Courses at UC Davis

This is a list of courses offered at UC Davis with content related to data science.

Upcoming Special Topics (Spring 2020)

  • STS 112: Visualizing Society with Data – analysis and visualization of historical and contemporary data about populations and societies using R. (CRN 84358)

Departmental Courses


  • ABG 250 Mathematical Modeling in Biological Systems.


  • ANT291: Statistical Rethinking – A Bayesian Course with Examples in R and Stan, Richard McElreath. Currently not taught, but link contains reference material. If you are interested in this content, check out DataLab’s research cluster on Applied Bayesian Statistics.


  • BST222. Survival Analysis
  • BST223. Generalized Linear Models
  • BST224. Analysis Of Longitudinal Data
  • BST225. Clinical Trials
  • BST226. Statistical Methods for Bioinformatics
  • BST227. Machine Learning in Genomics.


  • List of 2017-2018 computer science course offerings
  • ECS 116: Databases for Non-Majors
  • ECS 132: Probabilistic and Statistical Modeling
  • ECS 145: Scripting Languages
  • ECS 158: Programming on Parallel Architectures
  • ECS 163: Information Interfaces
  • ECS 165: Database Systems
  • ECS 170: Artificial Intelligence
  • ECS 171: Machine Learning
  • ECS 175: Computer Graphics
  • ECS 188: Ethics and Information Age
  • ECS 230: Applied Numerical Linear Algebra
  • ECS 231: Large Scale Scientific Computing
  • ECS 256: Probabilistic Modeling
  • ECS 271: Machine Learning
  • EEC 274. Internet Measurements, Modeling and Analysis
  • ECS 275A: Advanced Computer Graphics
  • ECS 275B: Advanced Computer Graphics


  • ECL298: Bayesian Models- A Statistical Primer
  • ECL231: Mathematical Methods in Population Biology
  • ECL290: Design and Analysis of Ecological Experiments
  • ECL233: Computational methods in population biology
  • ECL262: Advanced Population Dynamics
  • ECL298: R Data Analysis and Visualization (D-DAVIS); Basics of Data Manipulation in R
  • EVE231: Principles of Biological Data Analysis


  • ECN240A. Econometric Methods
  • ECN240B. Econometric Methods
  • ARE256. Applied Econometrics


  • EPI204A. Foundation of Statistical Methods
  • EPI204B. Statistical Models, Methods, and Data Analysis for Scientists



  • HYD273. Introduction to Geostatistics



  • POL211. Research Methods in Political Science
  • POL212. Quantitative Analysis in Political Science
  • POL213. Quantitative Analysis in Political Science II
  • POL279. Political Networks: Methods and Applications


  • PLS120. Applied Statistics in Agricultural Science
  • PLS205. Experimental Design and Analysis
  • PLS206. Applied Multivariate Modeling in Agricultural and Environmental Sciences
  • PLS298. Applied Statistical Modeling for Environmental Science


  • PSC204A. Statistical Analysis of Psychological Experiments


  • PHY 256: Physics of Information and Computation.


  • STA 130A: Mathematical Statistics: Brief Course
  • STA 130B: Mathematical Statistics: Brief Course
  • STA 138: Analysis of Categorical Data
  • STA 141A: Fundamentals of Statistical Data Science (using R)
  • STA 141B: Data & Web Technologies for Data Analysis (previously has used Python)
  • STA 141C: Big Data & High Performance Statistical Computing
  • STA 144: Sample Theory of Surveys
  • STA 145: Bayesian Statistical Inference
  • STA 160: Practice in Statistical Data Science
  • STA 206: Statistical Methods for Research I
  • STA 207: Statistical Methods for Research II
  • STA 208: Statistical Methods in Machine Learning
  • STA 224: Analysis of Longitudinal Data
  • STA 232: Applied Statistics I, II, III
  • STA 242: (Graduate Level) Introduction to Statistical Programming
  • STA 243: Computational Statistics


These are some “special topics” courses which are not taught regularly, the focal topic is subject to change, and/or may be of particular domain interest.

Spring 2018
  • Graduate group in ecology “R-DAVIS” (Introduction to R for data analysis and visualization)
Winter 2018
  • CEE/GEO 254: Introduction to R, Niemeier
Fall 2017
  • PLS 298: Applied statistical modeling for the environmental sciences, Latimer
  • EPI 202: Quantitative epidemiology, Harvey
  • PCS 205C: Structural equation modeling, Rhemtulla
  • ECS 265A: Distributed Database Systems, Sadoghi
Winter 2017
Spring 2016
Winter 2016
  • ECL 298:
  • ANT 291: Statistical Rethinking – A Bayesian Course with Examples in R and Stan, McElreath
  • PHY 256: Physics of Information and Computation, Crutchfield
  • STA 250: Numerical Optimization, Hsieh
  • Fall 2015
  • BIM 289C: Special Topics in Computational Bioengineering: Genomic Big Data Analysis, Aviran
Winter 2015
  • ANT 291: Statistical Rethinking – A Bayesian Course with Examples in R and Stan, McElreath
  • MAT 280: Topics in Convex Optimization
  • PLS 205: Design, analysis and interpretation of experiments, Dubcovsky