UC Berkeley's new Bachelor of Arts in Data Science, designed in collaboration with a host of faculty from across the University, will empower students with deep technical knowledge, expertise in how to apply that knowledge in a field of their choosing, and an understanding of the social and human contexts and ethical implications of how data are collected, analyzed and used. This combination will position graduates to help inform and develop solutions to a range of pressing challenges, from adapting industry to a new world of data, to amplifying learning in education, to helping communities recover from disaster.
The Data Science Major comes in response to intensifying student, faculty, business, and societal demand amid the exponential growth of data in virtually all aspects of life. This transformation is generating a substantial unmet need for graduates who are not only technically proficient in analyzing data but who also know how to responsibly collect and manage data, and use data to make decisions and discoveries, think critically, and communicate effectively.
Data Science combines computational and inferential reasoning to draw conclusions based on data about some aspect of the real world. Data scientists come from all walks of life, all areas of study, and all backgrounds. They share an appreciation for the practical use of mathematical and scientific thinking and the power of computing to understand and solve problems for business, research, and societal impact.
The Data Science Major will equip students to draw sound conclusions from data in context, using knowledge of statistical inference, computational processes, data management strategies, domain knowledge, and theory. Students will learn to carry out analyses of data through the full cycle of the investigative process in scientific and practical contexts. Students will gain understanding of the human and ethical implications of data analysis and integrate that knowledge in designing and carrying out their work.
Description of the Undergraduate Major
- Foundations in Mathematics and Computing
- Computational and Inferential Depth
- Modeling, Learning and Decision Making
- Domain Emphasis
- Human Contexts and Ethics