Berkeley Master of Information and Data Science

Berkeley Master of Information and Data Science
Name	Master of Information and Data Science
Institution	University of California, Berkeley
School	School of Information
Established	2016
Degree	Master of Information and Data Science (MIDS)
Format	Online
Duration	20 months
Location	Berkeley, California

Contents

Berkeley Master of Information and Data Science is a professional graduate program administered by the School of Information at the University of California, Berkeley. The program integrates instruction from faculty affiliated with Princeton University, Massachusetts Institute of Technology, Stanford University, Harvard University, and industry partners such as Google, IBM, Facebook, and Amazon. MIDS emphasizes applied data science techniques, computational methods, statistics, ethics, and domain-specific applications relevant to organizations like NASA, Centers for Disease Control and Prevention, World Bank, and United Nations.

Overview

The program is delivered through an online platform developed in collaboration with instructional designers who have worked with Coursera, edX, LinkedIn Learning, and Udacity, and supported by technical infrastructure similar to projects from Lawrence Berkeley National Laboratory and Argonne National Laboratory. MIDS students engage with case studies drawn from McKinsey & Company, Deloitte, Accenture, Goldman Sachs, JPMorgan Chase, and public-sector partners such as California Department of Public Health and City of New York. Cohorts include professionals who previously held positions at Microsoft, Apple Inc., Intel Corporation, Cisco Systems, Oracle Corporation, Uber Technologies, Lyft, Inc., Airbnb, Salesforce, and Spotify.

Core coursework covers topics taught in conjunction with faculty connected to Department of Statistics, University of California, Berkeley, Electrical Engineering and Computer Sciences, School of Information, and guest lecturers from Harvard Business School, Kellogg School of Management, Wharton School, and Sloan School of Management. Modules include instruction on algorithms referencing research from Alan Turing Institute, machine learning reflecting methods from DeepMind, OpenAI, and Google DeepMind, and causal inference influenced by scholarship at London School of Economics, Yale University, and Columbia University. Students may pursue applied projects with partners such as Genentech, Pfizer, Moderna, Johns Hopkins University, Mayo Clinic, and Sutter Health.

Elective specializations and capstone options have connected themes—natural language processing with links to work at Stanford NLP Group and Allen Institute for AI, computer vision drawing on research from Carnegie Mellon University and Oxford University, and data engineering reflecting tooling from Apache Software Foundation, Hadoop, Spark, and Kubernetes. Ethics and policy seminars reference frameworks from European Commission, National Institute of Standards and Technology, and World Economic Forum.

Admissions consider academic records from institutions such as University of California, Berkeley, University of Cambridge, University of Oxford, Princeton University, Massachusetts Institute of Technology, University of Chicago, and professional experience at firms like McKinsey & Company and Goldman Sachs. Applicants submit materials similar to processes at Common Application and fellowship programs like Rhodes Scholarship and Fulbright Program, with standardized test considerations paralleling policies at Graduate Record Examination-accepting universities. Financial aid options include loans available through programs modeled after Federal Direct Loan Program and scholarships inspired by awards from Gates Foundation, Rockefeller Foundation, Ford Foundation, and internal fellowships comparable to those at Haas School of Business.

Faculty teaching and advising are drawn from scholars affiliated with School of Information, Department of Electrical Engineering and Computer Sciences, UC Berkeley, and cross-listed researchers with appointments at Berkeley Lab and visiting positions from Princeton University, Columbia University, New York University, University of Washington, and University of Toronto. Research areas span machine learning traditions from Geoffrey Hinton-influenced networks, reinforcement learning consistent with methods from Richard Sutton, and statistical modeling in the lineage of Bradley Efron and Donald Rubin. Collaborative projects have involved grants or partnerships with National Science Foundation, National Institutes of Health, Defense Advanced Research Projects Agency, and corporations such as Intel Corporation and NVIDIA.

Graduates have transitioned into roles at organizations like Google, Facebook, Amazon, Apple Inc., Microsoft, Netflix, Airbnb, Uber Technologies, Capital One, Goldman Sachs, Bloomberg L.P., Palantir Technologies, Snowflake (company), and research positions at Lawrence Berkeley National Laboratory and Los Alamos National Laboratory. Alumni pursue titles such as data scientist (roles historically held at LinkedIn), machine learning engineer (roles at DeepMind), data engineer (roles at Cloudera), and product manager (roles at Google and Amazon Web Services). Career services mirror practices from Career Services (UC Berkeley), with employer recruiting similar to processes at Stanford University and Harvard University.

The degree launched amid a growth phase for online professional masters, contemporaneous with programs at Georgia Institute of Technology, Columbia University, and Northwestern University. Development involved collaboration with instructional partners and drew on historical precedents from digital learning initiatives at Massachusetts Institute of Technology and Stanford Online. Program evolution included curriculum updates reflecting breakthroughs announced at conferences like NeurIPS 2016 and policy discussions at United Nations General Assembly and European Parliament regarding data governance.

Category:University of California, Berkeley academic programs