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: Computer Science/Information/Statistics C8; and either Computer Science 61A, Computer Science 88 or Engineering 7. Corequisite: Mathematics 54 or Electrical Engineering 16A. Computer Science C8 Computer Science 61A Computer Science 88 Engineering 7 Mathematics 54 Electrical Engineering 16A

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: STAT C100

Class Schedule (Fall 2019):
TuTh 9:30AM - 10:59AM, Wheeler 150 – Deborah Nolan, Joshua Hug

Class Schedule (Spring 2020):
TuTh 9:30AM - 10:59AM, Wheeler 150 – Ani Adhikari, Joseph Gonzalez

Class homepage on inst.eecs

General Catalog listing