Information and Data Science: MIDS

University of California, Berkeley

About the Program

The Master of Information and Data Science (MIDS) is an online, part-time professional degree program that prepares students to work effectively with heterogeneous, real-world data 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 Fifth Year Master of Information and Data Science (Fifth Year MIDS) is a customized pathway in the MIDS program that is designed for students who have just completed their undergraduate education at UC Berkeley. 

Students attend weekly live ("synchronous") sessions with classmates and instructors via an online platform as well as engaging with online ("asynchronous") videos and assignments on their own time. 

The curriculum includes research design and applications for data and analysis, statistics for data science, data engineering, applied machine learning, data visualization, and data ethics. 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), a master's in Information and Cybersecurity (MICS), and a Ph.D in Information Science.



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 has completed a basic degree 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 on a 9-point scale (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 the 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. Unofficial transcripts must contain specific information including the name of the applicant, name of the school, all courses, grades, units, & degree conferral (if applicable). 
  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, by the recommender, not the Graduate Admissions.
  3. Evidence of English language proficiency: All applicants who have completed a basic degree from a country or political entity in which the official language is not English are required to submit official evidence of English language proficiency. This applies to institutions 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.

Applicants who have previously applied to Berkeley must also submit new test scores that meet the current minimum requirement from one of the standardized tests. Official TOEFL score reports must be sent directly from Educational Test Services (ETS). The institution code for Berkeley is 4833 for Graduate Organizations. Official IELTS score reports must be sent electronically from the testing center to University of California, Berkeley, Graduate Division, Sproul Hall, Rm 318 MC 5900, Berkeley, CA 94720. TOEFL and IELTS score reports are only valid for two years prior to beginning the graduate program at UC Berkeley. Note: score reports can not expire before the month of June.


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, work experience, essays, letters of recommendation, and goals that are a good fit for the program.

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.
  • A high level of quantitative ability as conveyed by significant work experience that demonstrates your quantitative abilities and/or academic coursework that demonstrates quantitative aptitude
  • 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.
  • Proficiency in programming languages, such as Python or Java, demonstrated by prior work experience or advanced coursework. Applicants who lack this experience in their academic or work background but meet all other admission requirements will be required to take the Introduction to Data Science Programming course in their first term.
  • The ability to communicate effectively, as demonstrated by academic performance, professional experience, and/or strong essays that demonstrate effective communication skills.
  • Not RequiredOfficial Graduate Record Examination (GRE) General Test or Graduate Management Admission Test (GMAT) scores. As of Fall 2020, we have eliminated the GRE/GMAT requirement. We recommend you put your time and effort towards the required application materials.
  • 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.

Note: Admission to the Fifth Year Master of Information and Data Science program requires that the applicant complete their undergraduate education at UC Berkeley in the year prior to starting the program. Consequently, applicants are not required to submit TOEFL scores. However, applicants are required to submit three letters of recommendation and additional short answer essays.

For more information and application instructions, prospective MIDS students should visit the datascience@berkeley Admissions Overview and prospective 5th Year MIDS students should visit the I School Admissions page.

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). Students also complete an Immersion Program.

The unit and coursework requirements for the Fifth Year Master of Information and Data Science are identical, although fifth-year students are also encouraged and supported to seek internships to complement and encourage professional development.


Foundation Courses
DATASCI 200Introduction to Data Science Programming3
DATASCI 201Research Design and Applications for Data and Analysis3
DATASCI 201AResearch Design and Applications for Data and Analysis for Early Career Data Scientists4
DATASCI 203Statistics for Data Science3
DATASCI 205Fundamentals of Data Engineering3
DATASCI 207Applied Machine Learning3
Advanced Courses
DATASCI 209Data Visualization3
DATASCI 231Behind the Data: Humans and Values3
DATASCI 233Privacy Engineering3
DATASCI 241Experiments and Causal Inference3
DATASCI 251Deep Learning in the Cloud and at the Edge3
DATASCI 255Machine Learning Systems Engineering3
DATASCI 261Machine Learning at Scale3
DATASCI 266Natural Language Processing with Deep Learning3
DATASCI 271Statistical Methods for Discrete Response, Time Series, and Panel Data3
DATASCI 281Computer Vision3
DATASCI 290Special Topics3
Capstone Course
DATASCI 210Capstone3


The Immersion Program provides MIDS students with the opportunity to meet faculty and peers in person. Students 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 three times a year, each three- to four-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.


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

Information and Data Science

Faculty and Instructors


Morgan Ames, Assistant Adjunct Professor. Science and technology studies; computer-supported cooperative work and social computing; education; anthropology; youth technocultures; ideology and inequity; critical data science. .

Daniel Gillick, Assistant Adjunct Professor. Natural Language Processing, machine learning, artificial intelligence, statistics, speech recognition.

Douglas Alex Hughes, Assistant Adjunct Professor. Experiments and Causal Identification, Social Networks, Political Behavior and Outcomes.

Paul Laskowski, Assistant Adjunct Professor. Information economics, telecommunications policy, network architecture, innovation.

Deirdre Mulligan, Professor. Privacy, fairness, human rights, cybersecurity, technology and governance, values in design.
Research Profile

David H. Reiley, Adjunct Professor. Field experiments, advertising, auctions and other pricing mechanisms, charitable fundraising, and electronic commerce.

Michael Rivera, Assistant Adjunct Professor. Research design, political science, voting and political behavior, technology and politics, civic participation and social media. .

Steven Weber, Professor. Political science, international security, international political economy, information science.
Research Profile


Zachary Alexander, Lecturer.

Brooks Ambrose, Postdoctoral Scholar and Lecturer.

Fereshteh Amini, Lecturer.

Daniel Aranki, Lecturer.

Sahab Aslam, Lecturer.

Prabhakar Attaluri, Lecturer.

Gerald Benoît, Lecturer.

Amit Bhattacharyya, Lecturer.

Katayoun Borojerdi, Lecturer.

Clinton Brownley, Lecturer.

Mark Butler, Lecturer.

Felipe Campos, Lecturer.

Daniel Cer, Lecturer.

Shiraz Chakraverty, Lecturer.

Kevin Crook, Lecturer.

Kim Darnell, Lecturer.

Vinicio De Sola, Lecturer.

Ryan DeJana, Lecturer.

Brad DesAulniers, Lecturer.

John-Peter Dolphin, Lecturer.

Micah Gell-Redman, Lecturer.

Nathan Stanley Good, Lecturer.

Peter Grabowski, Lecturer.

Annette Greiner, Lecturer.

Scott Guenther, Postdoctoral Scholar and Lecturer.

John Alexis Guerra Gómez, Lecturer.

Ramki Gummadi, Lecturer.

Kyle Hamilton, Lecturer.

Darragh Hanley, Lecturer.

Conor Healy, Lecturer.

Daniel Hedblom, Postdoctoral Scholar and Lecturer.

Todd Michael Holloway, Lecturer.

Tracy Wei Huang, Lecturer.

Richard Huntsinger, Lecturer.

Cornelia Ilin, Postdoctoral Scholar and Lecturer.

Corey Jackson, Postdoctoral Scholar and Lecturer.

Gerard Kelly, Lecturer.

Stanislav Kelman, Lecturer.

Gunnar Kleemann, Lecturer.

Jari Koister, Lecturer.

Zona Kostic, Lecturer.

James Kunz, Lecturer.

Bum Chol Kwon, Lecturer.

Mark Labovitz, Lecturer.

Christopher Llop, Lecturer.

Sushovan Majhi, Postdoctoral Scholar and Lecturer.

Alex Marrs, Lecturer.

Taylor Martin, Lecturer.

Jared Maslin, Lecturer.

Mark Mims, Lecturer.

Lee Moore, Lecturer.

Emanuel Moss, Lecturer.

Esteban Arias Navarro, Lecturer.

Majid Maki Nayeri, Postdoctoral Scholar and Lecturer.

Fred Nugen, Lecturer.

Fred Nugen, Lecturer.

Eric Penner, Postdoctoral Scholar and Lecturer.

Elena Petrov, Lecturer.

Michael Pritchard, Lecturer.

Joachim Rahmfeld, Lecturer.

Andy Reagan, Lecturer.

Sid J Reddy, Lecturer.

Dmitry Rekesh, Lecturer.

Carlos Rivera, Postdoctoral Scholar and Lecturer.

Mahnaz Roshanaei, Postdoctoral Scholar and Lecturer.

Yacov Salomon, Lecturer.

Doris Schioberg, Lecturer.

Uri Schonfeld, Lecturer.

James Shanahan, Lecturer.

Joyce Shen, Lecturer.

Neha Singh, Lecturer.

Paul Spiegelhalter, Lecturer.

David Steier, Lecturer.

Michael Tamir, Lecturer.

Alberto Todeschini, Lecturer.

Puya H. Vahabi, Lecturer.

Luis Villarreal, Lecturer.

Jeff Yau, Lecturer.

James York-Winegar, Lecturer.

Todd Young, Lecturer.

Anna Zaitsev, Postdoctoral Scholar and Lecturer.

Contact Information

School of Information

102 South Hall

Phone: 510-642-1464


Senior Director of Student Affairs

Siu Yung Wong


Phone: 855-678-MIDS

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