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

Prerequisites: COMPSCI C8 / INFO C8 / STAT C8 or ENGIN 7; and either COMPSCI 61A or COMPSCI 88. Corequisites: MATH 54 or EECS 16A.

Credit Restrictions: Students will receive no credit for DATA C200\COMPSCI C200A\STAT C200C after completing DATA C100.

Formats:
Summer: 6.0-6.0 hours of lecture, 2.0-2.0 hours of discussion, and 0.0-2.0 hours of laboratory per week
Spring: 6.0-6.0 hours of lecture, 2.0-2.0 hours of discussion, and 0.0-2.0 hours of laboratory per week
Fall: 6.0-6.0 hours of lecture, 2.0-2.0 hours of discussion, and 0.0-2.0 hours of laboratory per week
Spring: 3.0-3.0 hours of lecture, 1.0-1.0 hours of discussion, and 0.0-1.0 hours of laboratory per week
Fall: 3.0-3.0 hours of lecture, 1.0-1.0 hours of discussion, and 0.0-1.0 hours of laboratory per week

Grading basis: letter

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

Also listed as: STAT C200C, DATA C200

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