CSC 380: Principles of Data Science (Spring 2025)

This course introduces students to principles of data science that are necessary for computer scientists to make effective decisions in their professional careers. A number of computer science sub-disciplines now rely on data collection and analysis. For example, computer systems are now complicated enough that comparing the execution performance of two different programs becomes a statistical estimation problem rather than a deterministic computation. This course teaches students the basic principles of how to properly collect and process data sources in order to derive appropriate conclusions from them. The course has main components of: basic probability, basic statistics and data wrangling, and basic data analysis using programming libraries.

Logistics info

Time and venue: MW 3:30-4:45pm, WSEL200SW

Syllabus

Piazza link Access code: datascience

Gradescope Entry code: 8K2GY3 (Make sure your gradescope email address is the same as the one you have on D2L.)

D2L course webpage

Course staff

Instructor	Chicheng Zhang (Pronouns: he/him/his)		chichengz at arizona dot edu
TA	Yichen Li (Pronouns: he/him/his)		yichenl at arizona dot edu
TA	Tian Tan (Pronouns: he/him/his)		tiantan at arizona dot edu
TA	Jianwei (James) Shen (Pronouns: he/him/his)		sjwjames at arizona dot edu

Office Hours: see D2L

Textbook

There is no single designated textbook for this course. Much of the course materials and assigned readings will be based on the following books:

• WJ: Watkins, J., “An Introduction to the Science of Statistics: From Theory to Implementation”

• MK: Murphy, K. “Machine Learning: A Probabilistic Perspective.” MIT press, 2012 (accessible online via UA library)

• WL: Wasserman, L. “All of Statistics: A Concise Course in Statistical Inference.” Springer, 2004 (accessible online via UA library)

• ISL: James, G., Witten, D., Hastie, T., & Tibshirani, R. An introduction to statistical learning with Applications in Python. New York: Springer

• Data 8 and Data 100 (from Berkeley)

Other useful resources

• Notes for probability from Stanford’s CS 229 course.

• The Probability and Statistics Cookbook, and Calculus cheatsheet.

• You may find using LaTeX helpful in writing homeworks or reports. Some useful LaTeX resources: Learn LaTeX in 30 minutes by Overleaf; Introduction to LATEX by MIT Research Science Institute