Statistics (STAT)

STAT 180 - AP/OTH CREDIT IN STATISTICS

Short Title: AP/OTH CREDIT IN STATISTICS

Department: Statistics

Grade Mode: Transfer Courses

Course Type: Transfer

Credit Hours: 4

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Lower-Level

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.

STAT 238 - SPECIAL TOPICS

Short Title: SPECIAL TOPICS

Department: Statistics

Grade Mode: Standard Letter

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

Credit Hours: 1-4

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Lower-Level

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

STAT 280 - ELEMENTARY APPLIED STATISTICS

Short Title: ELEMENTARY APPLIED STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Distribution Group: Distribution Group III

Credit Hours: 4

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Lower-Level

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.

STAT 305 - INTRODUCTION TO STATISTICS FOR BIOSCIENCES

Short Title: INTRO TO STAT FOR BIOSCIENCES

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Distribution Group: Distribution Group III

Credit Hours: 4

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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

STAT 310 - PROBABILITY AND STATISTICS

Short Title: PROBABILITY & STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Distribution Group: Distribution Group III

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

Prerequisite(s): MATH 102 or MATH 106

STAT 311 - HONORS PROBABILITY AND MATHEMATICAL STATISTICS

Short Title: HONORS STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Distribution Group: Distribution Group III

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 312 - PROBABILITY & STATISTICS FOR ENGINEERS

Short Title: PROB & STAT FOR ENGINEERS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Distribution Group: Distribution Group III

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 313 - UNCERTAINTY AND RISK IN URBAN INFRASTRUCTURES

Short Title: RISK-BASED DEC UNDER UNCERT

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Distribution Group: Distribution Group III

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 315 - PROBABILITY AND STATISTICS FOR DATA SCIENCE

Short Title: STATISTICS FOR DATA SCIENCE

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Distribution Group: Distribution Group III

Credit Hours: 4

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 376 - ECONOMETRICS

Short Title: ECONOMETRICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 4

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 385 - METHODS OF DATA ANALYSIS AND SYSTEM OPTIMIZATION

Short Title: METHODS FOR DATA ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Distribution Group: Distribution Group III

Credit Hours: 4

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 405 - R FOR DATA SCIENCE

Short Title: R FOR DATA SCIENCE

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Distribution Group: Distribution Group III

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 406 - SAS STATISTICAL PROGRAMMING

Short Title: SAS STATISTICAL PROGRAMMING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Laboratory

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 410 - LINEAR REGRESSION

Short Title: LINEAR REGRESSION

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 4

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

Prerequisite(s): STAT 310 or STAT 311 or STAT 312 or ECON 307 or ECON 382 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): CAAM 335 or MATH 355.

STAT 411 - ADVANCED STATISTICAL METHODS

Short Title: ADVANCED STATISTICAL METHODS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 413 - INTRODUCTION TO STATISTICAL MACHINE LEARNING

Short Title: INTRO TO STAT MACHINE LEARNING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 415 - DATA SCIENCE CONSULTING

Short Title: DATA SCIENCE CONSULTING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 418 - PROBABILITY

Short Title: PROBABILITY

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 419 - STATISTICAL INFERENCE

Short Title: STATISTICAL INFERENCE

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 421 - APPLIED TIME SERIES AND FORECASTING

Short Title: APPLIED TIME SERIES/FORECASTNG

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 423 - PROBABILITY IN BIOINFORMATICS AND GENETICS

Short Title: PROB BIOINFORMATICS & GENETICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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. Graduate/Undergraduate Equivalency: STAT 623. Mutually Exclusive: Cannot register for STAT 423 if student has credit for STAT 623.

STAT 425 - INTRODUCTION TO BAYESIAN INFERENCE

Short Title: INTRO TO BAYESIAN INFERENCE

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 435 - DATA SCIENCE PROJECTS

Short Title: DATA SCIENCE PROJECTS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 440 - STATISTICS FOR BIOENGINEERING

Short Title: STATISTICS FOR BIOENGINEERING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hour: 1

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

Prerequisite(s): BIOE 252 (may be taken concurrently)

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.

STAT 449 - QUANTITATIVE FINANCIAL RISK MANAGEMENT

Short Title: QUAN FINANCIAL RISK MANAGEMENT

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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 450 - SENIOR CAPSTONE PROJECT

Short Title: SENIOR CAPSTONE PROJECT

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment limited to students with a class of Senior. Enrollment is limited to students with a major in Statistics. Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 453 - BIOSTATISTICS

Short Title: BIOSTATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 477 - SPECIAL TOPICS

Short Title: SPECIAL TOPICS

Department: Statistics

Grade Mode: Standard Letter

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

Credit Hours: 1-4

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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

STAT 482 - QUANTITATIVE FINANCIAL ANALYTICS

Short Title: QUANT FINANCIAL ANALYTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 484 - ENVIRONMENTAL RISK ASSESSMENT & HUMAN HEALTH

Short Title: ENVIRON RISK ASSESS&HUMAN HLTH

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 485 - ENVIRONMENTAL STATISTICS AND DECISION MAKING

Short Title: ENVIR STAT & DECISION MAKING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 486 - MARKET MODELS

Short Title: MARKET MODELS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 487 - COFES BLOCKCHAIN AND CRYPTOCURRENCIES

Short Title: COFES BLOCKCHAIN/CRYPTO

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 490 - UNDERGRADUATE RESEARCH IN STATISTICS

Short Title: UNDERGRADUATE RESEARCH IN STAT

Department: Statistics

Grade Mode: Standard Letter

Course Type: Research

Credit Hours: 1-3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 491 - INDEPENDENT STUDY

Short Title: INDEPENDENT STUDY

Department: Statistics

Grade Mode: Standard Letter

Course Type: Independent Study

Credit Hours: 1-6

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

Description: Repeatable for Credit.

STAT 492 - STATISTICS PRACTICUM

Short Title: STATISTICS PRACTICUM

Department: Statistics

Grade Mode: Satisfactory/Unsatisfactory

Course Type: Internship/Practicum

Credit Hour: 1

Restrictions: Enrollment is limited to students with a major in Statistics. Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 496 - RTG CROSS-TRAINING IN DATA SCIENCE

Short Title: RTG CROSS-TRAINING IN DATA SCI

Department: Statistics

Grade Mode: Standard Letter

Course Type: Seminar

Credit Hour: 1

Restrictions: Enrollment is limited to students with a major in Computer Science or Statistics. Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

STAT 499 - TOPICS IN STATISTICAL SCIENCES

Short Title: TOPICS IN STATISTICAL SCIENCES

Department: Statistics

Grade Mode: Standard Letter

Course Type: Seminar

Credit Hours: 1-3

Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students.

Course Level: Undergraduate Upper-Level

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.

Course URL: statistics.rice.edu

STAT 502 - NEURAL MACHINE LEARNING I

Short Title: NEURAL MACHINE LEARNING I

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

Course URL: www.ece.rice.edu/~erzsebet/ANNcourse.html

STAT 503 - TOPICS IN METHODS AND DATA ANALYSIS

Short Title: TOPICS METHODS&DATA ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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

STAT 509 - ADVANCED PSYCHOLOGICAL STATISTICS I

Short Title: ADVANCED PSYC STATISTICS I

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 4

Restrictions: Enrollment is limited to students with a major in Human-Comp Inter & Humn Factrs or Psychology. Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 510 - ADVANCED PSYCHOLOGICAL STATISTICS II

Short Title: ADVANCED PSYC STATISTICS II

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 514 - INTRODUCTION TO BIOSTATISTICS

Short Title: INTRODUCTION TO BIOSTATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 3

Restrictions: Enrollment is limited to students with a major in Bioengineering. Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 515 - DATA SCIENCE CONSULTING

Short Title: DATA SCIENCE CONSULTING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 518 - PROBABILITY

Short Title: PROBABILITY

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 519 - STATISTICAL INFERENCE

Short Title: STATISTICAL INFERENCE

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 520 - APPLIED MULTIVARIATE STATISTICAL METHODS

Short Title: APPLIED MULTIVAR STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 525 - BAYESIAN STATISTICS

Short Title: BAYESIAN STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 530 - CAUSAL ANALYSIS

Short Title: CAUSAL ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 532 - FOUNDATIONS OF STATISTICAL INFERENCE I

Short Title: FOUNDATIONS OF STAT INF I

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 533 - FOUNDATIONS OF STATISTICAL INFERENCE II

Short Title: FOUNDATIONS OF STAT INF II

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

Prerequisite(s): STAT 532

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

STAT 535 - DATA SCIENCE PROJECTS

Short Title: DATA SCIENCE PROJECTS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 540 - INTERNSHIP IN STATISTICAL MODELING

Short Title: PRACTICUM IN STAT & DATA SCI

Department: Statistics

Grade Mode: Standard Letter

Course Type: Internship/Practicum

Credit Hours: 1-2

Restrictions: Enrollment is limited to students with a major in Statistics. Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 541 - MULTIVARIATE ANALYSIS

Short Title: MULTIVARIATE ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 542 - SIMULATION

Short Title: SIMULATION

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 545 - GLM & CATEGORICAL DATA ANALYSIS

Short Title: GLM & CATEG'L DATA ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 547 - SURVIVAL ANALYSIS

Short Title: SURVIVAL ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 549 - FUNCTIONAL DATA ANALYSIS

Short Title: FUNCTIONAL DATA ANALYSIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 550 - NONPARAMETRIC FUNCTION ESTIMATION

Short Title: NONPARAMETRIC FUNCTION EST

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 551 - ADVANCED TOPICS IN TIME SERIES

Short Title: ADVANCED TOPICS IN TIME SERIES

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 552 - APPLIED STOCHASTIC PROCESSES

Short Title: APPLIED STOCHASTIC PROCESSES

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 553 - BIOSTATISTICS

Short Title: BIOSTATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 555 - BIOSTATISTICS CONSULTING AND COLLABORATION

Short Title: BIOSTAT CONSULTG & COLLAB

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

Course URL: statistics.rice.edu

STAT 581 - MATHEMATICAL PROBABILITY I

Short Title: MATHEMATICAL PROBABILITY I

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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

STAT 582 - MATHEMATICAL PROBABILITY II

Short Title: MATHEMATICAL PROBABILITY II

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

Prerequisite(s): STAT 581

Description: Continuation of STAT 581.

STAT 583 - INTRODUCTION TO RANDOM PROCESSES AND APPLICATIONS

Short Title: INTRO RANDOM PROCESSES & APPL

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 590 - GRADUATE RESEARCH IN STATISTICS

Short Title: GRAD RESEARCH IN STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Research

Credit Hours: 1-15

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 591 - INDEPENDENT STUDY

Short Title: INDEPENDENT STUDY

Department: Statistics

Grade Mode: Standard Letter

Course Type: Independent Study

Credit Hours: 1-15

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 600 - GRADUATE SEMINAR IN STATISTICS

Short Title: GRADUATE SEMINAR IN STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Seminar

Credit Hour: 1

Restrictions: Enrollment is limited to students with a major in Statistics. Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 601 - STATISTICS COLLOQUIUM

Short Title: STATISTICS COLLOQUIUM

Department: Statistics

Grade Mode: Standard Letter

Course Type: Seminar

Credit Hour: 1

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

Description: Repeatable for Credit.

STAT 602 - NEURAL MACHINE LEARNING AND DATA MINING II

Short Title: NEURAL MACHINE LEARNING II

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

Course URL: www.ece.rice.edu/~erzsebet/NMLcourseII.html

STAT 604 - COMPUTATIONAL ECONOMICS

Short Title: COMPUTATIONAL ECONOMICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 605 - R FOR DATA SCIENCE

Short Title: R FOR DATA SCIENCE

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 606 - SAS STATISTICAL PROGRAMMING

Short Title: SAS STATISTICAL PROGRAMMING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Laboratory

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 607 - COMPUTATION FOR STATISTICAL RESEARCH

Short Title: STATISTICAL COMPUTING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 610 - ECONOMETRICS I

Short Title: ECONOMETRICS I

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 611 - ECONOMETRICS II

Short Title: ECONOMETRICS II

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 613 - STATISTICAL MACHINE LEARNING

Short Title: STAT MACHINE LEARNING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 615 - REGRESSION AND LINEAR MODELS

Short Title: REGRESSION AND LINEAR MODELS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

Course URL: ece.rice.edu/~erzsebet/STAT615.html

STAT 616 - ADVANCED STATISTICAL METHODS

Short Title: ADVANCED STATISTICAL METHODS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 620 - SPECIAL TOPICS

Short Title: SPECIAL TOPICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Seminar

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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

STAT 621 - APPLIED TIME SERIES AND FORECASTING

Short Title: APPLIED TIME SERIES/FORECASTNG

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

Prerequisite(s): STAT 615 (may be taken concurrently)

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.

STAT 623 - PROBABILITY IN BIOINFORMATICS AND GENETICS

Short Title: PROB BIOINFORMATICS & GENETICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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. Graduate/Undergraduate Equivalency: STAT 423. Mutually Exclusive: Cannot register for STAT 623 if student has credit for STAT 423.

STAT 625 - ADVANCED BAYESIAN INFERENCE

Short Title: ADVANCED BAYESIAN INFERENCE

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 630 - TOPICS IN CLINICAL TRIALS

Short Title: TOPICS IN CLINICAL TRIALS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

Prerequisite(s): STAT 519 and STAT 615

Description: This course deals with fundamental concepts in the design of clinical studies, ranging from early dose-finding studies (phase I) to screening studies (phase II) to randomized comparative studies (phase III). The goal is to prepare the student to read the clinical trial literature critically and to design clinical studies. Additionally, the faculty will introduce newer designs for clinical studies that incorporate prior knowledge and/or satisfy optimality considerations. Topics include protocol writing; randomization; sample size calculation; study design options; interim monitoring; adaptive designs; multiple end points; and writing up the results of a clinical trial for publication.

STAT 635 - FOUNDATIONS OF STATISTICS

Short Title: FOUNDATIONS OF STATISTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 648 - GRAPHICAL MODELS AND NETWORKS

Short Title: GRAPH MODELS & NETWORKS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 649 - QUANTITATIVE FINANCIAL RISK MANAGEMENT

Short Title: QUAN FINANCIAL RISK MANAGEMENT

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 650 - STOCHASTIC CONTROL AND STOCHASTIC DIFFERENTIAL EQUATIONS

Short Title: STOCH CONTRL & STOCH DIFF EQU

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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, materials science, biology, and other fields.

STAT 677 - SPECIAL TOPICS

Short Title: SPECIAL TOPICS

Department: Statistics

Grade Mode: Standard Letter

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

Credit Hours: 1-4

Restrictions: Enrollment is limited to Graduate or Visiting Graduate level students.

Course Level: Graduate

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

STAT 682 - QUANTITATIVE FINANCIAL ANALYTICS

Short Title: QUANT FINANCIAL ANALYTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 684 - ENVIRONMENTAL RISK ASSESSMENT & HUMAN HEALTH

Short Title: ENVIRON RISK ASSESS&HUMAN HLTH

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 685 - ENVIRONMENTAL STATISTICS AND DECISION MAKING

Short Title: ENVIR STAT & DECISION MAKING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 686 - MARKET MODELS

Short Title: MARKET MODELS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 687 - COFES BLOCKCHAIN AND CRYPTOCURRENCIES

Short Title: COFES BLOCKCHAIN/CRYPTO

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

STAT 696 - RTG CROSS-TRAINING IN DATA SCIENCE

Short Title: RTG CROSS-TRAINING IN DATA SCI

Department: Statistics

Grade Mode: Standard Letter

Course Type: Seminar

Credit Hour: 1

Restrictions: Enrollment is limited to students with a major in Computer Science or Statistics. Enrollment is limited to Graduate level students.

Course Level: Graduate

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.

STAT 699 - TOPICS IN STATISTICAL SCIENCES

Short Title: TOPICS IN STATISTICAL SCIENCES

Department: Statistics

Grade Mode: Standard Letter

Course Type: Seminar

Credit Hours: 1-3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

Course URL: www.statistics.rice.edu

STAT 800 - THESIS

Short Title: THESIS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Research

Credit Hours: 1-15

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

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