Spring Data Science courses

What is the Data Science Education Program?
Berkeley’s Data Science Education Program offers an interdisciplinary curriculum that provides a foundation for undergraduates in all fields and intended majors to engage capably and critically with data. No matter their major or background, all students today have a critical need to navigate a data-rich world.
Foundations of Data Science (CS C8 and STAT C8)
The program starts at the introductory level, with Foundations of Data Science, or Data 8, which teaches core computational and statistics concepts while enabling students to work hands-on with real data.
  • Accessible to students in all intended majors with no prerequisites.
  • Ideal for freshmen and sophomores; also now open to others
  • Appropriate for science and engineering students preparing to pursue more advanced courses, as well as social science and humanities students
  • Satisfies requirements, including the L&S Quantitative Reasoning requirement and the statistics requirement in most majors requiring statistics (See full list)
  • Taught this spring by acclaimed Computer Science Professor John DeNero
  • 4 units; lecture and (two-hour) lab section (students must enroll in both)
Tying data science to students’ interests
Connector courses enable students to develop deeper understanding or apply core concepts from the foundational course to explore real-world issues that relate to students’ areas of interest across disciplines.
New advanced courses launching this spring
New courses are being developed for Spring 2017 that take Foundations of Data Science (Data 8) as a prerequisite. They are ideal for students looking to move further into data science and take their knowledge to the next level.
  • Statistical Methods for Data Science (Stat 28): Stat 28 is a new lower-division course for students in many disciplines who have taken Data 8 and want to learn more advanced techniques without the additional mathematics called on in upper-division statistics. Students are introduced to “R”, the widely used statistical language, and obtain hands-on experience in implementing statistical methods on real-world datasets.
  • Probability for Data Science (Stat 140): This new course taught by Ani Adhikari introduces students to probability theory using both mathematics and computation. The prerequisites are Foundations of Data Science (Data 8) and one year of calculus.
  • Faculty will pilot a new course, Principles and Techniques of Data Science (Data 100) as a core offering for a data science major and minor, which has been approved by the Academic Senate as CS C100 and Stat  C100. As more details are available, they will be announced at data.berkeley.edu.
Advanced integrative opportunities are also being developed. These 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).
What students are saying about Foundations of Data Science:
  • “One of the things I most enjoy about data science is the diversity-- my classmates range from English majors to bio majors to computer science majors -- all looking at data from our different perspectives.”
  • “This class puts theory into practice. I was able to use data to tell powerful visual stories about the struggles I experienced growing up in southeast LA.”
  • “Out of all the classes I’ve taken, this class gave me the most practical knowledge. I’m applying it in my internship at Google already.”
To learn more, please watch this two-minute video made by Berkeley students this fall.
Please check out our website at data.berkeley.edu, and email me at mhurley@berkeley.edu if you have any questions.