Statistics

Statistics
https://statistics.rice.edu/
2103 Duncan Hall
713-348-6032

Marina Vannucci
Department Chair
marina@rice.edu

Rudy Guerra
Associate Department Chair
rguerra@rice.edu

Statistics course work acquaints students with the role played in the modern world by probabilistic and statistical ideas and methods. Students grow familiar with both the theory and the application of techniques in common use as they are trained in statistical research.

The flexibility of the undergraduate program allows students to concentrate on theoretical or applied training, or they may link their studies in statistics to work in other related departments.

The graduate program has concentrations in applied probability, Bayesian methodology, bioinformatics, biomathematics, biostatistics, computational finance, epidemiology, large and complex data, machine and statistical learning, quality control, statistical computing, spatial and, stochastic processes, and time series analysis. Statistics is a cornerstore of the campus wide data science initiative. 

A coordinated MBA/professional Master of Statistics degree also is available in conjunction with the Jesse H. Jones Graduate School of Business.

Master's Programs

Doctoral Program

Coordinated Program

Chair

Marina Vannucci

Professors

Dennis Cox
Katherine B. Ensor
Rudy Guerra
Mark Kimmel
David Scott
James R. Thompson

Assistant Professors

Genevera Allen
Philip Ernst
Daniel Kowal
Meng Li
Michael Schweinberger

Professor in the Practice

John A. Dobelman
Loren Raun

Lecturer

E. Neely Atkinson

Professors, Joint Appointments

Bryan W. Brown
Mahmoud El-Gamal
Don H. Johnson
Krishna Palem
Robin Sickles
Edward E. Williams
Rick K. Wilson

Associate Professors, Joint Appointments

David M. Lane
Barbara Ostdiek

Research Professor

Erzsébet Merényi

Associate Research Professor

Janet Siefert

Adjunct Professors

Donald A. Berry
Barry Brown
Scott Cantor
Kim-Anh Do
Richard Heydorn
Sallie Keller
Suzanne Leal
J. Jack Lee
Jeff Morris
Yu Shen
Peter Thall

Adjunct Associate Professors

Veera Baladandayuthapani
Xuelin Huang
Bonnie Ray
Ying Yuan

Adjunct Assistant Professors

Michele Guindani
Chad A. Shaw
Francesco Stingo
Hadley Wickam

For Rice University degree-granting programs:
To view the list of official course offerings, please see Rice’s Course Catalog
To view the most recent semester’s course schedule, please see Rice's Course Schedule

Statistics (STAT)

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: Graduate level students may not enroll.

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.

Course URL: statistics.rice.edu/feed/Courses.aspx

STAT 281 - HISTORY OF NUMBERS AND GAMES OF CHANCE

Short Title: NUMBER HISTORY/GAMES OF CHANCE

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 3

Restrictions: Graduate level students may not enroll.

Course Level: Undergraduate Lower-Level

Description: Starting with the colorful history of numbers, we discover their use to characterize chance or luck through probability; students will participate in one major project and submit a report-application areas include physics, computer science, sports, finance, etc. The course is accessible to sophomores and juniors in science, engineering or business. Cross-list: COMP 281, ELEC 281.

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: Graduate level students may not enroll.

Course Level: Undergraduate Upper-Level

Prerequisite(s): MATH 101 and MATH 102

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.

Course URL: statistics.rice.edu/feed/Courses.aspx

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: Graduate level students may not enroll.

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. Cross-list: ECON 307. Recommended prerequisite(s): MATH 212.

Course URL: statistics.rice.edu/feed/Courses.aspx

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: Graduate level students may not enroll.

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: UNCERT & RISK IN URBAN INFRAST

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Distribution Group: Distribution Group III

Credit Hours: 3

Restrictions: Graduate level students may not enroll.

Course Level: Undergraduate Upper-Level

Prerequisite(s): STAT 312 or STAT 310 or ECON 307 or ECON 382 or STAT 331 or ELEC 331

Description: Practical applications and relevance of infrastructure risk are developed in the context of real engineering problems and phenomena, including unique systems and challenges of the gulf coast area. The course starts with a survey of the roles of probability in engineering and focuses on computer-based methods, the Bayesian approach, risk analysis tools, and infrastructure safety. Cross-list: CEVE 313. Repeatable for Credit.

STAT 376 - ECONOMETRICS

Short Title: ECONOMETRICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 4

Restrictions: Graduate level students may not enroll.

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: Credit cannot be earned for STAT 376 and 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: Graduate level students may not enroll.

Course Level: Undergraduate Upper-Level

Prerequisite(s): STAT 280 or STAT 305 or STAT 310 or ECON 307 or STAT 312

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

Course URL: statistics.rice.edu/feed/Courses.aspx

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: Graduate level students may not enroll.

Course Level: Undergraduate Upper-Level

Prerequisite(s): STAT 305 or STAT 312 or (STAT 310 or ECON 307 or ECON 382) or STAT 385

Description: Students will learn how to work through data science problems within the statistical programming language R. The course covers the complete analytical process, from getting your data into R, to applying appropriate exploratory and statistical analysis, and communicating the results. Important topics in data science (e.g. databases, web scraping, and big data) and efficient programming are integrated throughout the course. Graduate/Undergraduate Equivalency: STAT 605. Mutually Exclusive: Credit cannot be earned for STAT 405 and STAT 605.

STAT 410 - LINEAR REGRESSION

Short Title: LINEAR REGRESSION

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 4

Restrictions: Graduate level students may not enroll.

Course Level: Undergraduate Upper-Level

Prerequisite(s): STAT 310 or STAT 312 or ECON 307 or ECON 382

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.

Course URL: statistics.rice.edu/feed/Courses.aspx

STAT 411 - ADVANCED STATISTICAL METHODS

Short Title: ADVANCED STATISTICAL METHODS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Graduate level students may not enroll.

Course Level: Undergraduate Upper-Level

Prerequisite(s): (STAT 310 or STAT 312 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: Credit cannot be earned for STAT 411 and STAT 616.

Course URL: statistics.rice.edu/feed/Courses.aspx

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: Graduate level students may not enroll.

Course Level: Undergraduate Upper-Level

Prerequisite(s): STAT 410 and STAT 405 or CAAM 210 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 MATH 354 or MATH 355.

STAT 415 - DATA SCIENCE CONSULTING

Short Title: DATA SCIENCE CONSULTING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture/Laboratory

Credit Hours: 3

Restrictions: Graduate level students may not enroll.

Course Level: Undergraduate Upper-Level

Prerequisite(s): STAT 405 or COMP 140 or CAAM 210

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. Recommended Prerequisite(s): STAT 413 or COMP 440 or COMP 540 or COMP 330 or STAT 411. Mutually Exclusive: Credit cannot be earned for STAT 415 and STAT 515. Repeatable for Credit.

STAT 418 - PROBABILITY

Short Title: PROBABILITY

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Graduate level students may not enroll.

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: Credit cannot be earned for STAT 418 and STAT 518.

STAT 419 - STATISTICAL INFERENCE

Short Title: STATISTICAL INFERENCE

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Graduate level students may not enroll.

Course Level: Undergraduate Upper-Level

Prerequisite(s): (MATH 354 or MATH 355 or CAAM 335) 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: Credit cannot be earned for STAT 419 and 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: Graduate level students may not enroll.

Course Level: Undergraduate Upper-Level

Prerequisite(s): STAT 410

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. Recommended Prerequisite(s): STAT 410. Mutually Exclusive: Credit cannot be earned for STAT 421 and STAT 621.

Course URL: statistics.rice.edu/feed/Courses.aspx

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: Graduate level students may not enroll.

Course Level: Undergraduate Upper-Level

Prerequisite(s): STAT 310 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: Credit cannot be earned for STAT 423 and STAT 623.

Course URL: statistics.rice.edu/feed/Courses.aspx

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: Graduate level students may not enroll.

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 440 - STATISTICS FOR BIOENGINEERING

Short Title: STATISTICS FOR BIOENGINEERING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hour: 1

Restrictions: Graduate level students may not enroll.

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. Offered first five weeks of the semester. BIOE 252 may be taken concurrently with STAT 440. BIOE 440/STAT 440 and BIOE 439 cannot both be taken for credit. Cross-list: BIOE 440. Mutually Exclusive: Credit cannot be earned for STAT 440 and 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: Graduate level students may not enroll.

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 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: Credit cannot be earned for STAT 449 and ECON 449.

STAT 450 - SENIOR CAPSTONE PROJECT

Short Title: SENIOR CAPSTONE PROJECT

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 1-3

Restrictions: Enrollment limited to students with a class of Senior. Enrollment is limited to students with a major in Statistics. Graduate level students may not enroll.

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: Graduate level students may not enroll.

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: Credit cannot be earned for STAT 453 and STAT 553.

Course URL: statistics.rice.edu/feed/Courses.aspx

STAT 470 - FROM SEQUENCE TO STRUCTURE: AN INTRODUCTION TO COMPUTATIONAL BIOLOGY

Short Title: FROM SEQUENCE TO STRUCTURE

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 4

Restrictions: Graduate level students may not enroll.

Course Level: Undergraduate Upper-Level

Description: Contemporary introduction to problems in computational biology spanning sequence to structure. The course has three modules: the first introduces students to the design and statistical analysis of gene expression studies; the second covers statistical machine learning techniques for understanding experimental data generated in computational biology; and the third introduces problems in the modeling of protein structure using computational methods from robotics. The course is project oriented with an emphasis on computation and problem-solving. Cross-list: BIOE 470, COMP 470. Recommended Prerequisite(s): COMP 280 and (STAT 310 or STAT 331).

Course URL: statistics.rice.edu/feed/Courses.aspx

STAT 482 - QUANTITATIVE FINANCIAL ANALYTICS

Short Title: QUANT FINANCIAL ANALYTICS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Graduate level students may not enroll.

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: Credit cannot be earned for STAT 482 and 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: Graduate level students may not enroll.

Course Level: Undergraduate Upper-Level

Prerequisite(s): 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: Credit cannot be earned for STAT 484 and 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: Graduate level students may not enroll.

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: Credit cannot be earned for STAT 485 and STAT 685.

Course URL: statistics.rice.edu/feed/Courses.aspx

STAT 486 - MARKET MODELS

Short Title: MARKET MODELS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Graduate level students may not enroll.

Course Level: Undergraduate Upper-Level

Prerequisite(s): STAT 310 or ECON 307 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: Credit cannot be earned for STAT 486 and STAT 686.

Course URL: statistics.rice.edu/feed/Courses.aspx

STAT 490 - INDEPENDENT STUDY

Short Title: INDEPENDENT STUDY

Department: Statistics

Grade Mode: Standard Letter

Course Type: Independent Study

Credit Hours: 1-6

Restrictions: Graduate level students may not enroll.

Course Level: Undergraduate Upper-Level

Description: Repeatable for Credit.

STAT 491 - INDEPENDENT STUDY

Short Title: INDEPENDENT STUDY

Department: Statistics

Grade Mode: Standard Letter

Course Type: Independent Study

Credit Hours: 6

Restrictions: Graduate level students may not enroll.

Course Level: Undergraduate Upper-Level

Description: 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. Graduate level students may not enroll.

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: Credit cannot be earned for STAT 496 and STAT 696. Repeatable for Credit.

STAT 498 - RESEARCH THEMES IN THE MATHEMATICAL SCIENCES

Short Title: RESEARCH THEMES IN MATH. SCI.

Department: Statistics

Grade Mode: Standard Letter

Course Type: Seminar

Credit Hours: 1-3

Restrictions: Graduate level students may not enroll.

Course Level: Undergraduate Upper-Level

Description: A seminar course that will cover selected theme of general research in the mathematical sciences from the perspectives of mathematics, computational and applied mathematics and statistics. The course may be repeated multiple times for credit. Cross-list: CAAM 498, MATH 498. Graduate/Undergraduate Equivalency: STAT 698. Mutually Exclusive: Credit cannot be earned for STAT 498 and STAT 698. Repeatable for Credit.

STAT 499 - MATHEMATICAL SCIENCES SEMINAR

Short Title: MATHEMATICAL SCIENCES

Department: Statistics

Grade Mode: Standard Letter

Course Type: Seminar

Credit Hours: 1-3

Restrictions: Graduate level students may not enroll.

Course Level: Undergraduate Upper-Level

Description: This course prepares a student for research in the mathematical sciences. Topics will change each semester. Current topics include bioinformatics, biomathematics, computational finance, simulation driven optimization, and data simulation. Each semester may introduce new topics. Graduate/Undergraduate Equivalency: STAT 699. Repeatable for Credit.

Course URL: www.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

Credit Hours: 3

Restrictions: Enrollment limited to students with a class of Graduate. Enrollment is limited to students with a major in 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 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 limited to students with a class of Graduate. 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. Recommended Prerequisite(s): STAT 413 or COMP 440 or COMP 540 or COMP 330 or STAT 411. Mutually Exclusive: Credit cannot be earned for STAT 515 and 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: Credit cannot be earned for STAT 518 and 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: Credit cannot be earned for STAT 519 and STAT 419.

STAT 522 - ADVANCED BAYESIAN STATISTICS

Short Title: ADVANCED 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

Prerequisite(s): STAT 422 or STAT 622

Description: Modern Topics in Bayesian Statistics.

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 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.

Course URL: statistics.rice.edu/feed/Courses.aspx

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.

Course URL: statistics.rice.edu/feed/Courses.aspx

STAT 540 - PRACTICUM IN STATISTICAL MODELING

Short Title: STATISTICAL MODELING

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 2

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

Course Level: Graduate

Description: This course introduces current theoretical and applied problems encountered in statistical practice. The content changes each semester in response to contemporary topics. Designed for graduate students in statistics. Repeatable for Credit.

Course URL: statistics.rice.edu/feed/Courses.aspx

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.

Course URL: statistics.rice.edu/feed/Courses.aspx

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.

Course URL: statistics.rice.edu/feed/Courses.aspx

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.

Course URL: statistics.rice.edu/feed/Courses.aspx

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.

Course URL: statistics.rice.edu/feed/Courses.aspx

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.

Course URL: statistics.rice.edu/feed/Courses.aspx

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.

Course URL: statistics.rice.edu/feed/Courses.aspx

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

Description: This course covers the theory of some of the most frequently used stochastic processes in application; discrete and continuous time, Markov chains, Poisson and renewal processes, and Brownian motion.

Course URL: statistics.rice.edu/feed/Courses.aspx

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: Credit cannot be earned for STAT 553 and STAT 453.

Course URL: statistics.rice.edu/feed/Courses.aspx

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: CAAM 581.

Course URL: statistics.rice.edu/feed/Courses.aspx

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.

Course URL: statistics.rice.edu/feed/Courses.aspx

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: CAAM 583, ELEC 533.

STAT 590 - 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: Repeatable for Credit.

STAT 591 - 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 Graduate level students.

Course Level: Graduate

Description: 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.

Course URL: statistics.rice.edu/feed/Courses.aspx

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 limited to students with a class of Graduate. Enrollment is limited to Graduate level students.

Course Level: Graduate

Description: This course covers numerical methods most commonly used in Economics, including solving systems of equations, numerical optimization, stochastic dynamic programming, numerical differentiation and integration, monte carlo methods, and solving ordinary and partial differential equations. 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: Students will learn how to work through data science problems within the statistical programming language R. The course covers the complete analytical process, from getting your data into R, to applying appropriate exploratory and statistical analysis, and communicating the results. Important topics in data science (e.g. databases, web scraping, and big data) and efficient programming are integrated throughout the course. STAT 605 includes more advanced assignments and/or examinations than STAT 405. Graduate/Undergraduate Equivalency: STAT 405. Mutually Exclusive: Credit cannot be earned for STAT 605 and 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: This course will cover the following: (1) DATA step including arrays, merging, do-loop processing, if then else statements, set statements importing and exporting, space optimization, (2) PROC TABULATE and PROC REPORT, (3) Brief functions survey, e.g. random number generators, character and mathematical functions, time and data functions etc., (4) Formats, (5) Brief survey of statistical PROC's, (6) SAS ODS (Output Delivery System) from statistical procedures, (7) Output datasets from statistical procedures, (8) PROC GRAPH and Statistical Graphics Procedures (SGPLOT, SGPANEL, SGSCATTER), (9) PROC SQL (includes built-in short course on basic SQL), (10) PROC IML including functions, subroutines and optimization etc., (11) Macro programming facility. Priority registration is given to STAT majors.

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 the ability to analyze critically 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 limited to students with a class of Graduate. Enrollment is limited to Graduate level students.

Course Level: Graduate

Description: Topics in linear and nonlinear simultaneous equations estimation, including qualitative and categorical dependent variables models and duration analysis. Applied exercises use SAS and the Wharton Quarterly Econometric Model. 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.

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

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

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: Credit cannot be earned for STAT 616 and 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.

Course URL: statistics.rice.edu/feed/Courses.aspx

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: Credit cannot be earned for STAT 621 and STAT 421.

Course URL: statistics.rice.edu/feed/Courses.aspx

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 331

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: Credit cannot be earned for STAT 623 and STAT 423.

Course URL: statistics.rice.edu/feed/Courses.aspx

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.

Course URL: statistics.rice.edu/feed/Courses.aspx

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 MODELS IN POPULATION DYNAMICS AND POPULATION GENETICS

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

Description: This course is devoted to stochastic models of biological phenomena at cell and molecular lever, using tools of probability and statistics. The course starts with a refresher on conditional expectations and martingale convergence, Markov and point processes, and generating functions and functionals. It continues with branching process models of proliferations and then with Markov chain models of population genetics. Most examples concern growth and nutation of biological cells, with particular emphasis on cancer.

Course URL: statistics.rice.edu/feed/Courses.aspx

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: Credit cannot be earned for STAT 682 and 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 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: Credit cannot be earned for STAT 684 and 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: Credit cannot be earned for STAT 685 and STAT 485.

Course URL: statistics.rice.edu/feed/Courses.aspx

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: Credit cannot be earned for STAT 686 and STAT 486.

Course URL: statistics.rice.edu/feed/Courses.aspx

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: Credit cannot be earned for STAT 696 and STAT 496. Repeatable for Credit.

STAT 698 - RESEARCH THEMES IN THE MATHEMATICAL SCIENCES

Short Title: RESEARCH THEMES IN MATH. SCI.

Department: Statistics

Grade Mode: Standard Letter

Course Type: Seminar

Credit Hours: 1-3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

Description: A seminar course that will cover selected theme of general research in the mathematical sciences from the perspectives of mathematics, computational and applied mathematics and statistics. The course may be repeated multiple times for credit. Cross-list: CAAM 698, MATH 698. Graduate/Undergraduate Equivalency: STAT 498. Mutually Exclusive: Credit cannot be earned for STAT 698 and STAT 498. Repeatable for Credit.

STAT 699 - MATHEMATICAL SCIENCES SEMINAR

Short Title: MATHEMATICAL 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

Description: This course prepares a student for research in the mathematical sciences on a specific topic. Each section is dedicated to a different topic. Current topics include bioinformatics, biomathematics, computational finance, simulation driven optimization, and data simulation. The topics change each semester. Graduate/Undergraduate Equivalency: STAT 499. Repeatable for Credit.

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: Repeatable for Credit.

Description and Code Legend

Note: Internally, the university uses the following descriptions, codes, and abbreviations for this academic program. The following is a quick reference: 

Course Catalog/Schedule 

  • Course offerings/subject code: STAT 

Department Description and Code

  • Statistics: STAT 

Undergraduate Degree Description and Code

  • Bachelor of Arts Degree: BA 

Undergraduate Major Description and Code

  • Major in Statistics: STAT 

Undergraduate Minor Description and Code

  • Minor in Statistics: STAS

Graduate Degree Descriptions and Codes

  • Master of Arts degree: MA
  • Master of Statistics degree: MStat
  • Doctor of Philosophy degree: PhD

Graduate Degree Program Description and Code

  • Degree Program in Statistics: STAT