The School of Information is UC Berkeley’s newest professional school. Located in the center of campus, the I School is a graduate research and education community committed to expanding access to information and to improving its usability, reliability, and credibility while preserving security and privacy.
The School of Information offers four degrees:
The Master of Information Management and Systems (MIMS) program educates information professionals to provide leadership for an information-driven world.
The Master of Information and Data Science (MIDS) is an online degree preparing data science professionals to solve real-world problems. The 5th Year MIDS program is a streamlined path to a MIDS degree for Cal undergraduates.
The Master of Information and Cybersecurity (MICS) is an online degree preparing cybersecurity leaders for complex cybersecurity challenges.
Our Ph.D. in Information Science is a research program for next-generation scholars of the information age.
The School of Information's courses bridge the disciplines of information and computer science, design, social sciences, management, law, and policy. We welcome interest in our graduate-level Information classes from current UC Berkeley graduate and undergraduate students and community members. More information about signing up for classes.
Research by faculty members and doctoral students keeps the I School on the vanguard of contemporary information needs and solutions.
The I School is also home to several active centers and labs, including the Center for Long-Term Cybersecurity (CLTC), the Center for Technology, Society & Policy, and the BioSENSE Lab.
I School graduate students and alumni have expertise in data science, user experience design & research, product management, engineering, information policy, cybersecurity, and more — learn more about hiring I School students and alumni.
In an effort to support public health officials in resource allocation and intervention development for targeted areas at high risk for the spread of COVID-19, we develop linear regression machine learning model that uses twitter data to predict county-level rates of expected foot traffic in public spaces.
