Berkeley’s Data Science education program aims at a comprehensive curriculum built from the entry level upward to meet students’ varied needs for data fluency. It includes a diverse constellation of connector courses that allow students to explore real-world issues related to their areas of interest, and continues with intermediate and advanced courses that enable them to apply more complex concepts and approaches. The constellation of Berkeley’s data science course offerings is broad and deep, and includes a wide range of courses in a variety of departments across campus.

Foundations of Data Science (Data 8)

Foundations of Data Science, or Data 8, is Berkeley’s pathbreaking lower-division course that teaches core computational and statistics concepts while enabling students to work hands-on with real data. It is designed to be accessible to undergraduates of any intended major, and does not require prior experience in the field.

Connectors

Connector courses enable students to apply core concepts from the Foundations course to explore real-world issues that relate to students’ areas of interest. Offered by faculty across many departments and fields of study, connectors are optional but highly encouraged and are designed to be taken at the same or after the Foundations course.  They typically (but not always) offer students two units of course credit.

Examples:

Extenders

Extenders are courses that take Foundations of Data Science or connector courses as a starting point for further work in data science. Instructors of these courses explicitly build upon the concepts and skills developed in the entry-level data science curriculum. Some extenders require only Foundations of Data Science  or a specific connector as a prerequisite, while others may require calculus or other prerequisites.  

Examples include:

Closely Related Courses

These are courses that are not technically part of the Data Science Education Program but for which the instructors are “not oblivious” to DSEP (mainly Data 8) in their design, and the course is being developed with attention to students from Data 8

NWMEDIA 190: Making Sense of Cultural Data

Near Eastern Studies 190: Introduction to Digital Humanities: from Analog to Digital

History 100S: Text Analysis for Digital Humanists and Social Scientists

Advanced Integrative Opportunities

Advanced integrative opportunities enable more advanced students to work hands-on with data in an interdisciplinary, project-based manner. For instance, Terrestrial Hydrology (Geog C136/ESPM C130) is a new course focused on the role that hydrology plays in malaria transmission in sub-Saharan Africa (prerequisites are Math 1A-1B and Physics 7A).

Data science modules in existing courses

Faculty have begun to introduce data science modules into their existing courses as a way to enable students to tap the power of data in a range of disciplines and fields. Using the approach and concepts taught in the Foundations and connector courses, faculty are adding short modules to use data science into existing courses ranging from entry-level to advanced. As an example, a Rhetoric instructor in Fall 2016 developed a three-class module on polling data in the context of the current election. Learn more about modules>>

Other core courses for students who want to pursue data science

In addition to the courses listed above, a variety of advanced and mid-level courses are offered as part of existing programs. Here are just a few examples offered in Fall 2016:

    • CS 186: Introduction to Database Systems

    • CS 189: Introduction to Machine Learning

    • STAT 133: Concepts in Computing with Data

    • STAT 154: Modern Statistical Prediction and Machine Learning

    • STAT 159: Reproducible and Collaborative Data Science

    • CS 61A, B, C

    • CS 188 189 186 70 170

    • Math 53 and Math 54, Math 1A, Math 1B