Course Schedule and Catalog

Long Title: VISITING RESEARCH

Department: Statistics

Grade Mode: Satisfactory/Unsatisfactory

Language of Instruction: Taught in English

Course Type: Research

Credit Hours: 0

Description: Research conducted by visiting student scholars. Department Permission Required. Repeatable for Credit.

Long Title: AP/OTH CREDIT IN STATISTICS

Department: Statistics

Grade Mode: Transfer Courses

Language of Instruction: Taught in English

Course Type: Transfer

Credit Hours: 4

Description: Provides transfer credit based on student performance on approved examinations in statistics, such as the AP Statistics exam. This credit counts toward the total credit hours required for graduation, but does not count for distribution. Equivalency: STAT 280.

Long Title: SPECIAL TOPICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Independent Study, Internship/Practicum, Laboratory, Lecture, Seminar

Credit Hours: 1 TO 4

Description: Topics and credit hours may vary each semester. Contact department for current semester’s topic(s). Repeatable for Credit.

Long Title: ELEMENTARY APPLIED STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Distribution Group: Distribution Group III

Credit Hours: 4

Description: Topics include basic probability, descriptive statistics, probability distributions, confidence intervals, significance testing, simple linear regression and correlation, association between categorized variables. Equivalency: STAT 180.

Long Title: INTRODUCTION TO STATISTICS FOR BIOSCIENCES

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Distribution Group: Distribution Group III

Credit Hours: 4

Prerequisite(s): (MATH 101 OR MATH 105 OR MATH 112) AND (MATH 102 OR MATH 106)

Description: An introduction to statistics for Biosciences with emphasis on statistical models and data analysis techniques. Computer-assisted data analysis and examples, are explored in laboratory sessions. Topics include descriptive statistics, correlation and regression, categorical data analysis, statistical inference through confidence intervals and significance testing, rates, and proportions. Real-world examples are emphasized. Recommended Prerequisite(s): MATH 212 or MATH 222

Long Title: PROBABILITY AND STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Distribution Group: Distribution Group III

Credit Hours: 3

Prerequisite(s): MATH 102 OR MATH 106

Description: Probability and the central concepts and methods of statistics including probability, random variables, distributions of random variables, expectation, sampling distributions, estimation, confidence intervals, and hypothesis testing. Cross-list: ECON 307. Recommended prerequisite(s): MATH 212. Mutually Exclusive: Cannot register for STAT 310 if student has credit for DSCI 301/STAT 315.

Long Title: HONORS PROBABILITY AND MATHEMATICAL STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Distribution Group: Distribution Group III

Credit Hours: 3

Prerequisite(s): MATH 212 OR MATH 222

Description: Probability and the central concepts and methods of statistics including probability, random variables, distributions of random variables, expectation, sampling distributions, estimation, confidence intervals, and hypothesis testing. Advanced topics (not covered in STAT 310 or STAT 315) include the modeling stochastic phenomena and asymptotic statistical theory. Intended for students wishing to understand more rigorous statistical theory and for those contemplating a BS degree in Statistics or graduate school in statistical science. Required prerequisite(s): MATH 212 (or equivalent). Mutually Exclusive: A student cannot register for STAT 311 if student has credit for ECON 307/STAT 310 or STAT 315/DSCI 301.

Long Title: PROBABILITY & STATISTICS FOR ENGINEERS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Distribution Group: Distribution Group III

Credit Hours: 3

Prerequisite(s): MATH 102

Description: Probability and the central concepts and methods of statistics including probability, distributions of random variables, expectation, sampling distributions, estimation, confidence intervals, and hypothesis testing. Examples are predominantly from civil and environmental engineering. Recommended Prerequisite(s): MATH 212.

Long Title: UNCERTAINTY AND RISK IN URBAN INFRASTRUCTURES

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Distribution Group: Distribution Group III

Credit Hours: 3

Prerequisite(s): STAT 312 OR STAT 310 OR STAT 315 OR DSCI 301 OR ECON 307 OR ECON 382 OR STAT 331 OR ELEC 331

Description: This course explores methods for practical risk-based decision support, particularly for infrastructure systems. Uncertainty quantification (UQ) to external events including natural hazards is at the core of risk-informed design, operation, and mitigation actions. UQ also guides engineering practice and enables code developments. The course emphasizes decision theory, Bayesian approaches, risk analysis tools, and infrastructure safety. Cross-list: CEVE 313. Repeatable for Credit.

Long Title: PROBABILITY AND STATISTICS FOR DATA SCIENCE

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Distribution Group: Distribution Group III

Credit Hours: 4

Prerequisite(s): MATH 102 OR MATH 106

Description: An introduction to mathematical statistics and computation for applications to data science. Topics include probability, random variables expectation, sampling distributions, estimation, confidence intervals, hypothesis testing and regression. A weekly lab will cover the statistical package, R, and data projects. Cross-list: DSCI 301. Recommended Prerequisite(s): MATH 212. Mutually Exclusive: Cannot register for STAT 315 if student has credit for ECON 307/STAT 310.

Long Title: ECONOMETRICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Credit Hours: 4

Prerequisite(s): (ECON 209 OR ECON 309 OR ECON 446) AND (ECON 308 OR ECON 401 OR ECON 477)

Description: Survey of estimation and forecasting models. Includes multiple regression time series analysis. A good understanding of linear algebra is highly desirable. Cross-list: ECON 310. Mutually Exclusive: Cannot register for STAT 376 if student has credit for ECON 409/STAT 400.

Long Title: METHODS OF DATA ANALYSIS AND SYSTEM OPTIMIZATION

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Credit Hours: 4

Prerequisite(s): STAT 180 OR STAT 280 OR STAT 305 OR STAT 310 OR ECON 307 OR STAT 312 OR STAT 315 OR DSCI 301

Description: The three general areas covered in this methodology oriented course are (a) statistical methods, including regression, sampling, and experimental design; (b) simulation based methods in statistics, queuing and inventory problems; (c) an introduction to optimization methods. Excel serves as the basic computing software.

Long Title: R FOR DATA SCIENCE

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Distribution Group: Distribution Group III

Credit Hours: 3

Prerequisite(s): STAT 305 OR STAT 312 OR STAT 310 OR ECON 307 OR STAT 311 OR STAT 385 OR STAT 315 OR DSCI 301

Description: This course introduces students to the statistical programming language, R, and how to use it in statistical and data science problems. The course traces the data science pipeline from importing data into R, exploring and visualizing data, applying a variety of statistical methods, and communicating results. Important computational tools for data science (e.g. databases, web scraping, and big data) and good programming practice are integrated throughout the course. No programming experience is required. Graduate/Undergraduate Equivalency: STAT 605. Mutually Exclusive: Cannot register for STAT 405 if student has credit for STAT 605.

Long Title: SAS STATISTICAL PROGRAMMING

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Laboratory

Credit Hours: 3

Prerequisite(s): STAT 305 OR STAT 312 OR ECON 307 OR ECON 382 OR STAT 385 OR STAT 310 OR STAT 315 OR DSCI 301

Description: Students will learn how to work within the statistical programming language SAS. The course covers from getting data into SAS, transforming and plotting it, to applying appropriate statistical analysis, and communicating the results. Important topics such as database managing with SQL, macro programming, interactive Matrix Language, and efficient programming in general are integrated throughout the course. Graduate/Undergraduate Equivalency: STAT 606. Mutually Exclusive: Cannot register for STAT 406 if student has credit for STAT 606.

Long Title: LINEAR REGRESSION

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Credit Hours: 4

Prerequisite(s): STAT 310 OR STAT 311 OR STAT 312 OR ECON 307 OR STAT 315 OR DSCI 301

Description: An introduction to linear regression and its applications. Topics include simple and multiple linear regression, least squares, analysis of variance, model selection, diagnostics, remedial measures. Applications to real data using statistical software are emphasized. Recommended Prerequisite(s): CMOR 302 or MATH 355. Mutually Exclusive: Cannot register for STAT 410 if student has credit for STAT 615.

Long Title: ADVANCED STATISTICAL METHODS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): (STAT 310 OR STAT 312 OR STAT 315 OR DSCI 301 OR ECON 307 OR ECON 382) AND (STAT 410 OR STAT 615)

Description: Advanced topics in statistical applications such as sampling, experimental design and statistical process control. STAT 411 will have assignments and examinations focusing more on basic concepts than on theoretical methods. Graduate/Undergraduate Equivalency: STAT 616. Mutually Exclusive: Cannot register for STAT 411 if student has credit for STAT 616.

Long Title: INTRODUCTION TO STATISTICAL MACHINE LEARNING

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 410 AND (STAT 405 OR CAAM 210 OR CMOR 220 OR COMP 140 OR COMP 130)

Description: This course is an introduction to concepts, methods, and best practices in statistical machine learning. Topics covered include regularized regression, classification, kernels, dimension reduction, clustering, trees, and ensemble learning. Emphasis will be placed on applied data analysis and computation. Recommended Prerequisite(s): STAT 411 and CAAM 335 or CMOR 302 or MATH 354 or MATH 355. Mutually Exclusive: Cannot register for STAT 413 if student has credit for ELEC 478.

Long Title: DATA SCIENCE CONSULTING

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Credit Hours: 3

Prerequisite(s): STAT 405 OR COMP 140 OR CAAM 210 OR CMOR 220

Description: Students in this course will advise clients at Rice and beyond in a data science consulting clinic, learn best practices in consulting, and gain exposure to a variety of real data science problems. Instructor Permission Required.Graduate/Undergraduate Equivalency: STAT 515. Recommended Prerequisite(s): STAT 413 or COMP 440 or COMP 540 or COMP 330 or STAT 411. Mutually Exclusive: Cannot register for STAT 415 if student has credit for STAT 515. Repeatable for Credit.

Long Title: PROBABILITY

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: Topics include random variables, distributions, transformations, moment generating functions, common families of distributions, independence, sampling distributions, and basic stochastic processes. STAT 418 will have assignments and examinations focusing more on basic concepts than on theoretical methods. Graduate/Undergraduate Equivalency: STAT 518. Mutually Exclusive: Cannot register for STAT 418 if student has credit for STAT 518.

Long Title: STATISTICAL INFERENCE

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): (MATH 354 OR MATH 355 OR CAAM 334 OR CAAM 335 OR CMOR 302 OR CMOR 303) AND STAT 418

Description: Topics include principles of data reduction, point estimation, hypothesis testing, interval estimation, Bayesian inference, Decision Theory, inference foundations of analysis of variance and regression. STAT 419 will have assignments and examinations focusing more on basic concepts than on theoretical methods. Graduate/Undergraduate Equivalency: STAT 519. Mutually Exclusive: Cannot register for STAT 419 if student has credit for STAT 519.

Long Title: APPLIED TIME SERIES AND FORECASTING

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 410 OR ECON 310

Description: Applied time series modeling and forecasting, with applications to financial markets. STAT 621 is a graduate version of STAT 421 with advanced assignments. Graduate/Undergraduate Equivalency: STAT 621. Mutually Exclusive: Cannot register for STAT 421 if student has credit for STAT 621.

Long Title: PROBABILITY IN BIOINFORMATICS AND GENETICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Credit Hours: 4

Prerequisite(s): STAT 310 OR ECON 307 OR STAT 315 OR DSCI 301 OR STAT 312 OR STAT 418

Description: Course introduces the student to modern biotechnology and genomic data. Statistical methods to analyze genomic data are covered, including probability models, basic stochastic processes, and statistical modeling. Biological topics include DNA sequence analysis, phylogenetic inference, gene finding, and molecular evolution. A weekly 1-hour lab on computational aspects of the course will cover software and practice of bioinformatics and computational biology, including modern data sets. Graduate/Undergraduate Equivalency: STAT 623. Mutually Exclusive: Cannot register for STAT 423 if student has credit for STAT 623.

Long Title: INTRODUCTION TO BAYESIAN INFERENCE

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 410 AND STAT 405 OR COMP 210 OR COMP 140 OR COMP 130

Description: This course is an introduction to Bayesian inference, with emphasis on concepts and methods for analyzing data. We will consider a variety of models, including MCMC algorithms and methods for linear regression and hierarchical models. Computational methods will be emphasized. Recommended Prerequisite(s): STAT 411 or CAAM 335 or MATH 355.

Long Title: DATA SCIENCE PROJECTS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Credit Hours: 3

Description: In this project-based course, student teams will complete semester-long data science research or analysis projects selected from a variety of disciplines and industries. Students will also learn best practices in data science. Instructor Permission Required.Graduate/Undergraduate Equivalency: STAT 535. Mutually Exclusive: Cannot register for STAT 435 if student has credit for STAT 535. Repeatable for Credit.

Long Title: STATISTICS FOR BIOENGINEERING

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 1

Prerequisite(s): BIOE 252

Description: Course covers application of statistics to bioengineering. Topics include descriptive statistics, estimation, hypothesis testing, ANOVA, and regression. BIOE 252 may be taken concurrently with BIOE 440. BIOE 440/STAT 440 and BIOE 439 cannot both be taken for credit. Cross-list: BIOE 440. Mutually Exclusive: Cannot register for STAT 440 if student has credit for BIOE 439.

Long Title: QUANTITATIVE FINANCIAL RISK MANAGEMENT

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): MATH 211 AND MATH 212 AND (ECON 400 OR STAT 400 OR ECON 409 OR STAT 410) OR STAT 310 OR ECON 307 OR STAT 315 OR DSCI 301 OR STAT 312 OR STAT 331 OR ELEC 331

Description: This course covers the use of financial securities and derivatives to take or hedge financial risk positions. Most commonly used instruments, from simple forwards and futures to exotic options and swaptions are covered. The pricing of derivatives securities will also be studied, but the emphasis will be on the mechanics and uses of financial engineering methods. STAT 449 is mutually exclusive to ECON 449. Credit cannot be given for both. Graduate/Undergraduate Equivalency: STAT 649. Mutually Exclusive: Cannot register for STAT 449 if student has credit for ECON 449/STAT 649.

Long Title: SENIOR CAPSTONE PROJECT

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: Students engage in individual or team-oriented statistical projects to solve problems motivated by theory, computation, or application to real problems and data. Typical projects involve statistical modeling, data analysis, and computing to answer substantive questions in engineering or the physical, biological, or social sciences. Participants attend regular seminars addressing project development, research techniques and effective written and verbal communication skills in presenting statistical results. Repeatable for Credit.

Long Title: BIOSTATISTICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 410

Description: An overview of statistical methodologies useful in the practice of Biostatistics. Topics include epidemiology, rates, and proportions, categorical data analysis, regression, and logistic regression, retrospective studies, case-control studies, survival analysis. Real biomedical applications serve as context for evaluating assumptions of statistical methods and models. R serves as the computing software. Graduate/Undergraduate Equivalency: STAT 553. Mutually Exclusive: Cannot register for STAT 453 if student has credit for STAT 553.

Long Title: SPECIAL TOPICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Internship/Practicum, Laboratory, Lecture, Seminar

Credit Hours: 1 TO 4

Description: Topics and credit hours may vary each semester. Contact department for current semester's topic(s). Repeatable for Credit.

Long Title: QUANTITATIVE FINANCIAL ANALYTICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: A modern approach to fundamental analytics of securities, the classic works of Graham and Dodd. Deconstructing the Efficient Market Hypothesis Financial Statement Analysis, Capital Market Theory, CAPM, APT, Fama-French Empirical Financial Forecasting. Graduate/Undergraduate Equivalency: STAT 682. Mutually Exclusive: Cannot register for STAT 482 if student has credit for STAT 682.

Long Title: ENVIRONMENTAL RISK ASSESSMENT & HUMAN HEALTH

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Credit Hours: 3

Prerequisite(s): STAT 180 OR STAT 280 OR STAT 305

Description: Learn and apply quantitative risk assessment methodology to estimate human health risk from environmental exposure to contamination in air, soil and water. Students will conduct a series of team projects focused on toxicology, risk based screening levels, exposure concentration estimation and risk characterization. Cross-list: CEVE 484, Graduate/Undergraduate Equivalency: STAT 684. Mutually Exclusive: Cannot register for STAT 484 if student has credit for STAT 684.

Long Title: ENVIRONMENTAL STATISTICS AND DECISION MAKING

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Credit Hours: 3

Prerequisite(s): STAT 305 OR STAT 385

Description: A project oriented computer intensive course focusing on statistical and mathematical solutions and investigations for the purpose of environmental decisions. This course is the undergraduate version of STAT 685 with reduced requirements. Graduate/Undergraduate Equivalency: STAT 685. Recommended Prerequisite(s): STAT 305 and STAT 385. Mutually Exclusive: Cannot register for STAT 485 if student has credit for STAT 685.

Long Title: MARKET MODELS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 310 OR ECON 307 OR STAT 315 OR DSCI 301 OR ECON 382 OR STAT 312

Description: This course takes the classical efficient market models and superimposes upon it models for other stochastic phenomena not generally accounted for in efficient market theory, showing how risk is lessened by portfolios and other mechanisms. This undergraduate course uses computer simulations as an alternative to closed form solutions. Graduate/Undergraduate Equivalency: STAT 686. Mutually Exclusive: Cannot register for STAT 486 if student has credit for STAT 686.

Long Title: COFES BLOCKCHAIN AND CRYPTOCURRENCIES

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: How will blockchains empower positive and radical change in our increasingly globalized and data-driven society? Students should be prepared for exposure to highly interdisciplinary discussions regarding applying new technology to rethink existing economic & social structures. No technical or engineering experience is required. Graduate/Undergraduate Equivalency: STAT 687.

Long Title: UNDERGRADUATE RESEARCH IN STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Research

Credit Hours: 1 TO 3

Description: This course provides 1-3 credit hours of credit for STAT majors who wish to pursue a research project of mutual interest to the student and a faculty member in a selected area of statistical specialization. The student will conduct independent research under the faculty member’s direction. Repeatable for Credit.

Long Title: INDEPENDENT STUDY

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Independent Study

Credit Hours: 1 TO 6

Description:  Repeatable for Credit.

Long Title: STATISTICS PRACTICUM

Department: Statistics

Grade Mode: Satisfactory/Unsatisfactory

Language of Instruction: Taught in English

Course Type: Internship/Practicum

Credit Hours: 1

Description: Designed for undergraduate statistics majors. The course is to provide experience in real world applications and practice in statistics. An off-campus internship is required. Instructor Permission Required. Repeatable for Credit.

Long Title: RTG CROSS-TRAINING IN DATA SCIENCE

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Seminar

Credit Hours: 1

Description: A seminar course to introduce students to topics in Data Science at the interface between Statistics and Computer Science. Students participate in the process of preparing, delivering and critiquing talks. Topics change each semester. Instructor Permission Required.Cross-list: COMP 496, Graduate/Undergraduate Equivalency: STAT 696. Mutually Exclusive: Cannot register for STAT 496 if student has credit for STAT 696. Repeatable for Credit.

Long Title: TOPICS IN STATISTICAL SCIENCES

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Seminar

Credit Hours: 1 TO 3

Description: This course prepares a student for research in the statistical sciences. Topics may change each semester depending on instructor interest. Current topics include bioinformatics, computational finance, data science, Bayesian methods, mathematical tools for statistics, simulation, optimization, and dependent data, high-dimensional problems, and complex problems. Additional topics may be introduced. Contact the instructor for more information. Graduate/Undergraduate Equivalency: STAT 699. Repeatable for Credit.

Long Title: NEURAL MACHINE LEARNING I

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: Review of major neural machine learning (Artificial Neural Network) paradigms. Analytical discussion of supervised and unsupervised neural learning algorithms and their relation to information theoretical methods. Practical applications to data analysis such as pattern recognition, clustering, classification, function approximation/regression, non-linear PCA, projection pursuit, independent component analysis, with lots of examples from image and digital processings. Details are posted at www.ece.rice.edu/~erzsebet/ANNcourse.html. Cross-list: COMP 502, ELEC 502.

Long Title: TOPICS IN METHODS AND DATA ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: Applications of least squares and general linear mode. Cross-list: POLI 503.

Long Title: ADVANCED PSYCHOLOGICAL STATISTICS I

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Credit Hours: 4

Description: Introduction to inferential statistics, with emphasis on analysis of variance. Students who do not meet registration requirements as Graduate and Psychology or MHCIHF (Master in Human-Computer Interaction and Human Factors) Majors must receive instructor permission to register. Cross-list: PSYC 502.

Long Title: ADVANCED PSYCHOLOGICAL STATISTICS II

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): PSYC 502 OR STAT 509

Description: A continuation of PSYC 502, focusing on multiple regression. Other multivariate techniques and distribution-free statistics are also covered. Cross-list: PSYC 503.

Long Title: INTRODUCTION TO BIOSTATISTICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Credit Hours: 3

Description: Presents basic and advanced methods of statistics as applied to problems in bioengineering. Demonstrates techniques for data organization, exploration, and presentation. Foundations of statistical estimation, inference, and testing are reviewed. Optimal planning of experiments is explored. Advanced techniques include multiple regression, variable selection, logistic regression, analysis of variance, survival analysis, multiple measurements and measurements over time. Additional topics, such as Bayesian methods, will be discussed as time allows. Labs will use the statistical software JMP and/or R. Cross-list: BIOE 514.

Long Title: DATA SCIENCE CONSULTING

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Credit Hours: 3

Description: Students in this course will advise clients from across this Rice community in a data science consulting clinic, learn best practices in consulting, and gain exposure to a variety of real data science problems. Instructor Permission Required.Graduate/Undergraduate Equivalency: STAT 415. Recommended Prerequisite(s): STAT 413 or COMP 440 or COMP 540 or COMP 330 or STAT 411. Mutually Exclusive: Cannot register for STAT 515 if student has credit for STAT 415. Repeatable for Credit.

Long Title: PROBABILITY

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: Topics include random variables, distributions, transformations, moment generating functions, common families of distributions, independence, sampling distributions, and basic stochastic processes. STAT 518 will have more advanced assignments and examinations focusing on theoretical methods. Graduate/Undergraduate Equivalency: STAT 418. Mutually Exclusive: Cannot register for STAT 518 if student has credit for STAT 418.

Long Title: STATISTICAL INFERENCE

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 518

Description: Topics include principles of data reduction, point estimation, hypothesis testing, interval estimation, Bayesian inference, Decision Theory, inference foundations of analysis of variance and regression. STAT 519 will have more advanced assignments and examinations focusing on theoretical methods. Graduate/Undergraduate Equivalency: STAT 419. Mutually Exclusive: Cannot register for STAT 519 if student has credit for STAT 419.

Long Title: APPLIED MULTIVARIATE STATISTICAL METHODS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: This multidisciplinary course for non-STAT majors will address modern applied multivariate statistical methods used in business, economics, engineering, biomedicine, and the environmental and social sciences. Topics include covariance and correlation matrices, multivariate analysis of variance, unsupervised class discovery, cluster validity, supervised class prediction, classifier ensemble diversity. Recommended Prerequisite(s): Introductory statistics course, such STAT 280 or STAT 305 or STAT 310 or STAT 315.

Long Title: BAYESIAN STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: This course covers Bayesian Inference and methods for analyzing data. The emphasis will be on applied data analysis rather than theoretical development. We will consider a variety of models, including linear regression, hierarchical models, and models for categorical data. Recommended Prerequisite(s): STAT 519 and STAT 615 and STAT 605.

Long Title: CAUSAL ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: Correlation is not causation, but what exactly is causation? In this course we will explore the framework statistical science has formalized to approach causation. We will examine the potential outcomes concept, counterfactual reasoning and directed acyclic graph (DAG) models. The course will cover key theorems and results in the field as well as practical estimation and inferential techniques. The course will address instrumental variables as well as exploratory use of causal methods for model building and design of studies. We will survey applications through case studies in various disciplines. After taking this course students will be able to construct causal models, estimate causal effects and distinguish what data are relevant and irrelevant for causal analysis. Students will also be introduced to software techniques using R. Recommended Prerequisite(s): The course is open to all graduate students, but students should be aware that this is a graduate level course in statistics. Students should be performing graduate level research in their respective fields and/or have a background in statistical inference and research methods.

Long Title: FOUNDATIONS OF STATISTICAL INFERENCE I

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 519

Description: The first semester in a two-semester sequence in mathematical statistics: random variables, distributions, small and large sample theorems of decision theory and Bayesian methods, hypothesis testing, point estimation, and confidence intervals; topics such as exponential families, univariate and multivariate linear models, and nonparametric inference will also be discussed. Required for graduate students in statistics.

Long Title: FOUNDATIONS OF STATISTICAL INFERENCE II

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 532

Description: A continuation of STAT 532. Required for Ph.D. students in statistics.

Long Title: DATA SCIENCE PROJECTS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Credit Hours: 3

Description: In this project-based course, student teams will complete semester-long data science research or analysis projects selected from a variety of disciplines and industries. Students will also learn best practices in data science. Instructor Permission Required.Graduate/Undergraduate Equivalency: STAT 435. Mutually Exclusive: Cannot register for STAT 535 if student has credit for STAT 435. Repeatable for Credit.

Long Title: INTERNSHIP IN STATISTICAL MODELING

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Internship/Practicum

Credit Hours: 1 TO 2

Description: Designed for graduate students in statistics. This course introduces current theoretical and applied problems encountered in statistical practice through practical internships. Students will be required to complete a paid or unpaid off-campus internship. MSTAT students will be required to submit a written, 10-15 page report/document summarizing the statistical experience developed during the internship, as well documenting how the internship was instrumental to the Master's in Statistical course of study. Repeatable for Credit.

Long Title: MULTIVARIATE ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 410 OR STAT 615

Description: Study of multivariate data analysis and theory. Topics include normal theory, principal components, factor analysis, discrimination, estimation and hypothesis testing, multivariate analysis of variance and regression clustering.

Long Title: SIMULATION

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 519 AND (STAT 615 OR STAT 410)

Description: Topics in stochastic simulation including; random number generators; Monte Carlo methods, resampling methods, Markov Chain Monte Carlo, importance sampling and simulation based estimation for stochastic processes.

Long Title: GLM & CATEGORICAL DATA ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 519 OR STAT 615 OR STAT 410

Description: Contingency tables, association parameters, chi-squared tests, general theory of generalized linear models, logistics regression, loglinear models, poisson regression.

Long Title: SURVIVAL ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 519 AND STAT 615

Description: Lifetime tables, cumulative distribution theory, censored data, Kaplan-Meier survival curves, log-rank tests, Cox proportional hazards models, parametric and non parametric estimation, hypothesis testing.

Long Title: FUNCTIONAL DATA ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 533 AND STAT 581

Description: Statistical methods for functional data; spaces of functions; pre-processing of functional data; probability models for functional data; basis representations including spline functions, orthogonal bases such as wavelets, and functional principal components; methods of inference for functional data including both frequentist and Bayesian methods.

Long Title: NONPARAMETRIC FUNCTION ESTIMATION

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: Survey of topics in data analysis including data visualization, multivariate density estimation, and nonparametric regression. Advanced applications will include clustering, discrimination, dimension reduction, and bump-hunting using nonparametric density procedures.

Long Title: ADVANCED TOPICS IN TIME SERIES

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 552 OR STAT 621 OR STAT 622

Description: The course will cover current topics in both modeling and forecasting discrete and continuous time series. A brief coverage will also be given to spatial and spatial-temporal processes.

Long Title: APPLIED STOCHASTIC PROCESSES

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 518 OR CMOR 500

Description: This course covers the theory of some of the most frequently used stochastic processes in application: Poisson and renewal processes, discrete-time and continuous-time Markov chains, martingales, Brownian motion and diffusion processes. Cross-list: CMOR 554.

Long Title: BIOSTATISTICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 615

Description: Same as STAT 453 with advanced problem sets. Graduate/Undergraduate Equivalency: STAT 453. Mutually Exclusive: Cannot register for STAT 553 if student has credit for STAT 453.

Long Title: BIOSTATISTICS CONSULTING AND COLLABORATION

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 545 AND STAT 553 AND STAT 615

Description: Students will gain experience by working on real collaborative projects that biostatisticians encounter every day. The goal of the course is to introduce students to projects where statistics and science meet and interact to produce knowledge. The students will learn to work with clinical/basic science collaborators to elicit the scientific question of interest, design studies, identify the correct statistical analyses tools, and communicate the results in both oral and written form. We will also address important topics related to developing productive collaborations, such as building trust and mutual respect, effective communication, participating in multidisciplinary teams and reproducible research. This course is also offered at GSBS/MD Anderson Cancer Center as GS01 1723. Instructor Permission Required. Repeatable for Credit.

Long Title: MATHEMATICAL PROBABILITY I

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: Measure-theoretic foundations of probability. Open to qualified undergraduates. Required for PhD students in statistics. Cross-list: CMOR 552.

Long Title: MATHEMATICAL PROBABILITY II

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 581

Description: Continuation of STAT 581.

Long Title: INTRODUCTION TO RANDOM PROCESSES AND APPLICATIONS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: Review of basic probability; Sequences of random variables; Random vectors and estimation; Basic concepts of random processes; Random processes in linear systems, expansions of random processes; Wiener filtering; Spectral representation of random processes, and white-noise integrals. Cross-list: CMOR 553, ELEC 533.

Long Title: GRADUATE RESEARCH IN STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Research

Credit Hours: 1 TO 15

Description: Research course for graduate level research in probability and statistics. This course provides 1-15 hours of credit for students who wish to pursue a statistical research project of mutual interest to the student and a faculty member. The student will conduct independent research under the faculty member’s direction. Repeatable for Credit. Repeatable for Credit.

Long Title: INDEPENDENT STUDY

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Independent Study

Credit Hours: 1 TO 15

Description: Independent study for graduate level research topics in statistics. It provides credit for independent study in a selected area of statistical specialization. It is intended for directed reading, for conducting independent research, and documentation of conclusions and application of practical internships. Repeatable for Credit.

Long Title: GRADUATE SEMINAR IN STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Seminar

Credit Hours: 1

Description: Students participate in the process of researching professional literature (journal articles, book chapters, dissertations), preparing, delivering and critiquing talks. Literature topics change each semester. Repeatable for Credit.

Long Title: STATISTICS COLLOQUIUM

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Seminar

Credit Hours: 1

Description:  Repeatable for Credit.

Long Title: NEURAL MACHINE LEARNING AND DATA MINING II

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): ELEC 502 OR COMP 502 OR STAT 502

Description: Advanced topics in ANN theories, with a focus on learning high-dimensional complex manifolds with neural maps (Self-Organizing Maps, Learning Vector Quantizers and variants). Application to data mining, clustering, classification, dimension reduction, sparse representation. The course will be a mix of lectures and seminar discussions with active student participation, based on most recent research publications. Students will have access to professional software environment to implement theories. Cross-list: COMP 602, ELEC 602. Repeatable for Credit.

Long Title: GRAPH-THEORETICAL APPROACHES TO KNOWLEDGE DISCOVERY

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: The course will focus on using the power of graph representation and related tools for knowledge discovery from data (structure analysis, pattern recognition, cluster (community) detection). We will review necessary graph theoretical foundations, relevant types of graphs; characterization of graphs by various properties and measures (such as randomness, connectedness, clustering coefficient, similarity.) Topics will include graph segmentation algorithms such as Leading Eigenvector (spectral clustering), Walktrap (Markov-chain random walk), or Infomap, for inferring clusters; and graph matching algorithms for model comparison. We will explore graph representations emerging from vector quantization machine learning processes (e.g, neural maps, K-means), which provide compressed, rich models of complex data relations and facilitate analysis of large data sets with relatively small graphs. We will exercise applications in homeworks and a class project utilizing available graph manipulation software packages. No previous knowledge of Graph Theory is assumed. Students will be assumed to have working knowledge of the following: • Linear algebra (such as in MATH 355, ELEC 301 or equivalent) • Multivariate calculus (such as in MATH 212, MATH 232, or equivalent) • Probability and statistics (such as in STAT 310, or ELEC/STAT 331 or equivalent) Please see additional information on recommended background in the syllabus and at the course web site. Recommended Prerequisite(s): No previous knowledge of Graph Theory is assumed. Students will be assumed to have working knowledge of the following: • Linear algebra (such as in MATH 355, ELEC 301 or equivalent) • Multivariate calculus (such as in MATH 212, MATH 232, or equivalent) • Probability and statistics (such as in STAT 310, or ELEC/STAT 331 or equivalent) If unsure, please check with the instructor. In addition, • If you have taken (ELEC 531 and (CMOR 553 / ELEC 533 / STAT 583) ) or (STAT 615 and STAT 518) you automatically qualify. • Familiarity with simple information theoretical notions (ELEC 241 or equivalent) is an advantage. These will be briefly reviewed in the course as necessary. • Having taken machine learning type courses such as COMP/ELEC/STAT 502, ELEC/COMP 440, STAT 613 is an advantage.

Long Title: COMPUTATIONAL ECONOMICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): ECON 501 AND ECON 502 AND ECON 505 AND ECON 508 AND ECON 510 AND ECON 511 AND MATH 321

Description: Numerical methods most commonly used in economics and their application to frontier research projects in economic modeling. Topics include optimization theory and numerical integration.  Cross-list: ECON 504.

Long Title: R FOR DATA SCIENCE

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Credit Hours: 3

Description: This course introduces students to the statistical programming language, R, and how to use it in statistical and data science problems. The course traces the data science pipeline from importing data into R, exploring and visualizing data, applying a variety of statistical methods, and communicating results. Important computational tools for data science (e.g. databases, web scraping, and big data) and good programming practice are integrated throughout the course. No programming experience is required. STAT 605 includes more advanced assignments and/or examinations than STAT 405. Graduate/Undergraduate Equivalency: STAT 405. Mutually Exclusive: Cannot register for STAT 605 if student has credit for STAT 405.

Long Title: SAS STATISTICAL PROGRAMMING

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Laboratory

Credit Hours: 3

Description: Students will learn how to work within the statistical programming language SAS. The course covers from getting data into SAS, transforming and plotting it, to applying appropriate statistical analysis, and communicating the results. Important topics such as database managing with SQL, macro programming, interactive Matrix Language, and efficient programming in general are integrated throughout the course. Graduate/Undergraduate Equivalency: STAT 406. Mutually Exclusive: Cannot register for STAT 606 if student has credit for STAT 406.

Long Title: COMPUTATION FOR STATISTICAL RESEARCH

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: The course is designed to prepare statistics graduate students for the computational aspects of statistical research. Topics may include but are not limited to: solving linear systems, matrix factorizations, numerical integration, monte carlo integration, markov chain monte carlo, functions and objects, parallel computing, unit testing, graphics, continuous and discrete optimization, sparse matrix operations, parallel computing, R packages. The majority of instruction will be in the R programming language.

Long Title: ECONOMETRICS I

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: Estimation and inference in single equation regression models, multicollinearity, autocorrelated and heteroskedastic disturbances, distributed lags, asymptotic theory, and maximum likelihood techniques.  Emphasis is placed on critical analysis of the literature. Cross-list: ECON 510.

Long Title: ECONOMETRICS II

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): ECON 510 OR STAT 610

Description: Topics in linear and nonlinear simultaneous equations estimation, including panel data, qualitative and categorical dependent variable models, duration analysis, simulation-based estimation, treatment effects, stochastic production frontier estimation. Cross-list: ECON 511.

Long Title: STATISTICAL MACHINE LEARNING

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: This course is an advanced survey of statistical machine learning theory and methods. Emphasis will be placed methodological, theoretical, and computational aspects of tools such as regularized regression, classification, kernels, dimension reduction, clustering, graphical models, trees, and ensemble learning. Recommended Prerequisite(s): STAT 615 and STAT 605 and STAT 519. Mutually Exclusive: Cannot register for STAT 613 if student has credit for ELEC 478.

Long Title: REGRESSION AND LINEAR MODELS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: A survey of regression, linear models, and experimental design. Topics include simple and multiple linear regression, single- and multi-factor studies, analysis of variance, analysis of covariance, model selection, diagnostics. Data analysis using statistical software is emphasized. Mutually Exclusive: Cannot register for STAT 615 if student has credit for STAT 410.

Long Title: ADVANCED STATISTICAL METHODS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 615

Description: Advanced topics in statistical applications such as sampling, experimental design and statistical process control. STAT 616 will have more advanced assignments and examinations focusing on theoretical methods. Graduate/Undergraduate Equivalency: STAT 411. Mutually Exclusive: Cannot register for STAT 616 if student has credit for STAT 411.

Long Title: SPECIAL TOPICS IN STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Seminar

Credit Hours: 1 TO 3

Description: Seminar on advanced topics in Statistics. Repeatable for Credit.

Long Title: APPLIED TIME SERIES AND FORECASTING

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 615

Description: Applied time series modeling and forecasting, with applications to financial markets with advanced problem sets. This is a graduate version of STAT 421 with advanced assignments. The courses STAT 615 and STAT 431 may be taken concurrently with STAT 621 if courses are not in history. Graduate/Undergraduate Equivalency: STAT 421. Mutually Exclusive: Cannot register for STAT 621 if student has credit for STAT 421.

Long Title: PROBABILITY IN BIOINFORMATICS AND GENETICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Credit Hours: 4

Prerequisite(s): STAT 305 OR STAT 310 OR STAT 315 OR DSCI 301 OR STAT 331 OR STAT 418 OR STAT 518

Description: Course introduces the student to modern biotechnology and genomic data. Statistical methods to analyze genomic data are covered, including probability models, basic stochastic processes, and statistical modeling. Biological topics include DNA sequence analysis, phylogenetic inference, gene finding, and molecular evolution. A weekly 1-hour lab on computational aspects of the course will cover software and practice of bioinformatics and computational biology, including modern data sets. Graduate/Undergraduate Equivalency: STAT 423. Mutually Exclusive: Cannot register for STAT 623 if student has credit for STAT 423.

Long Title: ADVANCED BAYESIAN INFERENCE

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 525

Description: This course focuses on the Bayesian inference with emphasis on theory and applications. In this course, we will cover advancements and challenges in modern Bayesian inference, and illustrate a variety of theoretical and computational methods, simulation techniques, and hierarchical models that are suitable to analyze complex data. Repeatable for Credit.

Long Title: STATISTICAL DESIGNS FOR CLINICAL TRIALS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 519 AND STAT 615

Description: This course provides a comprehensive introduction to statistical designs used in clinical trials. It covers key concepts relevant to a biostatistician’s daily responsibilities in trial design, including different types of clinical trials (Phase I to Phase III), (adaptive) randomization methods, and sample size and power calculations. The course will also introduce commonly used design strategies in clinical trials, incorporating both frequentist and Bayesian approaches. Topics include adaptive dose-finding designs, group-sequential designs, sample size re-estimation, and sequential monitoring. Additionally, the course will cover advanced topics such as innovative designs for precision medicine, basket and platform trials, and trials utilizing complex endpoints. MD Anderson cancer trials will be used as motivating and illustrative examples throughout the course. By the end of the course, students are expected to be able to independently design standard clinical trials. No prior knowledge of biology or medicine is required, and basic Bayesian statistical concepts will be introduced.

Long Title: FOUNDATIONS OF STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: Statistical inference is the de facto tool in data science to carry out hypothesis testing and draw conclusions under uncertainty. With increasingly diverse stakeholders relying on inference as data-driven solutions, to study, decipher, and articulate its strength and limitation become more important than ever. In this course, we will discuss fundamental issues in statistical inference, partly in response to a range of daunting challenges posed by modern data science such as reproducibility and interpretability at large scales. A sample of topics includes the use of p-values vs. Bayes factors, frequentist properties of Bayesian procedures for both parametric and nonparametric models, Bernstein von-Mises phenomena, variable/feature selection, post-selection inference, false discovery control. Recommended Prerequisite(s): STAT 532/533 or equivalent courses on classical statistical inference, and STAT525 or equivalent courses on Bayesian inference.

Long Title: GRAPHICAL MODELS AND NETWORKS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 519

Description: Graphical models – aka Bayes networks, Markov networks, Gaussian networks, etc. – have been widely used to represent complex phenomena with dependence. The course aims to stimulate interest in graphical models and covers directed and undirected graphical models, exponential-family representations of graphical models, statistical inference, finite-sample and large-sample properties, and applications.

Long Title: QUANTITATIVE FINANCIAL RISK MANAGEMENT

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 519 OR STAT 615

Description: This course covers the use of financial securities and derivatives to take or hedge financial risk positions. Most commonly used instruments, from simple forwards and futures to exotic options and swaptions are covered. The pricing of derivatives securities will also be studied, but the emphasis will be on the mechanics and uses of financial engineering methods. Students receiving graduate credit in STAT 649 will be expected to address additional homework and test questions targeting a graduate level understanding of the material. Graduate/Undergraduate Equivalency: STAT 449. Mutually Exclusive: Cannot register for STAT 649 if student has credit for STAT 449.

Long Title: STOCHASTIC DIFFERENTIAL EQUATIONS WITH APPLICATIONS TO FINANCE

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 581 OR CAAM 581 OR CMOR 552

Description: This course will cover both theory and applications of stochastic differential equations. Topics include: the Langevin equation from physics, the Wiener process, white noise, the martingale theory, numerical methods and simulation, the Ito and Stratonovitch theories, applications in finance, signal processing, biology, and other fields.

Long Title: SPECIAL TOPICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Independent Study, Internship/Practicum, Laboratory, Lecture, Seminar

Credit Hours: 1 TO 4

Description: Topics and credit hours vary each semester. Contact department for current semester's topic(s). Repeatable for Credit.

Long Title: QUANTITATIVE FINANCIAL ANALYTICS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: A modern approach to fundamental analytics of securities, the classic works of Graham and Dodd. Deconstructing the Efficient Market Hypothesis Financial Statement Analysis, Capital Market Theory, CAPM, APT, Fama-French Empirical Financial Forecasting. Graduate/Undergraduate Equivalency: STAT 482. Mutually Exclusive: Cannot register for STAT 682 if student has credit for STAT 482.

Long Title: ENVIRONMENTAL RISK ASSESSMENT & HUMAN HEALTH

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Credit Hours: 3

Prerequisite(s): STAT 180 OR STAT 280 OR STAT 305

Description: Learn and apply quantitative risk assessment methodology to estimate human health risk from environmental exposure to contamination in air, soil and water. Students will conduct a series of team projects focused on toxicology, risk based screening levels, exposure concentration estimation and risk characterization. Cross-list: CEVE 684, Graduate/Undergraduate Equivalency: STAT 484. Mutually Exclusive: Cannot register for STAT 684 if student has credit for STAT 484.

Long Title: ENVIRONMENTAL STATISTICS AND DECISION MAKING

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture/Laboratory

Credit Hours: 3

Prerequisite(s): STAT 305 OR STAT 385

Description: A project oriented computer intensive course focusing on statistical and mathematical solutions and investigations for the purpose of environmental decisions. This course is required for EADM students. Graduate/Undergraduate Equivalency: STAT 485. Mutually Exclusive: Cannot register for STAT 685 if student has credit for STAT 485.

Long Title: MARKET MODELS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 518 AND (STAT 615 OR STAT 410)

Description: This course takes the classical efficient market models and superimposes upon it models for other stochastic phenomena not generally accounted for in efficient market theory, showing how risk is lessened by portfolios and other mechanisms. This graduate course uses computer simulations as an alternative to closed form solutions with advanced problem sets. Graduate/Undergraduate Equivalency: STAT 486. Mutually Exclusive: Cannot register for STAT 686 if student has credit for STAT 486.

Long Title: COFES BLOCKCHAIN AND CRYPTOCURRENCIES

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: How will blockchains empower positive and radical change in our increasingly globalized and data-driven society? Students should be prepared for exposure to highly interdisciplinary discussions regarding applying new technology to rethink existing economic & social structures. Graduate/Undergraduate Equivalency: STAT 487.

Long Title: RTG CROSS-TRAINING IN DATA SCIENCE

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Seminar

Credit Hours: 1

Description: A seminar course to introduce students to topics in Data Science at the interface between Statistics and Computer Science. Students participate in the process of preparing, delivering and critiquing talks. Topics change each semester. Instructor Permission Required.Cross-list: COMP 696, Graduate/Undergraduate Equivalency: STAT 496. Mutually Exclusive: Cannot register for STAT 696 if student has credit for STAT 496. Repeatable for Credit.

Long Title: TOPICS IN STATISTICAL SCIENCES

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Seminar

Credit Hours: 1 TO 3

Description: This course prepares a student for research in the statistical sciences. Topics may change each semester depending on instructor interest. Current topics include bioinformatics, computational finance, data science, Bayesian methods, mathematical tools for statistics, simulation, optimization, and dependent data, high-dimensional problems, and complex problems. Additional topics may be introduced. Contact the instructor for more information. Graduate/Undergraduate Equivalency: STAT 499. Repeatable for Credit.

Long Title: THESIS

Department: Statistics

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Research

Credit Hours: 1 TO 15

Description: Thesis for Graduate Students. Repeatable for credit. Repeatable for Credit.