# 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

#### ANIMAL SCIENCES

- ABG 250 Mathematical Modeling in Biological Systems.

#### ANTHROPOLOGY

- 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.

#### BIOSTATISTICS

- 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.

#### COMPUTER SCIENCE

- 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

#### ECOLOGY & EVOLUTION

- 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

#### ECONOMICS & AGRICULTURAL ECONOMICS

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

#### EPIDEMIOLOGY

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

#### GEOGRAPHY

- GEO200CN. Computational Methods in Geography Robert Hijmans

#### HYDROLOGY

- HYD273. Introduction to Geostatistics

#### MATHEMATICS

- MAT 128C: Numerical Analysis
- MAT 135A: Probability
- MAT 135B: Stochastic Processes
- MAT 160: Math for Data Analytics
- MAT 167: Applied Linear Algebra
- MAT 235C: Probability Theory
- MAT 226C: Numerical Methods
- MAT 280: Topics in Math. Past topics have included compressed sensing, harmonic analysis on graphs and networks.
- MAT 258A: Numerical Optimization
- MAT 280: Topics in Math

#### POLITICAL SCIENCE

- 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

#### PLANT SCIENCE

- 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

#### PSYCHOLOGY

- PSC204A. Statistical Analysis of Psychological Experiments

#### PHYSICS

- PHY 256: Physics of Information and Computation.

#### STATISTICS

- 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

### SPECIAL TOPICS ARCHIVE

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

- ECL290: Data wrangling for ecologists, Peek & Lubell
- Fall 2016
- Evolutionary Algorithms, Herman
- Topology of Data, Tsuruga
- Modern tools for data collection, management and analysis, Caillaud

##### Spring 2016

- ECS 253/MAE 253: Network Theory, D’Souza

##### 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