The UC Berkeley College of Computing, Data Science, and Society (CDSS) brings together programs, schools, departments and partners to create accessible and equitable educational opportunities and catalyze groundbreaking research that meets society's greatest challenges.
Each semester, you will take:
- Required course: Computing, Data Science, and the Future Workplace (CDSS X410)
- One core course: Principles and Techniques of Data Science (DATA C100) or Modern Statistical Prediction and Machine Learning (STAT 154)
- Two electives:
- One CDSS elective: An upper-division course within Statistics, Data Science, Computer Science, or select courses in Electrical Engineering and Computer Science (EECS).
- One Berkeley elective: A course from the broader UC Berkeley catalog (including additional CDSS courses).
All courses are subject to change at any time.
Program Prerequisites:
- Knowledge of key concepts in statistics and data science, including parameter estimation, hypothesis testing, maximum likelihood estimation, linear regression, tabular data and visualization
- Background and experience in program structures
- Multivariable calculus, linear algebra and differential equations
- Experience with some programming language
Required Course
Computing, Data Science, and the Future Workplace
CDSS X410 (1 unit)
This course prepares you to navigate—and lead in—an AI-augmented economy. You'll learn how technological change is reshaping jobs, skills and organizations and examine how artificial intelligence and machine learning are reshaping the future of work. Gain insights into what global employers expect from the next generation of data-driven professionals. Meet with leading companies, venture capital firms, and startups on site visits facilitated by the course instructors.
Core Courses
Principles and Techniques of Data Science
DATA C100 (4 units)
Explore the data science lifecycle, including question formulation, data collection and cleaning, exploratory data analysis and visualization, statistical inference and prediction, and decision-making. Focus on quantitative critical thinking and key principles and techniques needed to carry out this cycle. These include languages for transforming, querying and analyzing data; algorithms for machine learning methods, including regression, classification and clustering; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction; and techniques for scalable data processing.
Modern Statistical Prediction and Machine Learning
STAT 154 (4 units)
Study theory and practice of statistical prediction, as well as contemporary methods as extensions of classical methods. Topics include: optimal prediction rules, the curse of dimensionality, empirical risk, linear regression and classification, basis expansions, regularization, splines, the bootstrap, model selection, classification and regression trees, boosting and support vector machines. Understand computational efficiency versus predictive performance. Emphasis is placed on experience with real data and assessing statistical assumptions. This course uses Python as its primary computing language.
Electives—Shape Your Learning Path
Choose up to 2 elective courses.
While your core coursework provides foundational technical skills, your two electives allow you to dive deeper into the methods and applications that align with your career goals. Our academic advisers will work closely with you to navigate the catalog and select the courses that best fit your background and aspirations.
- Technical Depth (CDSS): Select one elective from a curated selection of upper-division courses in Statistics, Data Science, Computer Science, or Electrical Engineering and Computer Science (EECS). Tailor your study toward specialized areas such as causal inference, time series analysis, ethics of data or deep learning.
- Campus Breadth: Select a second elective from the broader UC Berkeley catalog to explore how data science intersects with other fields. You may also choose to take a second approved elective from the CDSS classes listed below.
The Berkeley Experience
You'll study alongside Berkeley undergraduates in classes taught by leading faculty, engaging directly with the data and computational challenges driving today's fields. Explore possible courses:
- Data Science classes
- Statistics classes
- Computer Science classes
- Electrical Engineering and Computer Science classes
Note on course selection: To ensure academic success, elective choices are subject to adviser approval and may depend on prerequisites and space availability. Your adviser will provide you with the most current list of eligible courses for your semester.
Transfer Courses!
Earn units that can transfer to your home university.
Units earned in this program can be transferred back to your home university, or help prepare you for graduate and professional schools. Learn more about transfer credits.
Earn a Certificate
All students must:
- Earn a minimum of 12 UC Berkeley units to fulfill program requirements and receive the certificate.
- Earn a final program GPA of 3.0 or higher based on all courses taken at UC Berkeley. P/NP courses will not count toward your final GPA.