CS C200A. Principles and Techniques of Data Science

Catalog Description: Explores the data science lifecycle: question formulation, data collection and cleaning, exploratory, analysis, visualization, statistical inference, prediction, and decision-making. Focuses on quantitative critical thinking and key principles and techniques: languages for transforming, querying and analyzing data; algorithms for machine learning methods: regression, classification and clustering; principles of informative visualization; measurement error and prediction; and techniques for scalable data processing. Research term project.

Units: 4.0

Prerequisites: Computer Science/Information/Statistics C8 or Engineering 7; and either Computer Science 61A or Computer Science 88. Corequisite: Mathematics 54 or Electrical Engineering 16A

Credit Restrictions: Students will receive no credit for STAT C200C\COMPSCI C200A\DATA C200 after completing DATA C100, or STAT 200C. A deficient grade in STAT C200C\COMPSCI C200A\DATA C200 may be removed by taking STAT 200C.

Formats:
Fall: 3.0 hours of lecture, 1.0 hours of discussion, and 1.0 hours of laboratory per week
Spring: 3.0 hours of lecture, 1.0 hours of discussion, and 1.0 hours of laboratory per week

Grading basis: letter

Final exam status: Written final exam conducted during the scheduled final exam period

Also listed as: DATA C200, STAT C200C


Class Schedule (Spring 2021):
TuTh 9:30AM - 10:59AM, Internet/Online – Andrew Bray, Joseph Gonzalez

Class homepage on inst.eecs

General Catalog listing