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 four areas of specialization:
    • Bioinformatics, Statistical Genetics, and Biostatistics, or
    • Environmental Statistics, or
    • Financial Statistics and the Statistics of Risk, or
    • Statistical Computing and Data Mining.
  • 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 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
Bioinformatics, Statistical Genetics, and Biostatistics
GLM & CATEGORICAL DATA ANALYSIS
SURVIVAL ANALYSIS
BIOSTATISTICS
PROBABILITY IN BIOINFORMATICS AND GENETICS
Environmental Statistics
ENVIRONMENTAL RISK ASSESSMENT & HUMAN HEALTH
ENVIRONMENTAL STATISTICS AND DECISION MAKING
Financial Statistics and the Statistics of Risk
APPLIED TIME SERIES AND FORECASTING
QUANTITATIVE FINANCIAL ANALYTICS
MARKET MODELS
Statistical Computing and Data Mining
BAYESIAN STATISTICS
MULTIVARIATE ANALYSIS
SIMULATION
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. 30-9
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
DATA SCIENCE CONSULTING
APPLIED MACHINE LEARNING AND DATA SCIENCE PROJECTS
NEURAL MACHINE LEARNING I
TOPICS IN METHODS AND DATA ANALYSIS
ADVANCED PSYCHOLOGICAL STATISTICS I
ADVANCED PSYCHOLOGICAL STATISTICS II
INTRODUCTION TO BIOSTATISTICS
FOUNDATIONS OF STATISTICAL INFERENCE I
and FOUNDATIONS OF STATISTICAL INFERENCE II
FUNCTIONAL DATA ANALYSIS
NONPARAMETRIC FUNCTION ESTIMATION
ADVANCED TOPICS IN TIME SERIES
APPLIED STOCHASTIC PROCESSES
BIOSTATISTICS
MATHEMATICAL PROBABILITY I
MATHEMATICAL PROBABILITY II
INTRODUCTION TO RANDOM PROCESSES AND APPLICATIONS
NEURAL MACHINE LEARNING AND DATA MINING II
COMPUTATIONAL ECONOMICS
SAS STATISTICAL PROGRAMMING
ECONOMETRICS I
ECONOMETRICS II
STATISTICAL MACHINE LEARNING
PROBABILITY IN BIOINFORMATICS AND GENETICS
TOPICS IN CLINICAL TRIALS
GRAPHICAL MODELS AND NETWORKS
QUANTITATIVE FINANCIAL RISK MANAGEMENT
STOCHASTIC CONTROL AND STOCHASTIC DIFFERENTIAL EQUATIONS
QUANTITATIVE FINANCIAL ANALYTICS
MATHEMATICAL SCIENCES SEMINAR
Approved Electives outside Statistics
APPLIED STATISTICS FOR BIOENGINEERING AND BIOTECHNOLOGY
FINANCIAL ECONOMICS I
CORPORATE FINANCE
EMPIRICAL METHODS IN FINANCE
ANALYSIS II
COMPUTATIONAL SCIENCE I
NUMERICAL METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS
ITERATIVE METHODS FOR SYSTEMS OF EQUATIONS AND UNCONSTRAINED OPTIMIZATION
OPTIMIZATION THEORY
NUMERICAL OPTIMIZATION
SIGNAL RECOVERY: THEORY AND SIMULATION
LINEAR AND INTEGER PROGRAMMING
APPLIED STOCHASTIC MECHANICS
APPLIED MONTE CARLO ANALYSIS
APPLICATION OF MOLECULAR SIMULATION AND STATISTICAL MECHANICS
SYSTEMS BIOLOGY OF HUMAN DISEASES
GRADUATE OBJECT-ORIENTED PROGRAMMING AND DESIGN
COMPILER CONSTRUCTION FOR GRADUATE STUDENTS
MULTI-CORE COMPUTING
DATABASE SYSTEM IMPLEMENTATION
INTRODUCTION TO DATABASE SYSTEMS
SECURE AND CLOUD COMPUTING
STATISTICAL MACHINE LEARNING
GRADUATE TOOLS AND MODELS - DATA SCIENCE
FUNCTIONAL PROGRAMMING
INTRODUCTION TO COMPUTER VISION
COMPUTER SYSTEMS ARCHITECTURE
ARTIFICIAL INTELLIGENCE
BIOINFORMATICS: SEQUENCE ANALYSIS
PROFESSIONAL DEVELOPMENT FOR BIOMEDICAL INFORMATICS
GRADUATE DESIGN AND ANALYSIS OF ALGORITHMS
COMPUTER PROGRAMMING FOR DATA SCIENCE
DYNAMIC OPTIMIZATION
ADVANCED TOPICS IN ENERGY ECONOMICS
TOPICS IN ECONOMETRICS II
GEOPHYSICAL DATA ANALYSIS: INVERSE METHODS
COMPLEXITY IN MODERN SYSTEMS
MACHINE LEARNING FOR RESOURCE-CONSTRAINED PLATFORMS
STATISTICAL SIGNAL PROCESSING
INFORMATION THEORY
IMAGING AT THE NANOSCALE
LEARNING FROM SENSOR DATA
INTRODUCTION TO MACHINE LEARNING
GRADUATE ELECTRICAL ENGINEERING RESEARCH PROJECTS-VERTICALLY INTEGRATED PROJECTS
SPECIAL TOPICS
FUNDAMENTALS OF MACHINE LEARNING
WORKPLACE COMMUNICATION FOR PROFESSIONAL MASTER'S STUDENTS IN ENGINEERING
MANAGEMENT FOR SCIENCE AND ENGINEERING
BUSINESS AND URBAN ANALYTICS
PROBABILITY AND STATISTICAL INFERENCE
DATA SCIENCE AND MACHINE LEARNING
TOPICS IN INDUSTRIAL ENGINEERNG
COMPLEX ANALYSIS
DATA ANALYSIS
DATA ANALYSIS II
ENERGY MARKET ORGANIZATION
THE NEW ENTERPRISE
QUANTITATIVE INVESTMENT STRATEGIES
FUTURES AND OPTIONS I
PORTFOLIO MANAGEMENT
APPLIED FINANCE
FUTURES AND OPTIONS II
MERGERS AND ACQUISITIONS
ENERGY DERIVATIVES
DECISION MODELS
QUANTUM MECHANICS I
STATISTICAL PHYSICS
BIOLOGICAL PHYSICS
FUNDAMENTALS OF QUANTUM OPTICS
ADVANCED TOPICS IN PHYSICS
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/2021_22/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.

Transfer Credit 

For Rice University’s policy regarding transfer credit, see Transfer Credit. Some departments and programs have additional restrictions on transfer credit. 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 guidelines:

  • 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.
  • Requests for transfer credit will be considered by the program director on an individual case-by-case basis. 

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