The number 2–ranked Master of Information and Data Science (MIDS) program,* delivered online, prepares students to be leaders in the data science field.
The online master’s program brings UC Berkeley to students, wherever they are. The WASC-accredited program blends a multidisciplinary curriculum, experienced faculty from UC Berkeley and top data-driven companies, an accomplished network of peers, and the flexibility of online learning.
The multidisciplinary online data science master’s curriculum draws upon computer science, social sciences, statistics, management, and law. Students use the latest tools and analytical methods to work with data at scale, derive insights from complex and unstructured data, and solve real-world problems.
The core curriculum focuses on the following key skills:
Experiential, project-based learning is a hallmark of the MIDS program. Students work collaboratively with the latest tools, environments, and processes on real-world data science problems, so they are prepared to work as leaders in the industry.
The online master’s in data science combines advanced technology and in-person experiences to ensure you benefit from the full UC Berkeley School of Information (I School) experience.
Find all of the online tools you need to succeed in one place: the virtual campus.
The 27-unit online program is designed for the working professional and can be completed at a flexible pace.
The MIDS curriculum features a wide range of courses that provide students with a comprehensive understanding of how data science can be used to inform decision-making in their organizations. Students will complete programming-focused courses, like the featured courses below, in concurrence with courses that focus on the ethical impact of data science and how to effectively communicate results.
Students will learn how to apply crucial machine learning techniques to solve problems, run evaluations and interpret results, and understand scaling up from thousands of data points to billions.
This course examines the legal, policy, and ethical issues that arise throughout the full life cycle of data science. Students use case studies to explore these issues across various domains, such as criminal justice, national security, health, marketing, politics, education, and employment.
This course is a broad introduction to linguistic phenomena and our attempts to analyze them with machine learning. The course covers a wide range of concepts, with a focus on practical applications such as information extraction, machine translation, sentiment analysis, and summarization.