Catalog Description: In this course, students will explore the data science lifecycle, including question formulation, data collection and cleaning, exploratory data analysis and visualization, statistical inference and prediction​, and decision-making.​ This class will focus on quantitative critical thinking​ and key principles and techniques needed to carry out this cycle. These include languages for transforming, querying and analyzing data; algorithms for machine learning methods including regression, classification and clustering; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction; and techniques for scalable data processing.

Units: 4

Prerequisites: DATA C8 or STAT 20 with a C- or better, or Pass; and COMPSCI 61A, COMPSCI/DATA C88C, or ENGIN 7 with a C- or better, or Pass; Corequisite: MATH 54, 56, 110, EECS 16A, PHYSICS 89 or equivalent linear algebra (C- or better, or Pass, required if completed prior to Data C100).

Credit Restrictions: Students will receive no credit for DATA C100\STAT C100\COMPSCI C100 after completing DATA 100. A deficient grade in DATA C100\STAT C100\COMPSCI C100 may be removed by taking DATA 100.

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: 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 C100, DATA C100

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