The UC Berkeley School of Information (I School) offers a professional Master of Information and Data Science (MIDS) delivered online.

The MIDS program is designed to train leaders in the ever-evolving field of data science. Our program focuses on problem solving, preparing you to creatively apply methods of data collection, analysis, and presentation to solve the world's most challenging problems.

Designed by I School faculty, our curriculum is multidisciplinary. You will bring together a range of methods to define a research question; to gather, store, retrieve, and analyze data; to interpret results; and to convey findings effectively. Using the latest tools and practices, you will identify patterns in and gain insights from complex data sets.

Earn Your Degree From Anywhere

Located in the San Francisco Bay Area, at the heart of the big data revolution, UC Berkeley remains at the forefront of the disciplines that make up the core of data science — from databases, statistics, and machine learning to data visualization, privacy, and security. The I School's recognized strengths in research design and information policy ensure a well-rounded and rigorous curriculum.

datascience@berkeley brings all the advantages of earning a UC Berkeley degree to you — and there's no need to relocate. With this online program, you can attend classes from anywhere in the world, so you will not have to sacrifice your personal or professional commitments.


The Master of Information and Data Science program is fully accredited by the Western Association of Schools and Colleges (WASC). WASC accreditation speaks to the quality of the program, faculty, and the Berkeley I School. It ensures that the program will prepare you for a career in data science.

Application Requirements

To complete your application, you must submit the following:

  • Online application
  • Official transcripts from all educational institutions attended from your undergraduate degree on
  • Official Graduate Record Examination (GRE) or Graduate Management Admission Test (GMAT) scores
  • Statement of Purpose and additional admissions statements
  • Two professional letters of recommendation
  • A working knowledge of fundamental computer science concepts including: data structures, algorithms and analysis of algorithms, and linear algebra
  • Current resume
  • TOEFL Scores (if applicable)
  • Application fee of $80 for domestic applicants or $100 for international applicants

Below is a sample course schedule and the expected path through the degree program. Students who are interested in taking the program on an accelerated basis can complete their coursework in 3 or 4 terms with approval from the School by taking up to 3 courses in one or more terms.

Term 1

Research Design and Application for Data and Analysis

This course introduces students to the burgeoning data sciences landscape, with a particular focus on learning how to apply data science reasoning techniques to uncover, enrich, and answer questions facing decision makers across a variety of industries and organizations today. After an introduction to data science and an overview of the program, students will explore how individuals and organizations assess options, make decisions, and probe the emerging role of big data in guiding both tactical and strategic decisions. Lectures, readings, discussions, and assignments will teach students how to apply disciplined, creative methods in order to ask better questions, efficiently gather data, interpret results, and convey findings to various audiences in order to change minds and behaviors. The emphasis throughout is on making practical contributions to decisions that organizations will and should make. Industries explored include sports management, finance, energy, journalism, intelligence, healthcare, and media/entertainment.

Exploring and Analyzing Data

The goal of this course is to provide students with an introduction to many different types of quantitative research methods and statistical techniques for analyzing data. We begin with a focus on measurement, inferential statistics, and causal inference. Then, we will explore a range of statistical techniques and methods using the open-source statistics language, R. We will use many different statistics and techniques for analyzing and viewing data, with a focus on applying this knowledge to real-world data problems. Topics in quantitative techniques include: descriptive and inferential statistics, sampling, experimental design, parametric and non-parametric tests of difference, ordinary least squares regression, and logistic regression.

Term 2

Storing and Retrieving Data

Storing, managing, and processing datasets are foundational to both applied computer science and data science. Indeed, successful deployment of data science in any organization is closely tied to how data is stored and processed. This course introduces the fundamentals of data storage, retrieval, and processing systems in the context of common data analytics processing needs. As these fundamentals are introduced, representative technologies will be used to illustrate how to construct storage and processing architectures. This course aims to provide a set of “building blocks” by which one can construct a complete architecture for storing and processing data. The course will examine how technical architectures vary depending on the problem to be solved and the reliability and freshness of the result.

The course considers the complete breadth of technology choices. The content spans from traditional databases and business warehouse architectures, so-called big-data architectures, to streaming analytics solutions and graph processing. Students will consider both small and large datasets because both are equally important and both justify different trade-offs. Exercises and examples will consider both simple and complex data structures, as well as data that is both clean and structured and dirty and unstructured.

Term 3

Applied Machine Learning

Machine learning is a rapidly growing field at the intersection of computer science and statistics that is concerned with finding patterns in data. It is responsible for tremendous advances in technology, from personalized product recommendations to speech recognition in cell phones. The goal of this course is to provide 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. Through a variety of lecture examples and programming projects, students will learn how to apply powerful machine learning techniques to new problems, run evaluations and interpret results, and think about scaling up from thousands of data points to billions.

Term 4

Data Visualization and Communication

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 students hands-on experience with designing and building data visualizations as well as reporting their findings in prose.

Term 5

Synthetic Capstone

In this capstone class, students will combine technical, analytic, interpretive, and social dimensions to design and execute a full data science project, developing their skills as data scientists with a focus on real-world applications and situations. The final project provides an opportunity to integrate all of the core skills and concepts learned throughout the program, and prepares students for long-term professional success in the field. It provides experience in formulating and carrying out a sustained, coherent, and influential course of work resulting in a tangible data science project using real-world data. Students are evaluated on their ability to collaboratively develop and communicate their work in both written and oral form. The capstone is completed as a group project, and each project will focus on open, pre-existing, secondary data.

The capstone is completed as a group/team project (3-4 students), and each project will focus on open, pre-existing secondary data. A robust listing of open datasets will be made available before the capstone course begins.

Tuition and Financial Aid

For MIDS students starting the program in academic year 2016-2017 (July 2016 – June 2017), tuition will be $2,333* per unit, plus a $537.25* semester fee. Tuition is charged per unit; datascience@berkeley is a 27 unit program. Please note that MIDS fees are subject to change at the start of each academic year and you should expect them to rise each year of the datascience@berkeley program.

Tuition includes student fees, technology platform licensing, and support. The cost of your hotel and most meals for your 3-4 day, on-campus immersion is included in your tuition. Airfare is not included.

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 for the Master of Information and Data Science program, you must meet the following requirements:

A bachelor's degree

You should have a superior scholastic record — normally well above a 3.0 GPA. The recognized equivalent to a bachelor's degree is also accepted, if earned from an accredited institution.

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