Master of Statistics (MStat) Degree

Program Learning Outcomes for the MStat Degree 

Upon completing the MStat degree, students will be able to:

  1. Master fundamental theory in probability and statistics.
  2. Become familiar with a broad range of statistical methods for applications.
  3. Become proficient at statistical computing.
  4. Develop effective communication skills as a professional statistician.

Requirements for the MStat Degree 

The MStat degree is a non-thesis master's degree. For general university requirements, please see Non-Thesis Master's Degrees. For additional requirements, regulations, and procedures for all graduate programs, please see All Graduate Students. Students pursuing the MStat degree must complete:

  • A minimum of 30 credit hours to satisfy degree requirements.
  • A minimum of 30 credit hours of graduate-level study (graduate semester credit hours, coursework at the 500-level or above).
  • A minimum of 24 graduate semester credit hours must be taken at Rice University.
  • A minimum of 24 graduate semester credit hours must be taken in standard or traditional courses (with a course type of lecture, seminar, laboratory, lecture/laboratory). 
  • A minimum residency enrollment of one fall or spring semester of part-time graduate study at Rice University.
  • A maximum of 2 courses (6 graduate semester credit hours) from transfer credit. For additional departmental guidelines regarding transfer credit, see the Policies tab.
  • The requirements of one area of specialization (see below for areas of specialization). The MStat degree program offers five areas of specialization:
    • Applied Statistics for Industry, or
    • Bioinformatics, Statistical Genetics, and Biostatistics, or
    • Financial Statistics and the Statistics of Risk, or
    • Statistical Computing and Data Mining, or
    • Preparation for PhD Studies (in Statistics, Mathematical Economics, Economics, and Finance).
  • A minimum overall GPA of 2.67 or higher in all Rice coursework.
  • A minimum program GPA of 2.67 or higher in all Rice coursework that satisfies requirements for the non-thesis master’s degree.

The courses listed below satisfy the requirements for this degree program. In certain instances, courses not on this official list may be substituted upon approval of the program's academic advisor or, where applicable, the department or program's Director of Graduate Studies. Course substitutions must be formally applied and entered into Degree Works by the department or program's Official Certifier. Additionally, these course substitutions must be approved by the Office of Graduate and Postdoctoral Studies. Students and their academic advisors should identify and clearly document the courses to be taken.

Summary

Total Credit Hours Required for the MStat Degree30

Degree Requirements

Core Requirements 1
STAT 518PROBABILITY3
STAT 519STATISTICAL INFERENCE3
STAT 605R FOR DATA SCIENCE3
STAT 615REGRESSION AND LINEAR MODELS3
STAT 616ADVANCED STATISTICAL METHODS3
Area of Specialization 2
Select a minimum of 2 courses (or up to 5 courses) from any of the following Areas of Specialization:6-15
Applied Statistics for Industry
STAT 525
BAYESIAN STATISTICS
STAT 541
MULTIVARIATE ANALYSIS
STAT 545
GLM & CATEGORICAL DATA ANALYSIS
STAT 616
ADVANCED STATISTICAL METHODS
STAT 685
ENVIRONMENTAL STATISTICS AND DECISION MAKING
STAT 687
COFES BLOCKCHAIN AND CRYPTOCURRENCIES
Bioinformatics, Statistical Genetics, and Biostatistics
STAT 545
GLM & CATEGORICAL DATA ANALYSIS
STAT 547
SURVIVAL ANALYSIS
STAT 553
BIOSTATISTICS
STAT 623
PROBABILITY IN BIOINFORMATICS AND GENETICS
Financial Statistics and the Statistics of Risk
STAT 621
APPLIED TIME SERIES AND FORECASTING
STAT 649
QUANTITATIVE FINANCIAL RISK MANAGEMENT
STAT 682
QUANTITATIVE FINANCIAL ANALYTICS
STAT 686
MARKET MODELS
Statistical Computing and Data Mining
STAT 525
BAYESIAN STATISTICS
STAT 541
MULTIVARIATE ANALYSIS
STAT 542
SIMULATION
STAT 613
STATISTICAL MACHINE LEARNING
Preparation for PhD Studies (in Statistics, Mathematical Economics, Economics, and Finance)
STAT 525
BAYESIAN STATISTICS
STAT 530
CAUSAL ANALYSIS
STAT 532
FOUNDATIONS OF STATISTICAL INFERENCE I
STAT 541
MULTIVARIATE ANALYSIS
STAT 545
GLM & CATEGORICAL DATA ANALYSIS
STAT 552
APPLIED STOCHASTIC PROCESSES
STAT 553
BIOSTATISTICS
STAT 581 / CMOR 552
MATHEMATICAL PROBABILITY I
STAT 613
STATISTICAL MACHINE LEARNING
Elective Requirements
Select up to 9 credit hours of remaining coursework from approved electives in a targeted area of interest to reach 30 total credit hours. 3,40-9
Total Credit Hours30

Footnotes and Additional Information 

Approved Electives

Depending on the student's interest, up to 15 credit hours of area of specialization and elective requirements may be chosen from the following typically approved coursework, in conjunction with the MStat advisor.

Approved Departmental (STAT) Electives0-15
DSCI 515
DATA SCIENCE CONSULTING
DSCI 535 / COMP 549
APPLIED MACHINE LEARNING AND DATA SCIENCE PROJECTS
STAT 502 / COMP 502 / ELEC 502
NEURAL MACHINE LEARNING I
STAT 503 / POLI 503
TOPICS IN METHODS AND DATA ANALYSIS
STAT 509 / PSYC 502
ADVANCED PSYCHOLOGICAL STATISTICS I
STAT 510 / PSYC 503
ADVANCED PSYCHOLOGICAL STATISTICS II
STAT 514 / BIOE 514
INTRODUCTION TO BIOSTATISTICS
STAT 532
& STAT 533
FOUNDATIONS OF STATISTICAL INFERENCE I
and FOUNDATIONS OF STATISTICAL INFERENCE II
STAT 549
FUNCTIONAL DATA ANALYSIS
STAT 550
NONPARAMETRIC FUNCTION ESTIMATION
STAT 551
ADVANCED TOPICS IN TIME SERIES
STAT 552
APPLIED STOCHASTIC PROCESSES
STAT 553
BIOSTATISTICS
STAT 581 / CMOR 552
MATHEMATICAL PROBABILITY I
STAT 582
MATHEMATICAL PROBABILITY II
STAT 583 / CMOR 553 / ELEC 533
INTRODUCTION TO RANDOM PROCESSES AND APPLICATIONS
STAT 602 / COMP 602 / ELEC 602
NEURAL MACHINE LEARNING AND DATA MINING II
STAT 604 / ECON 504
COMPUTATIONAL ECONOMICS
STAT 606
SAS STATISTICAL PROGRAMMING
STAT 610 / ECON 510
ECONOMETRICS I
STAT 611 / ECON 511
ECONOMETRICS II
STAT 613
STATISTICAL MACHINE LEARNING
STAT 623
PROBABILITY IN BIOINFORMATICS AND GENETICS
STAT 630
TOPICS IN CLINICAL TRIALS
STAT 648
GRAPHICAL MODELS AND NETWORKS
STAT 649
QUANTITATIVE FINANCIAL RISK MANAGEMENT
STAT 650
STOCHASTIC CONTROL AND STOCHASTIC DIFFERENTIAL EQUATIONS
STAT 682
QUANTITATIVE FINANCIAL ANALYTICS
STAT 687
COFES BLOCKCHAIN AND CRYPTOCURRENCIES
STAT 699
TOPICS IN STATISTICAL SCIENCES
Approved Electives outside Statistics
BIOE 539
APPLIED STATISTICS FOR BIOENGINEERING AND BIOTECHNOLOGY
BUSI 521 / ECON 505
FINANCIAL ECONOMICS I
BUSI 522
CORPORATE FINANCE
BUSI 523
EMPIRICAL METHODS IN FINANCE
CEVE 678 / MECH 678
APPLIED STOCHASTIC MECHANICS
CEVE 679 / MECH 679
APPLIED MONTE CARLO ANALYSIS
CHBE 615
APPLICATION OF MOLECULAR SIMULATION AND STATISTICAL MECHANICS
CHBE 682 / BIOE 682
SYSTEMS BIOLOGY OF HUMAN DISEASES
CMOR 520
COMPUTATIONAL SCIENCE
CMOR 523
NUMERICAL METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS
CMOR 530
ITERATIVE METHODS FOR SYSTEMS OF EQUATIONS AND UNCONSTRAINED OPTIMIZATION
CMOR 532
OPTIMIZATION THEORY
CMOR 533
NUMERICAL OPTIMIZATION
CMOR 541
LINEAR AND INTEGER PROGRAMMING
COMP 504
GRADUATE OBJECT-ORIENTED PROGRAMMING AND DESIGN
COMP 506
COMPILER CONSTRUCTION FOR GRADUATE STUDENTS
COMP 522
MULTI-CORE COMPUTING
COMP 530
DATABASE SYSTEM IMPLEMENTATION
COMP 533
INTRODUCTION TO DATABASE SYSTEMS
COMP 536 / ELEC 510
SECURE AND CLOUD COMPUTING
COMP 540
STATISTICAL MACHINE LEARNING
COMP 543
GRADUATE TOOLS AND MODELS - DATA SCIENCE
COMP 544
FUNCTIONAL PROGRAMMING
COMP 546 / ELEC 546
INTRODUCTION TO COMPUTER VISION
COMP 554 / ELEC 554
COMPUTER SYSTEMS ARCHITECTURE
COMP 557 / ELEC 557
ARTIFICIAL INTELLIGENCE
COMP 571
MODELING AND INFERENCE IN COMPUTATIONAL GENOMICS
COMP 573
PROFESSIONAL DEVELOPMENT FOR BIOMEDICAL INFORMATICS
COMP 582 / ELEC 512
GRADUATE DESIGN AND ANALYSIS OF ALGORITHMS
COMP 614
COMPUTER PROGRAMMING FOR DATA SCIENCE
ECON 523
DYNAMIC OPTIMIZATION
ECON 547
ADVANCED TOPICS IN ENERGY ECONOMICS
ECON 579
TOPICS IN ECONOMETRICS II
EEPS 651
GEOPHYSICAL DATA ANALYSIS: INVERSE METHODS
ELEC 513 / COMP 513
COMPLEXITY IN MODERN SYSTEMS
ELEC 515
MACHINE LEARNING FOR RESOURCE-CONSTRAINED PLATFORMS
ELEC 531
STATISTICAL SIGNAL PROCESSING
ELEC 535
INFORMATION THEORY
ELEC 571
IMAGING AT THE NANOSCALE
ELEC 575
LEARNING FROM SENSOR DATA
ELEC 578
INTRODUCTION TO MACHINE LEARNING
ELEC 591
GRADUATE ELECTRICAL ENGINEERING RESEARCH PROJECTS-VERTICALLY INTEGRATED PROJECTS
ELEC 677
SPECIAL TOPICS
ENGI 501
WORKPLACE COMMUNICATION FOR PROFESSIONAL MASTER'S STUDENTS IN ENGINEERING
ENGI 610 / NSCI 610
MANAGEMENT FOR SCIENCE AND ENGINEERING
INDE 571
PROBABILITY AND STATISTICAL INFERENCE
INDE 577
DATA SCIENCE AND MACHINE LEARNING
INDE 597
TOPICS IN INDUSTRIAL ENGINEERNG
MATH 517
COMPLEX ANALYSIS
MGMT 595
DATA ANALYSIS
MGMT 597
DATA ANALYSIS II
MGMT 616
ENERGY MARKET ORGANIZATION
MGMT 621
NEW ENTERPRISES
MGMT 638
DATA-DRIVEN INVESTMENTS: EQUITY
MGMT 642
FUTURES AND OPTIONS I
MGMT 645
PORTFOLIO MANAGEMENT
MGMT 648
APPLIED FINANCE
MGMT 650
FUTURES AND OPTIONS II
MGMT 652
MERGERS AND ACQUISITIONS
MGMT 656
ENERGY DERIVATIVES
MGMT 689
DECISION MODELS
MGMT 779
FINANCIAL INCLUSION LAB
PHYS 521
QUANTUM MECHANICS I
PHYS 526
STATISTICAL PHYSICS
PHYS 551
BIOLOGICAL PHYSICS
PHYS 572
FUNDAMENTALS OF QUANTUM OPTICS
PHYS 600
ADVANCED TOPICS IN PHYSICS
PSYC 637
META-ANALYSIS IN PSYCHOLOGICAL RESEARCH

Policies for the MStat Degree

Department of Statistics Graduate Program Handbook

For more detailed information regarding the MStat degree program policies, please see Statistics department's Graduate Handbook, which can be found here: https://gradhandbooks.rice.edu/2024_25/Statistics_Graduate_Handbook.pdf.

Program Restrictions and Exclusions

Students pursuing this degree should be aware of the following program restriction:

  • Courses comprising the 30-credit hour requirement shall not be taken or completed on a pass/fail grading basis.
  • Credit hours earned for engineering practicum, thesis, seminar, independent study courses, or similar variable credit hour courses may not be applied toward MStat degree requirements.

Transfer Credit 

For Rice University’s policy regarding transfer credit, see Transfer Credit. Some departments and programs have additional restrictions on transfer credit. Requests for transfer credit must be approved for Rice equivalency by the appropriate academic department offering the Rice equivalent course (corresponding to the subject code of the course content) and by the Office of Graduate and Postdoctoral Studies (GPS). Students are encouraged to meet with their academic program’s advisor when considering transfer credit possibilities.

Departmental Transfer Credit Guidelines

Students pursuing the MStat degree should be aware of the following departmental transfer credit guideline:

  • No more than 2 courses (6 credit hours) of transfer credit from U.S. or international universities of similar standing as Rice may apply towards the degree.

Additional Information 

For additional information, please see the Statistics website: https://statistics.rice.edu/academics/graduate/master-statistics/.

Opportunities for the MStat Degree

Fifth-Year Master's Degree Option for Rice Undergraduate Students 

In certain situations and with some terminal master's degree programs, Rice students have an option to pursue a master’s degree by adding an additional fifth year to their four years of undergraduate studies.

Advanced Rice undergraduate students in good academic standing typically apply to the master’s degree program during their junior or senior year. Upon acceptance, depending on course load, financial aid status, and other variables, they may then start taking some required courses of the master's degree program. A plan of study will need to be approved by the student's undergraduate major advisor and the master’s degree program director.

As part of this option and opportunity, Rice undergraduate students:

  • must complete the requirements for a bachelor's degree and the master's degree independently of each other (i.e. no course may be counted toward the fulfillment of both degrees).
  • should be aware there could be financial aid implications if the conversion of undergraduate coursework to that of graduate level reduces their earned undergraduate credit for any semester below that of full-time status (12 credit hours).
  • more information on this Undergraduate - Graduate Concurrent Enrollment opportunity, including specific information on the registration process can be found here.

Rice undergraduate students completing studies in science and engineering may have the option to pursue the Master of Statistics (MStat) degree. For additional information, students should contact their undergraduate major advisor and the MStat program director.

Additional Information 

For additional information, please see the Statistics website: https://statistics.rice.edu/academics/graduate/master-statistics/.