- 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 323: Statistical Learning and Inference*
- 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. 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.

[*Typically offered in 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. Flags: Ethics, Quantitative Reasoning. Core: Math.

[*Required for Human Development and Family Sciences (HDFS) majors. 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 and 328M. Flags: Ethics, Quantitative Reasoning, Independent Inquiry. Core: Math.

[*Required for Biology Majors. Meets requirements for Biochemistry and Chemistry Majors. This class should not be taken by SDS Majors. Typically offered in Fall and Spring Semesters and Summer Session.*]

#### 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.

[*Typically offered in Fall Semesters*.]

#### 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.

[*Required for Computer Science Majors. Typically offered in Fall and Spring Semesters.*]

#### 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; 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 322E and 348.

[*This class should not be taken by SDS Majors. Typically offered in Fall and Spring Semesters.*]

#### 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.

[*Currently offered in Spring Semesters*.]

**Course Number and Name will change to SDS 326E Elements of Statistical Machine Learning in Fall 2024*

#### SDS 324E: Elements of Regression Analysis

A follow-up 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 and SDS 322E) or SDS 320E. Only one of the following may be counted: Statistics and Data Sciences 324E or 332. Flag: Quantitative Reasoning.

[*Typically offered in Fall and Spring Semesters.*]

#### SDS 326E: **Elements of Statistical Machine Learning***

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.

[*Currently offered in 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 network. 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: Independent Inquiry and 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: Statistics and Data Sciences 320E; 322E; or SDS 313 and 315. 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. Offered in Spring Semesters Only*.]

**Currently 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. Offered in Fall Semesters only starting in Fall 2025.*]

#### SDS** 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 three lecture hours a week for one semester. May be repeated for credit. Prerequisite: Upper-division standing, consent of instructorand 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.]*