Graduate Degree Programs

The goals of the master's degree program in statistics are to provide students with a sound foundation in probability, mathematical statistics, and statistical methodology. The degree may be earned under either Plan A (with thesis) or Plan B (without thesis). If Plan A is chosen, a student needs to find a thesis advisor. To distinguish this degree from the degree in Applied Statistics, we refer to it as the "regular MS degree.”

A good background in calculus and linear algebra at the senior undergraduate level
is required for admission. At least one statistics and probability course at the post-calculus level (such as our STT 441-442 courses) is required. Students without these prerequisite courses will generally have to take them as preparatory courses in their first year, with no credit toward the requirements for the degree. Any exception should be approved by the STT MS Advisor or Director of Graduate Programs. Visit MS Program: How to Apply for application instructions.

An academic advisor works with the student to plan their program of study (any exception to the written program must be approved by the chairperson of the Department).  At least 30 credits in courses in the Department of Statistics and Probability or in a field of application of probability or statistics is required. 

Students who maintain a 3.5 cumulative GPA in the Four Core Courses 
STT 861, STT 862, STT 863, and STT 864 will not have to take a master’s exam during the semester the student applies for graduation. It may be either a written or an oral examination, as determined by a department committee. It is mandatory that the students who do NOT maintain a 3.5 in the Four Core Courses, must take the written/oral exam.

Core Courses:
STT 861 Theory of Probability and Statistics I
STT 862 Theory of Probability and Statistics I
STT 863 Statistical Methods 1
STT 864 Statistical Methods II

Strongly Recommended:
STT 801 Design of Experiments
STT 802 Statisical Computation

Electives: At least 9 additional credits in STT courses at the 800 or 900 level and 9 additional credits in other 800 - 900 level STT courses or other related field. Options are listed below. 

Strongly recommended
STT 843 Multivariate Analysis
STT 844 Time Series Analysis
STT 847 Analysis of Survival Data

See below for currently accepted elective courses from other departments for the MS in Statistics degree. For course descriptions and semesters offered, click on the course below or visit MSU Course Descriptions

CEP 921 Psychometric Theory I
CEP 923 Item Response Theory
CEP 934 Multivariate Data Analysis I
CEP 935 Hierarchical Linear Models (HLM)
CSE 802 Pattern Recognition and Analysis
CSE 847 Machine Learning
CSE 881 Data Mining
EC 820 a & b Econometrics
EC 822 a & b Time Series Econometrics
EPI 920 Advanced Methods in Epidemiology and Applied Statistics
GEO 866 Spatial Data Analysis
MTH 844 Projects in Industrial Mathmatics
The department updates this data regularly. For inquiries about other courses as possible electives, please contact the Graduate office, C410 Wells Hall, or email stt.gradoffice@msu.edu.

See the STT Graduate Handbook for details. 

The goals of the master's degree program in applied statistics are to provide students with a broad understanding of the proper application of statistical methodology and with experience in using computers effectively for statistical analysis. Special emphasis is placed on the concerns that an applied statistician must address in dealing with practical problems. 

A good background in calculus and linear algebra at the senior undergraduate level is required for admission. At least one statistics and probability course at the post-calculus level is required. Any exception should be approved by the STT MS Advisor or Director of Graduate Programs. Visit MS Program: How to Apply for application instructions.

An academic advisor works with the student to plan their program of study (any exception to the written program must be approved by the chairperson of the Department).  At least 33 credits in courses in the Department of Statistics and Probability or in a field of application of probability or statistics is required. 

Students who maintain a 3.5 cumulative GPA in the Four Core Courses STT441-442 or STT 861-862, 801, and 863 will not have to take a master’s exam during the semester the student applies for graduation (NOTE: STT 802 is a required core course but not calculated with your GPA of core courses). It may be either a written or an oral examination, as determined by a department committee. It is mandatory that the students who do NOT maintain a 3.5 in the Four Core Courses, must take the written/oral exam.

Core Courses:
STT 441 Probability and Statistics I: Probability
STT 442 Probability and Statistics II: Statistics
or
STT 861 Theory of Probability and Statistics I
STT 862 Theory of Probability and Statistics I
and
STT 801 Design of Experiments
STT 802 Statisical Computation
STT 863 Statistical Methods 1

Electives: At least 9 additional credits in STT courses at the 800 or 900 level and 9 additional credits in other 800 - 900 level STT courses or other related field. Options are listed below. 

Strongly recommended
STT 843 Multivariate Analysis
STT 844 Time Series Analysis
STT 847 Analysis of Survival Data
STT 864 Statistical Methods II

See below for currently accepted elective courses from other departments for the MS in Applied Statistics degree. For course descriptions and semesters offered, click on the course below or visit MSU Course Descriptions

BE 835 Modeling Methods in Biosystems Engineering
CEP 921 Psychometric Theory I
CEP 923 Item Response Theory
CEP 934 Multivariate Data Analysis I
CEP 935 Hierarchical Linear Models (HLM)
CSE 802 Pattern Recognition and Analysis
CSE 847 Machine Learning
CSE 881 Data Mining
EC 820 a & b Econometrics
EC 822 a & b Time Series Econometrics
EPI 809 Biostatistics II
FW 849 Applied Bayesian Inference using Monte Carlo Methods for Quantitative Biologists
GEO 866 Spatial Data Analysis
MTH 844 Projects in Industrial Mathmatics
The department updates this data regularly. For inquiries about other courses as possible electives, please contact the Graduate office, C410 Wells Hall, or email stt.gradoffice@msu.edu.

See the STT Graduate Handbook for details. 

The MS in Data Science program is recruiting students with strong undergraduate backgrounds who have curiosity and technical aptitude. Successful students have come from a wide variety of different backgrounds.  Some of our students are recent graduates, while others have been working in their careers for a substantial amount of time. 
MSDS Student Academic Profiles

Some Calculus and programming background (no specific language) is required.  Some introduction to probability and statistics is strongly recommended.  Multivariable calculus and Linear algebra are not required, but they are greatly helpful in the data science education. To learn how to apply, visit https://msds.msu.edu/prospective-students.aspx

The MSDS is a 30-credit graduate degree, comprised of 18 required credits, 9 elective credits, and a 3-credit capstone course. Please visit the MSU Registrar course search page for MSU catalog course descriptions.

Six required courses (18 credits) for this program are balanced between the three units:

• STT 810, a course on probability and mathematical statistics for data scientists at MS level
• STT 811, a course on applied statistical methodology for data scientists at MS level
• CSE 482, a computer-science course on big data analysis which includes collecting, storing, preprocessing and analyzing large amounts of data.
• CSE 881, a computer-science course on data mining, at MS level.
• CMSE 830, a foundational course on algorithms and methods in Data Science at MS level
• CMSE 831, a foundational course on applied and computational optimization for data scientists, including implementation, at MS level.

9 credits of elective courses draw from a broad set of courses in the three units. Students with the 6 required courses above are well-prepared for taking electives. The list of electives includes the following, and may include other courses approved by the MS DS committee:

• STT 802, statistical computation using the specialized software R.
• STT 812, a compact course on modern statistical data analysis, including statistical learning
• STT 844, a course on time series analysis
• STT 873, a course on statistical learning and data mining
• STT 874, a course on Bayesian analysis
• STT 875, a course on R programming for statistics
• CSE 802, a course on pattern recognition
• CSE 830, a course on the design and analysis of algorithms
• CSE 840, a course on computation foundations of AI
• CSE 847, a course on machine learning
• CSE 849, a course on deep learning
• CMSE/CSE 822, a joint course on parallel computing
• CMSE 402, a course on communication in data science.
• Other CMSE elective courses which are being developed at MSU, some of which are topics courses which have already been taught in CSME, and could be taught jointly with other units. Plans exist for the following topics:
  • CMSE 890 Uncertainty Quantification (has been taught)
  • CMSE 890 Applied Topology (has been taught)
  • CMSE 890 Probabilistic Graphical Models (planned)
  • CMSE 890 Mathematical Image Processing (planned)
  • CMSE 890 Biomedical Science Data (planned)
  • CMSE 890 Applied Machine Learning for Biomedicine (planned)
  • CMSE 890 Computational Methods for Machine Learning (planned)
• Other statistics topics courses STT 890 approved by the MS DS committee.
• Other computer science topics courses CSE 890 approved by the MS DS committee. 
• Any graduate-level MSU course covering data science topics which can be approved by the MS DS committee.

A 3-credit capstone course involves completion of an applied, industrial, or governmental data-science project. Credit for this course can be recorded as one of the three topics courses:

• STT 890
• CSE 890
• CMSE 890

The program is building a portfolio of case studies by featuring capstone projects driven by industry, government, or academia clients.

The Doctor of Philosophy degree program with a major in statistics is designed for students who plan to pursue careers in university teaching and research or in industrial and government research and consulting. A doctoral student pursuing the degree program in statistics may choose to emphasize either statistics or probability.

A master’s level understanding of statistics and probability and a sound understanding of undergraduate-level real analysis are necessary for success in the doctoral program. Strong applicants with deficiencies in one of these areas will be considered for admission, and if accepted, will be given the opportunity to learn the required material during their first year in the program. The Graduate Record Examination (GRE) General Test is required for all applicants. 

To learn how to apply, visit PhD Program: How to Apply

A working knowledge of real analysis is required for successful completion of the Ph.D. program.  Students without sufficient background must take a course in analysis, e.g., MTH 421. 

A student's major advisor or guidance committee chair works with the student to plan their program of study (any exception to the written program must be approved by the chairperson of the Department).

For course descriptions and semesters offered, visit MSU Course Descriptions

STT 867 Linear Model Methodology
STT 868 Mixed Models: Theory, Methods, and Applications
STT 872 Statistical Inference I
STT 873 Statistical Learning and Data Mining
STT 874 Introduction to Bayesian Analysis
STT 881 Theory of Probability I
STT 882 Theory of Probability II
STT 951 Statistical Inference II
STT 953 Asymptotic Theory
STT 961 Weak convergence and Asymptotic Theory
STT 964 Stochastic Analysis
STT 996 Advanced Topics in Probability
STT 997 Advanced Topics in Statistics
STT 999 PhD Dissertation Research Credits (24 minimum; 36 maximum)

 Requirements

1. Complete core courses STT 867, 868, 872, 881, and 882 
2. Complete at least five (5) courses from among (a) and (b):
   (a)  Advanced Probability:  STT 961, 964, 996 (at least 1)
   (b)  Advanced Statistics:  STT 873, 874, 951, 953, 997 (at least 1)
3. Complete at least three (3) additional elective courses offered at the 800-level or higher from any department. These courses must be approved by the student’s guidance committee. NOTE:  STT 996 and STT 997 are special topic courses, which may change from year to year.  Descriptions of courses can be found at https://student.msu.edu.
4. Pass two written preliminary examinations, one in Statistics and one in Probability 

See the STT Graduate Handbook for details. 

The Department of Statistics and Probability (STT) is the primary department. Applicants must be admitted to the STT Ph.D. program before they can be approved for the dual degree program. To learn how to apply, visit PhD Program: How to Apply

Visit these pages for program details. See the MSU Registrar course search page for MSU catalog course descriptions.

Requirements for Dual Major Doctoral Degrees: STT and CMSE 

Requirements for Dual Major Doctoral Degrees: STT and CSE

Requirements for Dual Major Doctoral Degrees: STT and Forestry