加州大學(xué)-伯克利

The Data Science Major degree program 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 an understanding of the human and ethical implications of data analytics and integrate that knowledge in designing and carrying out their work.

The Data Science major requirements include "Data 8" (STAT C8 ) and "Data 100" (COMPSCI C100), the core lower-division and upper-division elements of the major, along with courses from each of the following requirement groups:

• Foundations in Mathematics and Computing
• Computational and Inferential Depth
• Modeling, Learning and Decision Making
• Probability
• Human Contexts and Ethics
• Domain Emphasis

All students will select a Domain Emphasis, a cluster of one lower division course and two upper division courses, that brings them into the context of a domain and allows them to build bridges with data science.

Domain Emphasis

Domain Emphases give students a grounded understanding of a particular domain of data-intensive research, relevant theory, or an integrative intellectual thread. A Domain Emphasis is comprised of three courses chosen from a list. Each Domain Emphasis is rooted in a lower division course, which is typically also a prerequisite for the upper division courses.

A Domain Emphasis is not limited to courses that are intended to be specifically for data science. Rather, they should bring the data science student into the context of a domain. That may involve understanding the vocabulary, methods of study, theoretical foundations, or cultural outlook of the domain. The student needs to become able to build the bridges with data science in carrying out the emphasis, rather than expecting each course to do it for them.