Degrees & Requirements 2019

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Undergraduate Program

The Department of Mathematical Sciences has programs leading to the Bachelor of Science in Mathematics. It is flexible and can accommodate students desiring to concentrate in mathematics, applied mathematics, mathematics teaching, or statistics. Programs in these concentrations are designed with the help of faculty advisors.

Statistics Option

Statisticians are trained in principles of quantitative reasoning. They learn how to discover patterns in data, how to display data, how to construct mathematical models for data, and how to detect biases and uncertainties in data summaries or models. They are trained to design and analyze observational studies, surveys, and scientific experiments. The computer is an essential tool for statistical work. Statisticians are in demand; successful students should find that job opportunities are excellent. Although positions are available nationwide, the best employment opportunities are found in urban areas, industrial sites, and centers of government. The statistics option prepares students for such positions or for entry into a graduate program in statistics.

Graduate Programs

The following is a summary of information in the Course Catalog pertaining to a Graduate Certificate in Statistics, a MS degree in Statistics, and a PhD degree in Statistics.

Graduate Certificate in Statistics Criteria

The Graduate Certificate in Statistics is designed to provide additional education in statistical thinking and methodology over and above the basic coursework taken by the typical graduate student. This certificate will provide a clear record on the student’s transcript of credit, to document training in statistics beyond the traditional for the student’s field. This information may be of interest to future graduate programs or employers. The Graduate Certificate will also enhance the credentials of post-baccalaureate students or those currently employed in technical fields. With approval, non-degree-seeking students may earn a graduate certificate in statistics.

Course Requirements  12 Total Credits as follows: STAT 511 & STAT 512 Methods of Data Analysis I & Methods of Data Analysis II; Choose two from the following list, at least one of which must be either STAT 446 or STAT 541.

STAT 446 Sampling; STAT 431 Nonparametric Statistics; STAT 436/536 Introduction to Time Series Analysis; STAT 437 Introduction to Applied Multivariate Analysis; STAT 439 Introduction to Categorical Data Analysis; STAT 448 Mixed Effects Models; STAT 525 Biostatistics; STAT 528 Statistical Quality Control; STAT 541 Experimental Design.             

Obtain a grade of B or better in all coursework counted toward the certificate.

Obtain the approval of the Graduate School, the student's major department, and the Department of Mathematical Sciences.

MS in Statistics Program Guidelines

The Master of Science degree in statistics at Montana State University gives students a solid background in the applications as well as the theory of statistics. Students in this program prepare either for further graduate work or for academic, industrial, business, or government employment. Upon entrance, each student meets with the department's Graduate Program Committee to discuss career objectives and first year course work. During the second semester in the program each student forms a Graduate Committee and together, they outline the student's degree program. The prerequisites for the master's degree program in statistics consist of the following semester courses or their equivalent: Multivariable Calculus (M 273Q), Linear or Matrix Algebra (M 221), Methods of Data Analysis (STAT 411 and STAT 412),  Probability (STAT 421), and Mathematical Statistics (STAT 422). Students who have not completed these courses may be accepted into the master's program with the understanding they should make up these courses by adding one or two additional semesters to their program of study.

Either Plan A (thesis and 20 credits of course work) or Plan B (30 credits of course work) can be chosen. In either case, all courses on a graduate program must be numbered 400 or higher and STAT courses must be numbered 408 or higher. The specific program of study depends on the student's previous training and experience. Regardless of the plan chosen, (i) at least half of the required non-thesis credits must be STAT courses, (ii) at least two-thirds of the required non-thesis credits must be numbered 500 or higher, and (iii) the following core course credits are required.

Required Courses (16 semester credits): STAT 501 & STAT 502 Intermediate Probability and Statistics and Intermediate Mathematical Statistics; STAT 505 & STAT 506 Linear Models and Advanced Regression Analysis; Statistical Consulting Seminar (STAT 510); take two semesters (1 credit a semester); STAT 575 Professional Paper and Project.           

Additional requirements The MS in Statistics degree requires completion of either a thesis or a writing project.

Thesis (Plan A): The Plan A thesis typically requires at least 400 hours of work. The student must register for at least 10 Master's Thesis (STAT 590) in addition to the required 20 credits of course work. The student must give an oral defense of his/her thesis. Writing Project (Plan B): The Plan B writing project typically requires at least 90 hours of work, for which the student earns 2 credits of Professional Paper and Project (STAT 575). With permission from the student's committee, additional credits of STAT 575 (no more than 4 total) may be earned. Students should enroll in STAT 575 in their final Spring semester, and must give a seminar on the writing project before graduating.

Experience in data collection – either through a course such as Sampling (STAT 446) or Experimental Design (STAT 441)/Experimental Design (STAT 541)), or a course taken in a former degree program, or real-life experience.

For either Plan A or Plan B, the student must pass a comprehensive examination.

PhD in Statistics Program Requirements

The PhD program in Statistics at Montana State University prepares students for academic, industrial, business, or government employment. To earn a PhD in Statistics, a student must pass a qualifying exam, pass written and oral PhD comprehensive exams, and write and defend a PhD dissertation. The dissertation must be an original contribution to statistical science and must include new material worthy of publication. There is no departmental foreign language requirement for the PhD

A PhD student typically takes at least 30 credits of statistics in courses numbered 500 and higher. Credits from graduate courses taken from another department can be included in the Program of Study with the approval of the student's PhD Graduate Committee. Additional course work in statistics and/or mathematics may be necessary, depending on the candidate's chosen area of specialization and background. For example, a PhD student is expected to have completed all courses required for the MS degree in statistics and may need to make-up one or more of these courses if deficient.

Once admitted to the PhD program, the PhD student will participate in the Statistical Consulting Seminar (STAT 510, minimum of two credits). Through this participation, the student will gain important experience in practical problem solving, computational statistics and statistical report writing.  Also, it is expected that a PhD student will take a directed study course in Doc Reading & Research (STAT 689) in his/her area of specialty before taking the written and oral comprehensive exams followed by Doctoral Thesis (STAT 690).

Each student must devise areas of concentrated study. The requirements associated with each component are flexible, however the concentration areas must be approved by the student's committee and must include an amount of material equivalent to at least 6 graduate level courses. An area could involve course material from a discipline outside the department.  That is, the PhD Graduate Committee will determine the exact details of each component with the goal of assessing the student’s potential for performing independent research in the proposed research area.

A general review/summary related to the proposed research area. Reading and critiquing at least one journal article related to the proposed research area. Performing a data analysis with a written summary. The data analysis will be related to coursework taken by the student. A component related to Bayesian statistics and/or other relevant coursework determined by the student's PhD Graduate Committee.

Graduate Certificate in Statistics

Training in statistical methods is a required part of the education of graduate students in engineering and the sciences. The Graduate Certificate in Statistics is designed to provide additional education in statistical thinking and methodology over and above the basic coursework taken by the typical graduate student. This transcriptable certificate will provide a clear record of additional training in statistics for future graduate programs or employers. The Graduate Certificate will also be of interest to those currently employed in technical fields that utilize statistics in and to post-baccalaureate students.

Course Requirements

           Current graduate students must:

• Obtain the approval of the department head/graduate coordinator of the student's major department and the Department of Mathematical Sciences.
• Obtain a grade of B or better in all coursework counted toward the certificate.
  
  Non degree seeking students must:
• Obtain the approval of the department head/graduate coordinator of the Department of Mathematical Sciences and the Graduate School.
• Obtain a grade of B or better in all coursework.

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M. S. in Data Science

Program Guidelines⁹

The Master's of Science in Data Science degree at Montana State University is an interdisciplinary program that draws on courses in three programs: Computer Science, Mathematics, and Statistics. The broad goal is to provide students with foundational training in data analysis, with equal emphasis on the principles of computer science, mathematics, and statistics, and the ability to apply these principles to a range of data-driven problems. More specifically, the learning outcomes for graduates of the program are:

• Demonstrate knowledge of essential deterministic, randomized and approximation algorithms for data classification and clustering, dimensionality reduction, regression, and optimization.
• Demonstrate knowledge in the principles and practice of statistical experimental design, statistical inference, and decision theory.
• Demonstrate the ability to take a real-world data analysis problem, formulate a conceptual approach to the problem, match aspects of the problem to previously learned theoretical and methodological tools, break down the solution into a step-by-step approach, and implement a working solution in a modern software language.
• Communicate data science problems, analyses, and solutions effectively to both specialists and non-specialists through the use of effective technical writing, presentations, and data visualizations, and teamwork and collaboration.

Required Courses

There are three essential domains in this program: Computer Science, Statistics, and Mathematics. Each student is required to take:

• At least 2 courses (=6 credits) in each of the three essential domains
• In each domain one of those courses must be the Foundational Course. These foundational courses are:     
  • CSCI 532 (Algorithms),
  • STAT 541 (Experimental Design),
  • M 508 (Mathematical Foundations of Machine Learning)
• Additionally, students can choose among the following courses:
• CSCI 440 (Database Systems), CSCI 540 (Advanced Database Systems), CSCI 446 (Artificial Intelligence), CSCI 447 (Machine Learning: Soft Computing), CSCI 535 (Computational Topology), CSCI 547 (Machine Learning), CSCI 548 (Reasoning Uncertainty), CSCI 550 (Data Mining),
• STAT 408 (Statistical Computing and Graphical Analysis), STAT 511 (Methods of Data Analysis I), STAT 512 (Methods of Data Analysis II), STAT 436 (Introduction to Time Series Analysis) or 536 (Time Series Analysis), STAT 437 (Introduction to Applied Multivariate Analysis) or STAT 537 (Applied Multivariate Analysis I),
• M 441 (Numerical Linear Algebra & Optimization), M 442 (Numerical Solution of Differential Equations), M 507 (Mathematical Optimization)

Screening and Advising

All Applicants should complete and submit the screening and advising form; there is no charge for submitting this form. The form allows the Graduate Program Committee to pre-assess your qualifications and to advise you regarding specific application requirements. Be sure to list all Math and Statistics courses you have completed, including the number of credits and the grade in each course. Providing your GRE scores is optional. International applicants, in addition, need to submit TOEFL scores. Be sure to check which program option you plan to apply for, as well as the semester and year you wish to start. Also, indicate if you want to be considered for a Graduate Teaching Assistantship. Our graduate Program Committee will review this information and let you know if you should pursue the official application process. Applications to our programs are accepted on a rolling basis.⁹

Sample Programs*

Program for a student with a dominant interest in Computer Science, School of Computing:

Program for a student with a dominant interest in Mathematics, Department of Mathematical Sciences

Program for a student with a dominant interest in Statistics, Department of Mathematical Sciences:

_______________
*If students have already taken the suggested courses and have not reserved them for use in this program, then appropriate coursework will be identified, such as STAT 505 (Linear Models) and 506 (Advanced Regression Analysis) if student has completed STAT 511 and 512.

Prerequisites

• 3 semesters of Calculus (through Multivariable Calculus (M 273) or equivalent
• Linear Algebra (M 221) or equivalent
• Data Structures and Algorithms (CSCI 232) or equivalent
• Methods of Proof (M 242) or Discrete Structures (CSCI 246) or equivalent
• Introductory Statistics (STAT 216) or equivalent (additional statistics coursework such as Intermediate Statistical Methods (STAT 217) or STAT 401 and then STAT 511, 512 preferred)
• At least three senior level courses in mathematics, statistics, or computer science or equivalent

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Last revised: 2021-04-19