- Courses in statistics are taught by different departments. The following information is only applicable to courses offered by the Department of Statistics and Data Sciences under the SDS field of study.
- Registration questions regarding SDS courses can be directed to stat.admin@austin.utexas.edu. Please include your EID and the course unique number in your inquiry.
- Questions relating to the Undergraduate Certificate in Scientific Computation & Data Sciences and the Undergraduate Certificate in Applied Statistical Modeling can be directed to stat.certificates@austin.utexas.edu.
Statistics & Data Science Courses
Click on a course to be taken to its description.
- SDS 301: Elementary Statistical Methods
- SDS 302F: Foundations of Data Analysis
- SDS 313: Introduction to Data Science
- SDS 315: Statistical Thinking
- SDS 320E: Elements of Statistics
- SDS 320H: Honors Statistics
- SDS 321: Introduction to Probability and Statistics
- SDS 322E: Elements of Data Science
- SDS 324E: Elements of Regression Analysis
- SDS 326E: Elements of Statistical Machine Learning*
- SDS 431: Probability and Statistical Inference
- SDS 334 Intermediate Statistical Methods
- SDS 336: Practical Machine Learning
- SDS 354: Advanced Statistical Methods
- SDS 357: Case Studies in Data Science
- SDS 364: Bayesian Statistics
- SDS 366: Data Visualization*
- SDS 368: Statistical Theory
- SDS 375: Special Topics in Scientific Computation*
- SDS 179R, 279R, 379R: Undergraduate Research
*Please see notes below on these courses.
Course Descriptions
SDS 301: Elementary Statistical Methods
Covers the fundamental procedures for data organization and analysis. Subjects include frequency distributions, graphical presentation, sampling, experimental design, inference, and regression. Three lecture hours a week for one semester. A student may not earn credit for Educational Psychology 308 and 371, Mathematics 316, Statistics 309, 309H, or Statistics and Data Sciences 301 after having received credit for any of the following with a grade of at least C-: Statistics and Data Sciences 302, 302F, 303, 304, 305, 306, 320E, 328M. Core: Math.
[Typically offered in spring semesters; and summer sessions as an online course.]
SDS 302F: Foundations of Data Analysis
Introduction to data analysis and statistical methods. Topics may include: random sampling; principles of observational study and experimental design; data summaries and graphics; and statistical models and inference, including the simple linear regression model and one-way analysis of variance. Only one of the following may be counted: Statistics and Data Sciences 302, 306, 302F. Core: Math.
[Typically offered in fall and spring semesters.]
SDS 313: Introduction to Data Science
Introduction to the principles and practice of data science. Explore R and reproducible data analysis; summarizing data using descriptive statistics; data visualization and storytelling; data wrangling and relational data; basic prediction and classification using regression models; and programming in R. The equivalent of three lecture hours a week for one semester. Only one of the following may be counted: Statistics and Data Sciences 313, 322E, 348.
[Required for SDS majors. Non-majors are restricted from enrollment in the course. Offered in fall semesters only.]
SDS 315: Statistical Thinking
Introduction to the fundamental ideas of statistical thinking with R programming. Explore survey, experimental, and observational study design; common sources of random and systematic error in data; the bootstrap as a tool for quantifying uncertainty; hypothesis testing; regression; and the role of statistics in an ethical and just society. The equivalent of three lecture hours a week for one semester. Prerequisite: Statistics and Data Sciences 313 with a grade of at least C-.
[Required for SDS majors. Non-majors are restricted from enrollment in the course. Offered in spring semesters only.]
SDS 320E: Elements of Statistics
Introduction to statistics. Topics may include: probability; principles of observational study and experimental design; statistical models and inference, including the multiple linear regression model and one-way analysis of variance. R programming is introduced. Only one of the following may be counted: Statistics and Data Sciences 320E, 320H, and 328M. Core: Math.
[Required for SDS minors. This class should not be taken by SDS majors. Typically offered in fall and spring semesters; and summer sessions as an online course.]
SDS 320H: Honors Statistics
"Introduction to statistics. Subjects include probability; principles of observational study and experimental design; statistical models and inference, including the multiple linear regression model and one-way analysis of variance. R programming is introduced. Restricted to students in the Dean’s Scholars, Health Science Scholars, or Polymathic Scholars Honors Program in the College of Natural Sciences. Only one of the following may be counted: Statistics and Data Sciences 320E, 320H, and 328M. Core: Math.
[This class should not be taken by SDS Majors.]
SDS 321: Introduction to Probability and Statistics
Covers fundamentals of probability, combinatorics, discrete and continuous random variables, jointly distributed random variables, and limit theorems. Using probability to introduce fundamentals of statistics, including Bayesian and classical inference. The equivalent of four lectures hours a week. Only one of the following may be counted: Statistics and Data Sciences 321 & Statistics and Data Sciences 431. Prerequisite: The following with a grade of at least C-: Mathematics 408C, 408L, 408Q, 408R, or 408S.
[Approved Elective for SDS minor. Typically offered in fall and spring semesters.]
SDS 322E: Elements of Data Science
Learn data science tools and examine data wrangling; exploratory data analysis and data visualization; markdown and data workflow; simulation-based inference; and classification methods. R programming is emphasized and Python programming is introduced. Prerequisite: Credit for an introductory statistics course. Only one of the following may be counted: Statistics and Data Sciences 313, 322E and 348.
[Required for SDS minors. This class should not be taken by SDS majors. Typically offered in fall and spring semesters.]
SDS 324E: Elements of Regression Analysis
Explore the use of regression analysis in applied research and learn about multiple linear regression; ANOVA; logistic regression; random and mixed-effects models; and models for dependent data. Engage in the identification of appropriate statistical methods and interpretation of software output. R programming is introduced. Prerequisite: SDS 302F and SDS 322E; or SDS 320E. Only one of the following may be counted: Statistics and Data Sciences 324E or 332.
[Approved elective for SDS minor. This class should not be taken by SDS majors. Typically offered in fall and spring semesters.]
SDS 326E: Elements of Statistical Machine Learning*
Introduction to the concepts and tools of data science, statistics, and machine learning used to draw inferences about large-scale and real-world data. Explore data visualization, linear and nonlinear models, regularization, classification, resampling, tree-based methods, support vector machines, and unsupervised learning. Three lecture hours a week for one semester. Statistics and Data Sciences 323 and 326E may not both be counted. Prerequisite: Statistics and Data Sciences 320E, 321, 322E, or Mathematics 362K.
[Approved elective for SDS minor. This class should not be taken by SDS majors. Typically offered in fall & spring semesters.]
*Equivalent to SDS 323: Statistical Learning and Inference
SDS 431: Probability and Statistical Inference
Introduction to probability and statistical inference. Examine events and random experiments; basic rules of probability; joint, conditional, and marginal probability and independence; discrete and continuous random variables; random sampling and estimation; large-sample theory results and central limit theorem-based inferential summaries; and maximum likelihood estimation. Three lecture hours and two laboratory hours a week for one semester. Statistics and Data Sciences 321 and 431 may not both be counted. Prerequisite: Statistics and Data Sciences 315 with a grade of at least C-; and credit with a grade of at least C- or registration for Mathematics 408D, 408L, or 408S.
[Required for SDS majors. Non-majors are restricted from enrollment in the course. Offered in fall semesters only.]
SDS 334: Intermediate Statistical Methods
Introduction to applied regression analysis. Explore estimation and inference in multiple regression models; logistic regression; regression for count data; time-to-event models; and case studies in regression modeling in published work, emphasizing both the use and limitations of regression modeling in advancing scientific knowledge. The equivalent of three lecture hours a week for one semester. Prerequisite: Statistics and Data Sciences 431 with a grade of at least C-; Mathematics 340L or 341 or Statistics and Data Sciences 329C with a grade of at least C-; and Computer Science 303E or 312 with a grade of at least C-.
[Required for SDS majors. Non-majors are restricted from enrollment in the course. Offered in spring semesters only.]
SDS 336: Practical Machine Learning
Introduction to machine learning for data science with an emphasis on Python programming. Explore comparing algorithm performance; decision-tree algorithms; classification algorithms; model averaging; unsupervised learning; and neural networks. The equivalent of three lecture hours a week for one semester. Prerequisite: Statistics and Data Sciences 334 with a grade of at least C-; and registration for Computer Science 327E or previously completed with a grade of at least C-.
[Required for SDS majors. Non-majors are restricted from enrollment in the course. Offered in fall semesters only.]
SDS 354: Advanced Statistical Methods
Explore advanced methods in statistics and data science. Examine modeling data with multilevel (hierarchical) structure and causal inference, including design and analysis strategies. Discuss smoothing methods; spatial and time series models; additive models; and models for network data. The equivalent of three lecture hours a week for one semester. Prerequisite: Statistics and Data Sciences 334 with a grade of at least C-.
[Required for SDS majors. Non-majors are restricted from enrollment in the course. Offered in fall semesters only starting in fall 2025.]
SDS 357: Case Studies in Data Science
Explore advanced case studies in data science, with an emphasis on the full data analysis pipeline. Examine data collection, identification of data limitations; data privacy; data preparation and exploration; building, using, and evaluating models; creating data products; and communication and persuasion with data. The equivalent of three lecture hours a week for one semester. Prerequisite: Statistics and Data Sciences 334 with a grade of at least C-; credit with a grade of at least C- or registration for Statistics and Data Sciences 336. Flag: Writing.
[Required for SDS majors. Non-majors are restricted from enrollment in the course. Offered in spring semesters only starting in spring 2026.]
SDS 364: Bayesian Statistics
Introduction to the Bayesian approach for statistical inference. Explore prior, posterior, and predictive distributions: conjugate priors; informative and non-informative priors; models for normal, categorical, and count data; Bayesian computation, including MCMC and the Gibbs sampler; hierarchical models; and Bayesian model checking and model selection. The equivalent of three lecture hours a week for one semester. Prerequisite: Statistics and Data Sciences 431 or 321 with a grade of at least C-; Mathematics 340L or 341 or Statistics and Data Sciences 329C with a grade of at least C-; and credit with a grade of at least C- or registration for Statistics and Data Sciences 334.
[Approved elective for SDS majors. Non-majors are restricted from enrollment in the course.]
SDS 366: Data Visualization*
Explore how to visualize data sets. Reason about, and communicate with, data visualizations. The equivalent of three lecture hours a week for one semester. Prerequisite: SDS 320E and SDS 322E with grades of at least C-; or SDS 315 and SDS 313 with grades of at least C-. Only one of the following may be counted: Statistics and Data Sciences 366 and 375 (topic: Data Visualization in R).
[Approved Elective for SDS Majors and SDS Minors. Offered in spring semesters only.]
*Formerly offered under SDS 375: Special Topics in Scientific Computation.
SDS 368: Statistical Theory
Introduction to the mathematical theory of statistics. Explore maximum likelihood estimation, confidence intervals, hypothesis tests and statistical decision theory, tail and concentration bounds, concentration of measure, and nonparametric statistics. The equivalent of three lecture hours a week for one semester. Prerequisite: Statistics and Data Sciences 431 with a grade of at least C-; Mathematics 340L or 341 or Statistics and Data Sciences 329C with a grade of at least C-; and credit with a grade of at least C- or registration for Statistics and Data Sciences 334; and a solid foundation in calculus, probability theory, and linear algebra.
[Approved elective for SDS majors. Non-majors are restricted from enrollment in the course.]
SDS 375: Special Topics in Scientific Computation*
Three lecture hours a week for one semester. Statistics and Data Sciences 375 and Statistics and Scientific Computation 375 may not both be counted unless topics vary. May be repeated for credit when the topics vary. Prerequisite: Upper-division standing; additional prerequisites may vary with the topic.
*Only one of the following may be counted: Statistics and Data Sciences 366 and 375 (Topic: Data Visualization in R).
SDS 179R, 279R, 379R: Undergraduate Research
Individual research project under the supervision of one or more SDS faculty members. The equivalent of one, two, or three lecture hours a week for one semester. May be repeated for credit. Prerequisite: Upper-division standing, consent of instructor, and approval by the SDS undergraduate major faculty advisor.
[SDS majors may take this course to fulfill credit for a general elective, however, the course cannot be used to fulfill the required approved SDS electives. Please contact stat.admin@austin.utexas for more info.]