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 DATA C200\COMPSCI C200A\STAT C200C after completing DATA C100.

Formats:
Fall: 6.0 hours of lecture, 2.0 hours of discussion, and 2.0 hours of laboratory per week
Spring: 3.0 hours of lecture, 1.0 hours of discussion, and 1.0 hours of laboratory per week
Summer: 6.0 hours of lecture, 2.0 hours of discussion, and 2.0 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


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