melbourne (Australia) University of Missouri – St. Louis University of Nevada – Reno University of North Florida University of Saint Thomas University of South Florida University of Southern Indiana University of Texas – San Antonio University of Tulsa University of Wisconsin–Milwaukee

Ph.D. Dissertation Topics

Rather than restrict our students’ research to specific topics in information systems, students have the freedom to explore topics in which they have strong interests. When these topics require expertise not available within our MIS faculty, we are happy to draw from other disciplines within the University of Houston as well as from other Universities in order to create appropriate dissertation committees. Below are some dissertation topics that have been explored by our past Ph.D. students.

Information Science: PhD

About the Program

The doctoral program.

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.

The I School also offers a master's in Information Management and Systems (MIMS), a master's in  Information and Data Science  (MIDS), and a master's in  Information and Cybersecurity (MICS).

Visit School Website

Admission to the PhD Program

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.

Further  information about I School Ph.D. Admissions  can be found on the I School website. 

Applying for Graduate Admission

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 .

Where to apply?

Visit the Berkeley Graduate Division application page .

Doctoral Degree Requirements

Program design.

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

Please refer to  the School of Information website  for more information.

Breadth Courses

Major/Minor Areas

Related Courses

Info 201 research design and applications for data and analysis 3 units.

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

Additional Format: One and one-half hours of lecture per week.

Additional Details

Subject/Course Level: Information/Graduate

Grading: Letter grade.

Research Design and Applications for Data and Analysis: Read Less [-]

INFO 202 Information Organization and Retrieval 3 Units

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

Fall and/or spring: 15 weeks - 3 hours of lecture per week

Additional Format: Three hours of lecture per week.

Information Organization and Retrieval: Read Less [-]

INFO 203 Social Issues of Information 3 Units

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 [+]

Social Issues of Information: Read Less [-]

INFO 205 Information Law and Policy 3 Units

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 [+]

Prerequisites: Consent of instructor required for nonmajors

Instructor: Mulligan

Information Law and Policy: Read Less [-]

INFO 206A Introduction to Programming and Computation 2 Units

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 [+]

Fall and/or spring: 7.5 weeks - 4 hours of lecture per week

Additional Format: Four hours of lecture per week for seven and one-half weeks.

Instructor: Farid

Introduction to Programming and Computation: Read Less [-]

INFO 206B Introduction to Data Structures and Analytics 2 Units

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 [+]

Prerequisites: INFO 206A or equivalent, or permission of instructor

Credit Restrictions: Course must be completed for a letter grade to fulfill degree requirements.

Formerly known as: Information 206

Introduction to Data Structures and Analytics: Read Less [-]

INFO 213 Introduction to User Experience Design 4 Units

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 [+]

Introduction to User Experience Design: Read Less [-]

INFO 214 User Experience Research 3 Units

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 [+]

Additional Format: Three hours of Lecture per week for 15 weeks.

User Experience Research: Read Less [-]

INFO 215 Product Design Studio 3 Units

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 [+]

Objectives & Outcomes

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.

Prerequisites: DES INV 15 or COMPSCI 160 or INFO 213 AND INFO 214; or permission of the instructor. Students can take INFO 214 and INFO 215 concurrently, but students may not drop INFO 214 and remain in INFO 215

Formerly known as: Information Systems and Management 215

Product Design Studio: Read Less [-]

INFO 217A Human-Computer Interaction (HCI) Research 3 Units

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 [+]

Instructor: Salehi

Human-Computer Interaction (HCI) Research: Read Less [-]

INFO 218 Concepts of Information 3 Units

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 [+]

Prerequisites: Graduate standing

Instructors: Duguid, Nunberg

Concepts of Information: Read Less [-]

INFO 225 Leadership and Management 3 Units

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 [+]

Leadership and Management: Read Less [-]

INFO 231 Decisions and Algorithms 3 Units

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 [+]

Course Objectives: Help students understand systematic human errors and explore potential algorithmic solutions.

Decisions and Algorithms: Read Less [-]

INFO 233 Social Psychology and Information Technology 3 Units

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 [+]

Instructor: Cheshire

Social Psychology and Information Technology: Read Less [-]

INFO 234 Information Technology Economics, Strategy, and Policy 3 Units

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 [+]

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.

Instructor: Chuang

Information Technology Economics, Strategy, and Policy: Read Less [-]

INFO 239 Technology and Delegation 3 Units

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 [+]

Technology and Delegation: Read Less [-]

INFO 241 Experiments and Causal Inference 3 Units

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 [+]

Experiments and Causal Inference: Read Less [-]

INFO 247 Information Visualization and Presentation 4 Units

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 [+]

Prerequisites: INFO 206B or knowledge of programming and data structures with consent of instructor

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of laboratory per week

Additional Format: Three hours of lecture and one hour of laboratory per week.

Instructor: Hearst

Information Visualization and Presentation: Read Less [-]

INFO 251 Applied Machine Learning 4 Units

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 [+]

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.

Prerequisites: INFO 206B , or equivalent course in Python programming; INFO 271B , or equivalent graduate-level course in statistics or econometrics; or permission of instructor

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Format: Three hours of lecture and one hour of discussion per week.

Instructor: Blumenstock

Applied Machine Learning: Read Less [-]

INFO 253A Front-End Web Architecture 3 Units

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 [+]

Prerequisites: Introductory programming

Fall and/or spring: 15 weeks - 2 hours of lecture and 1 hour of laboratory per week

Additional Format: Two hours of lecture and one hour of laboratory per week.

Formerly known as: Information 253

Front-End Web Architecture: Read Less [-]

INFO 253B Back-End Web Architecture 3 Units

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 [+]

Back-End Web Architecture: Read Less [-]

INFO 255 Privacy Engineering 3 Units

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 [+]

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

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 .

Privacy Engineering: Read Less [-]

INFO 256 Applied Natural Language Processing 3 Units

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 [+]

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

Instructor: Bamman

Applied Natural Language Processing: Read Less [-]

INFO 258 Data Engineering 4 Units

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 [+]

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

Instructors: Hellerstein, Parameswaran, Jain

Data Engineering: Read Less [-]

INFO 259 Natural Language Processing 4 Units

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 [+]

Prerequisites: Familiarity with data structures, algorithms, linear algebra, and probability

Natural Language Processing: Read Less [-]

INFO C262 Theory and Practice of Tangible User Interfaces 4 Units

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 [+]

Instructor: Ryokai

Also listed as: NWMEDIA C262

Theory and Practice of Tangible User Interfaces: Read Less [-]

INFO C265 Interface Aesthetics 3 Units

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 [+]

Also listed as: NWMEDIA C265

Interface Aesthetics: Read Less [-]

INFO 271B Quantitative Research Methods for Information Systems and Management 3 Units

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 [+]

Prerequisites: Introductory statistics recommended

Quantitative Research Methods for Information Systems and Management: Read Less [-]

INFO 272 Qualitative Research Methods for Information Systems and Management 3 Units

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 [+]

Instructor: Burrell

Qualitative Research Methods for Information Systems and Management: Read Less [-]

INFO 283 Information and Communications Technology for Development 3 Units

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 [+]

Fall and/or spring: 15 weeks - 3 hours of seminar per week

Additional Format: Three hours of seminar per week.

Instructor: Saxenian

Formerly known as: Information C283

Information and Communications Technology for Development: Read Less [-]

INFO 288 Big Data and Development 3 Units

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 [+]

Prerequisites: Students are expected to have prior graduate training in machine learning, econometrics, or a related field

Big Data and Development: Read Less [-]

INFO 289 Public Interest Cybersecurity: The Cybersecurity Clinic Practicum 3 Units

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 [+]

Repeat rules: Course may be repeated for credit with instructor consent.

Public Interest Cybersecurity: The Cybersecurity Clinic Practicum: Read Less [-]

INFO 290 Special Topics in Information 1 - 4 Units

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 [+]

Repeat rules: Course may be repeated for credit when topic changes. Students may enroll in multiple sections of this course within the same semester.

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

Additional Format: One to four hours of lecture per week. One and one-half to six hours of lecture per week for 10 weeks. Two to eight hours of lecture per week for 8 weeks.

Special Topics in Information: Read Less [-]

INFO 290M Special Topics in Management 1 - 4 Units

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 [+]

Additional Format: One to four hours of lecture per week. Two to eight hours of lecture per week for 8 weeks.

Special Topics in Management: Read Less [-]

INFO 290S Special Topics in Social Science and Policy 2 - 4 Units

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 [+]

Fall and/or spring: 8 weeks - 4-8 hours of lecture per week 15 weeks - 2-4 hours of lecture per week

Additional Format: Two to four hours of lecture per week. Four to eight hours of lecture per week for 8 weeks.

Special Topics in Social Science and Policy: Read Less [-]

INFO 290T Special Topics in Technology 2 - 4 Units

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 [+]

Special Topics in Technology: Read Less [-]

INFO 291 Special Topics in Information 1 - 4 Units

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 [+]

Repeat rules: Course may be repeated for credit when topic changes.

Fall and/or spring: 15 weeks - 1-4 hours of lecture per week

Additional Format: One to four hours of lecture per week.

Grading: Offered for satisfactory/unsatisfactory grade only.

Instructor: Hoofnagle

INFO 293 Information Management Practicum 0.5 Units

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 [+]

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

Prerequisites: Consent of a Head Graduate Adviser for the School of Information

Repeat rules: Course may be repeated for credit without restriction.

Fall and/or spring: 15 weeks - 1 hour of internship per week

Summer: 10 weeks - 1.5 hours of internship per week

Additional Format: One hour of internship per week. One and one-half hours of internship per week for 10 weeks.

Information Management Practicum: Read Less [-]

INFO 294 Doctoral Research and Theory Workshop 2 Units

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 [+]

Prerequisites: PhD students only

Fall and/or spring: 15 weeks - 2 hours of workshop per week

Additional Format: Two hours of workshop per week.

Doctoral Research and Theory Workshop: Read Less [-]

INFO 295 Doctoral Colloquium 1 Unit

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 [+]

Prerequisites: Ph.D. standing in the School of Information

Fall and/or spring: 15 weeks - 1 hour of colloquium per week

Additional Format: One hour of colloquium per week.

Doctoral Colloquium: Read Less [-]

INFO 296A Seminar 2 - 4 Units

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 [+]

Prerequisites: Consent of instructor

Fall and/or spring: 15 weeks - 2-4 hours of seminar per week

Additional Format: Two to Four hours of Seminar per week for 15 weeks.

Seminar: Read Less [-]

INFO 298 Directed Group Study 1 - 4 Units

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

Credit Restrictions: Students will receive no credit for INFO 298 after completing INFOSYS 298.

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

Additional Format: One to four hours of directed group study per week. One and one-half to seven and one-half hours of directed group study per week for 8 weeks.

Directed Group Study: Read Less [-]

INFO 298A Directed Group Work on Final Project 1 - 4 Units

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 [+]

Prerequisites: Consent of instructor. Course must be taken for a letter grade to fulfill degree requirements

Additional Format: One to four hours of directed group study per week.

Directed Group Work on Final Project: Read Less [-]

INFO 299 Individual Study 1 - 12 Units

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 [+]

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

Additional Format: Format varies.

Individual Study: Read Less [-]

INFO 375 Teaching Assistance Practicum 2 Units

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 [+]

Fall and/or spring: 15 weeks - 2 hours of lecture per week

Additional Format: Two hours of lecture per week.

Subject/Course Level: Information/Professional course for teachers or prospective teachers

Instructor: Duguid

Teaching Assistance Practicum: Read Less [-]

Contact Information

School of information.

102 South Hall

Phone: 510-642-1464

Senior Director of Student Affairs

Siu Yung Wong

[email protected]

Senior Director of Admissions

Julia Sprague

[email protected]

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