Information and Data Science: MIDS

University of California, Berkeley

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).

Visit School Website

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

Foundation Courses
DATASCI W200Python Fundamentals for Data Science3
DATASCI W201Research Design and Applications for Data and Analysis3
DATASCI W203Statistics for Data Science3
DATASCI W205Storing and Retrieving Data3
DATASCI W207Applied Machine Learning3
DATASCI W209Data Visualization and Communication3
Advanced Courses
DATASCI W231Behind the Data: Humans and Values3
DATASCI W241Experiments and Causal Inference3
DATASCI W251Scaling Up! Really Big Data3
DATASCI W261Machine Learning at Scale3
DATASCI W266Natural Language Processing with Deep Learning3
DATASCI W271Statistical Methods for Discrete Response, Time Series, and Panel Data3
Capstone Course
DATASCI W210Capstone3

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

DATASCI W200 Python Fundamentals for Data Science 3 Units

Offered through: Information
Terms offered: Spring 2018, Fall 2017, Summer 2017
This fast-paced course gives students fundamental Python knowledge necessary for advanced work in data science. Students gain frequent practice writing code, building to advanced skills focused on data science applications. We introduce a range of Python objects and control structures, then build on these with classes on object-oriented programming. A major programming project reinforces these concepts, giving
students insight into how a large piece of software is built and experience managing a full-cycle development project. The last section covers two popular Python packages for data analysis, Numpy and Pandas, and includes an exploratory data analysis.
Python Fundamentals for Data Science: Read More [+]

DATASCI W201 Research Design and Applications for Data and Analysis 3 Units

Offered through: Information
Terms offered: Spring 2018, Fall 2017, Summer 2017
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 [+]

DATASCI W203 Statistics for Data Science 3 Units

Offered through: Information
Terms offered: Spring 2018, Fall 2017, Summer 2017
This course provides students with a foundational understanding of classical statistics within the broader context of data science. Topics include exploratory analysis and descriptive statistics, probability theory and the foundations of statistical modeling, estimators, hypothesis testing, and classical linear regression. Causal inference and reproducibility issues are treated briefly. Students will learn
to apply the most common statistical procedures correctly, checking assumptions and responding appropriately when they appear violated; to evaluate the design of a study and how the variables being measured relate to research questions; and to analyze real-world data using the open-source language R.
Statistics for Data Science: Read More [+]

DATASCI W205 Storing and Retrieving Data 3 Units

Offered through: Information
Terms offered: Spring 2018, Fall 2017, Summer 2017
Data Science depends on data, and a core competency mandated by this reliance on data is knowing effective and efficient ways to manage, search and compute over that data. This course is focused on how data can be stored, managed and retrieved as needed for use in analysis or operations. The goal of this course is provide students with both theoretical knowledge and practical experience leading to mastery of
data management, storage and retrieval with very large-scale data sets. Course must be taken for a letter grade to fulfill degree requirements.
Storing and Retrieving Data: Read More [+]

DATASCI W207 Applied Machine Learning 3 Units

Offered through: Information
Terms offered: Spring 2018, Fall 2017, Summer 2017
Machine learning is a rapidly growing field at the intersection of computer science and statistics concerned with finding patterns in data. It is responsible for tremendous advances in technology, from personalized product recommendations to speech recognition in cell phones. This course provides a broad introduction to the key ideas in machine learning. The emphasis will be on intuition and practical examples
rather than theoretical results, though some experience with probability, statistics, and linear algebra will be important. Course must be taken for a letter grade to fulfill degree requirements.
Applied Machine Learning: Read More [+]

DATASCI W209 Data Visualization and Communication 3 Units

Offered through: Information
Terms offered: Spring 2018, Fall 2017, Summer 2017
Communicating clearly and effectively about the patterns we find in data is a key skill for a successful data scientist. This course focuses on the design and implementation of complementary visual and verbal representations of patterns and analyses in order to convey findings, answer questions, drive decisions, and provide persuasive evidence supported by data. Assignments will give hands-on experience designing
data graphics and visualizations, and reporting findings in prose. Course must be taken for a letter grade to fulfill degree requirements.
Data Visualization and Communication: Read More [+]

DATASCI W210 Capstone 3 Units

Offered through: Information
Terms offered: Spring 2018, Fall 2017, Summer 2017
The capstone course will cement skills learned throughout the MIDS program – both core data science skills and “soft skills” like problem-solving, communication, influencing, and management – preparing students for success in the field. The centerpiece is a semester-long group project in which teams of students propose and select project ideas, conduct and communicate their work, receive and provide feedback
(in informal group discussions as well as formal class presentations), and deliver compelling presentations along with a Web-based final deliverable. Includes relevant readings, case discussions, and real-world examples and perspectives from panel discussions with leading data science experts and industry practitioners.
Capstone: Read More [+]

DATASCI W231 Behind the Data: Humans and Values 3 Units

Offered through: Information
Terms offered: Spring 2018, Fall 2017, Summer 2017
Intro to the legal, policy, and ethical implications of data, including privacy, surveillance, security, classification, discrimination, decisional-autonomy, and duties to warn or act. Examines legal, policy, and ethical issues throughout the full data-science life cycle — collection, storage, processing, analysis, and use — with case studies from criminal justice, national security, health, marketing, politics
, education, employment, athletics, and development. Includes legal and policy constraints and considerations for specific domains and data-types, collection methods, and institutions; technical, legal, and market approaches to mitigating and managing concerns; and the strengths and benefits of competing and complementary approaches.
Behind the Data: Humans and Values: Read More [+]

DATASCI W241 Experiments and Causal Inference 3 Units

Offered through: Information
Terms offered: Spring 2018, Fall 2017, Summer 2017
This course introduces students to experimentation in the social sciences. 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 have facilitated the development of better data gathering. Key to this area of inquiry is
the insight that correlation
does not necessarily imply causality. In this course, we learn how to
use experiments to establish causal effects and how to be appropriately skeptical of findings
from observational data.

Experiments and Causal Inference: Read More [+]

DATASCI W251 Scaling Up! Really Big Data 3 Units

Offered through: Information
Terms offered: Spring 2018, Fall 2017, Summer 2017
An overview of the contemporary toolkits for problems related to cloud computing and big data. Because the class is an advanced course, we generally assume familiarity with the concepts and spend more time on the implementation. Every lecture is followed by a hands-on assignment, where students get to experience some of the technologies covered in the lecture. By the time students complete the course, they
should be able to name the big data problem they are facing, select proper tooling, and know enough to start applying it.
Scaling Up! Really Big Data: Read More [+]

DATASCI W261 Machine Learning at Scale 3 Units

Offered through: Information
Terms offered: Spring 2018, Fall 2017, Summer 2017
This course teaches the underlying principles required to develop scalable machine learning pipelines for structured and unstructured data at the petabyte scale. Students will gain hands-on experience in Apache Hadoop and Apache Spark.

Machine Learning at Scale: Read More [+]

DATASCI W266 Natural Language Processing with Deep Learning 3 Units

Offered through: Information
Terms offered: Spring 2018, Fall 2017, Summer 2017
Understanding language is fundamental to human interaction. Our brains have evolved language-specific circuitry that helps us learn it very quickly; however, this also means that we have great difficulty explaining how exactly meaning arises from sounds and symbols. This course is a broad introduction to linguistic phenomena and our attempts to analyze them with machine learning. We will cover a wide range
of concepts with a focus on practical applications such as information extraction, machine translation, sentiment analysis, and summarization.
Natural Language Processing with Deep Learning: Read More [+]

DATASCI W271 Statistical Methods for Discrete Response, Time Series, and Panel Data 3 Units

Offered through: Information
Terms offered: Spring 2018, Fall 2017, Summer 2017
A continuation of Data Science W203 (Exploring and Analyzing Data), this course trains data science students to apply more advanced methods from regression analysis and time series models. Central topics include linear regression, causal inference, identification strategies, and a wide-range of time series models that are frequently used by industry professionals. Throughout the course, we emphasize choosing
, applying, and implementing statistical techniques to capture key patterns and generate insight from data. Students who successfully complete this course will be able to distinguish between appropriate and inappropriate techniques given the problem under consideration, the data available, and the given timeframe.
Statistical Methods for Discrete Response, Time Series, and Panel Data: Read More [+]

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.
Research Profile

Paul Duguid, Adjunct Professor. Trademark, information, communities of practice.
Research Profile

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.
Research Profile

Ray Larson, Professor. Information Retrieval system design and evaluation, database management.
Research Profile

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.
Research Profile

Doug Tygar, Professor. Privacy, technology policy, computer security, electronic commerce, software engineering, reliable systems, embedded systems, computer networks, cryptography, cryptology, authentication, ad hoc networks.
Research Profile

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

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

Contact Information

School of Information

102 South Hall

Phone: 510-642-1464

Visit School Website

Dean

AnnaLee Saxenian, PhD

102 South Hall

Phone: 510-642-9980

dean@ischool.berkeley.edu

Siu Yung Wong

Director of Student Affairs

510-664-7092

siu@ischool.berkeley.edu

Admissions

Phone: 855-678-MIDS

admissions@datascience.berkeley.edu

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