
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.
 SDS partners with the Texas Advanced Computing Center (TACC) to offer courses in scientific computing. These courses are marked with "(TACC)" after the course names below.
Popular Statistics and Data Sciences Courses for NonMajors:
 SDS 320E Elements of Statistics
 SDS 322E Elements of Data Science
 SDS 324E Elements of Regression Analysis
Click here to see more information about the Elements Collection of courses.
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 110T, 210T, 310T, 410T: Topics in Statistics and Computation
 SDS 318: Introduction to Statistical and Scientific Computing
 SDS 320E: Elements of Statistics
 SDS 320H: Honors Statistics
 SDS 321: Introduction to Probability and Statistics
 SDS 322: Introduction to Scientific Programming (TACC)
 SDS 322E: Elements of Data Science
 SDS 323: Statistical Learning and Inference
 SDS 324E: Elements of Regression Analysis
 SDS 329C: Practical Linear Algebra I
 SDS 335: Scientific/Technical Computing (TACC)
 SDS 150K: Data Analysis Applications
 SDS 352: Statistical Methods
 SDS 353: Advanced Multivariate Models
 SDS 358: Special Topics in Statistics
 SDS 374C: Parallel Computing for Scientists and Engineers (TACC)
 SDS 374E: Visualization and Data Analysis for Scientists and Engineers (TACC)
 SDS 375: Special Topics in Scientific Computation
 SDS 378: Introduction to Mathematical Statistics
 SDS 379R: Undergraduate Research
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. Only one of the following may be counted: Educational Psychology 371, Mathematics 316, Statistics 309 or Statistics and Data Sciences 301. A student may not earn credit for Educational Psychology 371, Mathematics 316, Statistics 309 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, 303, 304, 305, 306, 328M, Statistics and Scientific Computation 302, 303, 304, 305, 306, or 328M.
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 oneway analysis of variance. Only one of the following may be counted: Statistics and Data Sciences 302, 306, 302F. Flags: Ethics, Quantitative Reasoning. Core: Math.
SDS 110T, 210T, 310T, 410T: Topics in Statistics and Computation
Topics in Statistics and Computation. For each credit hour, one hour per week for one semester. May be repeated for credit when the topic varies.
SDS 318: Introduction to Statistical and Scientific Computing
An introduction to quantitative analysis using fundamental concepts in statistics and scientific computation. Probability, distributions, sampling, interpolation, iteration, recursion and visualization. Three lecture hours and one laboratory hour a week for one semester.
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 oneway analysis of variance. R programming is introduced. Only one of the following may be counted: Statistics and Data Sciences 320E and 328M. Flags: Ethics, Quantitative Reasoning, Independent Inquiry. Core: Math.
SDS 320H: Honors Statistics
An introduction to the fundamental theories, concepts, and methods of statistics. Emphasizes probability models, exploratory data analysis, sampling distributions, confidence intervals, hypothesis testing, correlation and regression, and the use of statistical software. Prerequisite: Admission to the Dean's Scholars Honors Program in the College of Natural Sciences, or consent of instructor.
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: Mathematics 362K, Statistics and Data Sciences 321, Statistics and Scientific Computation 321. Prerequisite: The following with a grade of at least C: Mathematics 408C, 408L, 408S or 408R.
SDS 322: Introduction to Scientific Programming (TACC)
Introduction to programming using both the C and Fortran (95, 2003) languages, with applications to basic scientific problems. Covers common data types and structures, control structures, algorithms, performance measurement, and interoperability. Prerequisite: Credit or registration for Mathematics 408K or 408C.
SDS 322E: Elements of Data Science
Introduction to data science. Topics may include: data wrangling; exploratory data analysis, including data visualization; markdown and data workflow; simulationbased 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 322E and 348.
SDS 323: Statistical Learning and Inference
An introduction to statistical influence, broadly construed as the process of drawing conclusions from data, and to quantifying uncertainty about said conclusions. Covers the major schools of thought that influence modern scientific practice, including classical frequentist methods, machine learning, and Bayesian inference. Three lecture hours a week for one semester. Statistics and Data Sciences 323 and Statistics and Scientific Computation 323 may not both be counted. Prerequisite: Statistics and Data Sciences 321 (or Statistical and Scientific Computation 321) or the equivalent.
SDS 324E: Elements of Regression Analysis
A followup to an introductory statistics course, with an emphasis on the use of regression analysis in applied research. Topics may include: multiple linear regression; ANOVA; logistic regression; random and mixed effects models; and models for dependent data. Emphasis is placed on identification of appropriate statistical methods and interpretation of software output. R programming is introduced. Prerequisite: SDS 302F or SDS 320E (or SDS 302, 304, 306, 328M). Only one of the following may be counted: Statistics and Data Sciences 324E or 332. Flag: Quantitative Reasoning.
SDS 329C: Practical Linear Algebra I
Matrix representations and properties of matrices; linear equations, eigenvalue problems and their physical interpretation; linear least squares and elementary numerical analysis. Emphasis will be placed on physical interpretation, practical numerical algorithms and proofs of fundamental principles. Prerequisite: Credit or registration for Mathematics 408K or 408C.
SDS 335: Scientific/Technical Computing (TACC)
Comprehensive introduction to computing techniques and methods applicable to many scientific disciplines and technical applications. Covers computer hardware and operating systems, systems software and tools, code development, numerical methods and math libraries, and basic visualization and data analysis tools. Three lecture hours a week for one semester. Prerequisite: Mathematics 408D or 408M and prior programming experience.
SDS 150K: Data Analysis Applications
Introduction to the use of statistical or mathematical applications for data analysis. Two hours per week for eight weeks. May be repeated for credit when the topics vary. Offered on the credit/no credit basis only. Prerequisites vary with the topic and are given in the Course Schedule. Topic 1: SPSS Topic 2: SAS Topic 3: STATA Topic 4: Selected Topics
SDS 352: Statistical Methods
Covers simple and multiple regression, fundamentals of experimental design, and analysis of variance methods. Other topics will be selected from the following: logistic regression, Poisson regression, resampling methods, introduction to Bayesian methods, and probability models. Includes substantial use of statistical software. Three lecture hours and one laboratory hour a week for one semester. Prerequisite: Statistics and Data Sciences 303, 304, 305, 306, or Mathematics 316.
SDS 353: Advanced Multivariate Models
Advanced topics in statistical modeling, including models for categorical and count data; spatial and timeseries data; and survival, hazard, and hierarchical models. Extensive use of statistical software to build on knowledge of introductory probability and statistics, as well as multiple regression. Statistics and Data Sciences 353 and Statistics and Scientific Computation 353 may not both be counted. Prerequisite: Mathematics 408D or 408M; and Statistics and Data Sciences 325H or 352.
SDS 358: Special Topics in Statistics
May be repeated for credit when the topics vary. Prerequisite: Upperdivision standing; additional prerequisites may vary with the topic and are given in the Course Schedule.
SDS 374C: Parallel Computing for Scientists and Engineers (TACC)
Parallel computing principles, architectures, and technologies. Parallel application development, performance, and scalability. Prepares students to formulate and develop parallel algorithms to implement effective applications for parallel computing systems. Three lecture hours a week for one semester. Prerequisite: Mathematics 408D or 408M, Mathematics 340L, and prior programming experience using C or Fortran on Unix/Linux systems.
SDS 374E: Visualization and Data Analysis for Scientists and Engineers (TACC)
Scientific visualization principles, practices and technologies, including remote and collaborative visualization. Also introduces statistical analysis, data mining and feature detection. Three lecture hours a week for one semester. Prerequisite: Mathematics 408D or 408M, Mathematics 340L, and prior programming experience using C or Fortran on Unix/Linux systems.
SDS 375: Special Topics in Scientific Computation
May be repeated for credit when the topics vary. Prerequisite: Upperdivision standing; additional prerequisites may vary with the topic and are given in the Course Schedule.
SDS 378: Introduction to Mathematics Statistics
Same as Mathematics 378K. Sampling distributions of statistics, estimation of parameters (confidence intervals, method of moments, maximum likelihood, comparison of estimators using mean square error and efficiency, sufficient statistics), hypothesis tests (pvalues, power, likelihood ratio tests), and other topics. Three lecture hours a week for one semester. Only one the following may be counted: Mathematics 378K, Statistics and Data Sciences 378, Statistics and Scientific Computation 378. Prerequisite: Mathematics 362K with a grade of at least C.
SDS 379R: Undergraduate Research
Individual research project under the supervision of one or more faculty members. The equivalent of three lecture hours a week for one semester. May be repeated for credit. Prerequisite: Upperdivision standing, and consent of instructor.