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: COMPSCI C8 / DATA C8 / INFO C8 / STAT C8; and COMPSCI 61A, COMPSCI 88, or ENGIN 7; Corequisite: MATH 54 or EECS 16A.
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 hours of lecture, 2.0 hours of discussion, and 2.0 hours of laboratory per week
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: STAT C100, DATA C100
Class Schedule (Fall 2022):
TuTh 09:30-10:59, Wheeler 150 –
Fernando Perez, Will Fithian