CS C100. Principles & Techniques of Data Science

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: 4.0

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:
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
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 C100, STAT C100


Class Schedule (Fall 2020):
TuTh 9:30AM - 10:59AM, Internet/Online – Anthony D. JOSEPH, Fernando Perez

Class homepage on inst.eecs

General Catalog listing