About the Program

The Master of Information and Data Science (MIDS) is a part-time professional degree program that prepares students to work effectively with heterogeneous, real-world data (ranging from tweet streams and call records to mouse clicks and GPS coordinates) and to extract insights from the data using the latest tools and analytical methods. The program emphasizes the importance of asking good research or business questions as well as the ethical and legal requirements of data privacy and security.

The curriculum includes research design and applications for data and analysis, storing and retrieving data, exploring and analyzing data, identifying patterns in data, and effectively visualizing and communicating data. MIDS features a project-based approach to learning and encourages the pragmatic application of a variety of different tools and methods to solve complex problems.

Graduates of the program will be able to:

• Imagine new and valuable uses for large datasets;
• Retrieve, organize, combine, clean, and store data from multiple sources;
• Apply appropriate data mining, statistical analysis, and machine learning techniques to detect patterns and make predictions;
• Design visualizations and effectively communicate findings; and
• Understand the ethical and legal requirements of data privacy and security.

The I School also offers a master's in Information Management and Systems (MIMS).

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Admissions

Admission to the University

Minimum Requirements for Admission

The following minimum requirements apply to all graduate programs and will be verified by the Graduate Division:

1. A bachelor’s degree or recognized equivalent from an accredited institution;
2. A grade point average of B or better (3.0);
3. If the applicant comes from a country or political entity (e.g., Quebec) where English is not the official language, adequate proficiency in English to do graduate work, as evidenced by a TOEFL score of at least 90 on the iBT test, 570 on the paper-and-pencil test, or an IELTS Band score of at least 7 (note that individual programs may set higher levels for any of these); and
4. Sufficient undergraduate training to do graduate work in the given field.

Applicants Who Already Hold a Graduate Degree

The Graduate Council views academic degrees not as vocational training certificates, but as evidence of broad training in research methods, independent study, and articulation of learning. Therefore, applicants who already have academic graduate degrees should be able to pursue new subject matter at an advanced level without need to enroll in a related or similar graduate program.

Programs may consider students for an additional academic master’s or professional master’s degree only if the additional degree is in a distinctly different field.

Applicants admitted to a doctoral program that requires a master’s degree to be earned at Berkeley as a prerequisite (even though the applicant already has a master’s degree from another institution in the same or a closely allied field of study) will be permitted to undertake the second master’s degree, despite the overlap in field.

The Graduate Division will admit students for a second doctoral degree only if they meet the following guidelines:

1. Applicants with doctoral degrees may be admitted for an additional doctoral degree only if that degree program is in a general area of knowledge distinctly different from the field in which they earned their original degree. For example, a physics PhD could be admitted to a doctoral degree program in music or history; however, a student with a doctoral degree in mathematics would not be permitted to add a PhD in statistics.
2. Applicants who hold the PhD degree may be admitted to a professional doctorate or professional master’s degree program if there is no duplication of training involved.

Applicants may apply only to one single degree program or one concurrent degree program per admission cycle.

Required Documents for Applications

1. Transcripts: Applicants may upload unofficial transcripts with your application for the departmental initial review. If the applicant is admitted, then official transcripts of all college-level work will be required. Official transcripts must be in sealed envelopes as issued by the school(s) attended. If you have attended Berkeley, upload your unofficial transcript with your application for the departmental initial review. If you are admitted, an official transcript with evidence of degree conferral will not be required.
2. Letters of recommendation: Applicants may request online letters of recommendation through the online application system. Hard copies of recommendation letters must be sent directly to the program, not the Graduate Division.
3. Evidence of English language proficiency: All applicants from countries or political entities in which the official language is not English are required to submit official evidence of English language proficiency. This applies to applicants from Bangladesh, Burma, Nepal, India, Pakistan, Latin America, the Middle East, the People’s Republic of China, Taiwan, Japan, Korea, Southeast Asia, most European countries, and Quebec (Canada). However, applicants who, at the time of application, have already completed at least one year of full-time academic course work with grades of B or better at a US university may submit an official transcript from the US university to fulfill this requirement. The following courses will not fulfill this requirement:
   • courses in English as a Second Language,
   • courses conducted in a language other than English,
   • courses that will be completed after the application is submitted, and
   • courses of a non-academic nature.

If applicants have previously been denied admission to Berkeley on the basis of their English language proficiency, they must submit new test scores that meet the current minimum from one of the standardized tests.

Where to Apply

Visit the Berkeley Graduate Division application page. 

Admission to the Program

Applications are evaluated holistically on a combination of prior academic performance, GRE/GMAT score, work experience, statement of purpose, and letters of recommendation.

The UC Berkeley School of Information seeks students with the academic abilities to meet the demands of a rigorous graduate program.

To be eligible to apply to the Master of Information and Data Science 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.
• Official Graduate Record Examination (GRE) General Test or Graduate Management Admission Test (GMAT) scores.
• A high level of quantitative ability as demonstrated by scores in the top 15 percent in the Quantitative section of either the GRE or GMAT, five years of technical work experience, or significant work experience that demonstrates your quantitative abilities.
• A high level of analytical reasoning ability and a problem-solving mindset as demonstrated in academic and/or professional performance.
• A working knowledge of fundamental concepts including: data structures, algorithms and analysis of algorithms, and linear algebra.
• Programming proficiency as demonstrated by prior work experience or advanced coursework. (For example: Python, Java, or R.)
• The ability to communicate effectively, as demonstrated by strong scores in the Verbal and Writing sections of either the GRE or GMAT, academic performance, or professional experience.
• A Statement of Purpose that clearly indicates professional career goals and reasons for seeking the degree.
• Official Test of English as a Foreign Language (TOEFL) scores for applicants whose academic work has been in a country other than the US, UK, Australia, or English-speaking Canada.

For more information and application instructions, please visit the datascience@berkeley Admissions Overview.

Master's Degree Requirements

Unit Requirements

The Master of Information and Data Science is designed to be completed in 20 months, but other options are available to complete the program. You will complete 27 units of course work over an average of five terms, taking a maximum of 9 units each term. Courses are divided into foundation courses (15 units), advanced courses (9 units), and a synthetic capstone (3 units). You will also complete an immersion at the UC Berkeley campus.

Curriculum

Immersion

As a Master of Information and Data Science (MIDS) student, the immersion is your opportunity to meet faculty and peers in person on the UC Berkeley campus. You will have the opportunity to gain on-the-ground perspectives from faculty and industry leaders, meet with data science professionals, and soak up more of the School of Information (I School) culture. Offered twice a year, each four- to five-day immersion will be custom crafted to deliver additional learning, networking, and community-building opportunities.

Please refer to the datascience@berkeley website for more information.

Courses

Please note: DATASCI courses are only available for Information and Data Science (MIDS) students.

Information and Data Science

Faculty and Instructors

+ Indicates this faculty member is the recipient of the Distinguished Teaching Award.

Faculty

David Bamman, Assistant Professor.

+ Robert Berring, Professor. China, law, contracts, Chinese law.
Research Profile

Jenna Burrell, Associate Professor.

Coye Cheshire, Associate Professor. Sociology, trust, social media, social psychology, social networks, collective action, social exchange, information exchange, social incentives, reputation, internet research, online research, online dating, online behavior.
Research Profile

John Chuang, Professor. Computer networking, computer security, economic incentives, ICTD.
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Paul Duguid, Adjunct Professor. Trademark, information, communities of practice.
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Robert J. Glushko, Adjunct Professor.

Morten Hansen, Professor.

Marti A. Hearst, Professor. Information retrieval, human-computer interaction, user interfaces, information visualization, web search, search user interfaces, empirical computational linguistics, natural language processing, text mining, social media.
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Ray Larson, Professor. Information Retrieval system design and evaluation, database management.
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Deirdre Mulligan, Associate Professor.

Geoffrey D. Nunberg, Adjunct Professor.

Zach Pardos, Assistant Professor. Education Data Science, Learning Analytics, Big Data in Education, data mining, Data Privacy and Ethics, Computational Psychometrics, Digital Learning Environments, Cognitive Modeling, Bayesian Knowledge Tracing, Formative Assessment, Learning Maps, machine learning.
Research Profile

David H. Reiley, Adjunct Professor.

Kimiko Ryokai, Associate Professor.

Pamela Samuelson, Professor. Public policy, intellectual property law, new information technologies, traditional legal regimes, information management, copyright, software protection and cyberlaw.
Research Profile

Annalee Saxenian, Professor. Innovation, information management, entrepreneurship, Silicon Valley, regional economic development, high skilled immigration, Asian development.
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Doug Tygar, Professor. Privacy, technology policy, computer security, electronic commerce, software engineering, reliable systems, embedded systems, computer networks, cryptography, cryptology, authentication, ad hoc networks.
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Steven Weber, Professor. Political science, international security, international political economy, information science.
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Qiang Xiao, Adjunct Professor.

Lecturers

Brooks D. Ambrose, Lecturer.

Lefteris Anastasopoulos, Lecturer.

Olukayode Segun Ashaolu, Lecturer.

Kurt Beyer, Lecturer.

Dav Clark, Lecturer.

Steven Fadden, Lecturer.

Alexander Gilgur, Lecturer.

Benjamin T. Gimpert, Lecturer.

Nathaniel Stanley Good, Lecturer.

Annette Greiner, Lecturer.

Quentin R. Hardy, Lecturer.

Anna Lauren Hoffmann, Lecturer.

Todd Michael Holloway, Lecturer.

Douglas Alex Hughes, Lecturer.

Jez Humble, Lecturer.

Coco Krumme, Lecturer.

Arash Nourian, Lecturer.

Emmanouil Papangelis, Lecturer.

Daniel Percival, Lecturer.

Daniel Perry, Lecturer.

Elisabeth Prescott, Lecturer.

Dmitry Rekesh, Lecturer.

Blaine Gary Robbins, Lecturer.

Ali Sanaei, Lecturer.

Juanjie Joyce Shen, Lecturer.

David Steier, Lecturer.

Andreas Weigend, Lecturer.

Peter Frank Weis, Lecturer.

Jake Ryland Williams, Lecturer.

Scott Young, Lecturer.

Visiting Faculty

Ramakrishna Akella, Visiting Professor.

Paul Laskowski, Visiting Assistant Professor.

Emeritus Faculty

Michael Buckland, Professor Emeritus. Information management, information retrieval, metadata, library services.
Research Profile

Michael D. Cooper, Professor Emeritus. Analysis, design, database management systems, implementation and evaluation of information systems, computer performance monitoring and evaluation, and library automation.
Research Profile

William S. Cooper, Professor Emeritus.

M. E. Maron, Professor Emeritus.

Nancy A. Van House, Professor Emeritus. Digital libraries, science, information management, technology studies, knowledge communities, user needs, information tools, artifacts, participation of users.
Research Profile