Graduate Programs and Careers

General Graduate Studies Information

Doctoral Programs

Pure and Applied

A traditional doctoral program in mathematics, offering broad training in mathematical foundations and specialized training in a research area for future faculty in universities and colleges or careers in industry and government research agencies.

The Pure and Applied Mathematics Specialization requires at least 60 credits in mathematics numbered 6000 or higher, excluding MATH 6990 and MATH 7990. At least 6 credits must be selected from seminars or classes numbered 7000 or higher, and no more than 30 of the credits can be completed in MATH 7970 (Dissertation Research). The qualifying examination for this option is in Real Analysis. The dissertation should be a publishable, significant contribution to research in an area of mathematics. PhD students in the "Pure and Applied Mathematics" specialization must obtain a minimum grade of "B" or better in at least two of the 6000-level topic tracks listed in Appendix C of the Graduate Program Handbook.

Statistics

Data science, theoretical and applied statistics for students seeking careers in academia, industry, or government.

The Statistics Specialization requires at least 60 credits in statistics at the 6000 and 7000 level, excluding STAT 6990 and STAT 7990. With the permission of the student’s supervisory committee, some of these credits may be in mathematics or in another discipline. At least 6 credits must be selected from seminars and classes numbered 7000 and higher, and a maximum of 30 credits may be completed in STAT 7970 (Dissertation Research). Students in this specialization take a qualifying examination in Probability and Mathematical Statistics. The dissertation constitutes a publishable, significant contribution to research in statistics.

Interdisciplinary Studies

Advanced training in mathematics and/or statistics in the context of another field of inquiry, such as biology, ecology, business, economics, engineering, or education. For faculty and research careers crossing boundaries between disciplines.

The Interdisciplinary Studies Specialization requires at least 60 credits numbered 6000 or higher, excluding MATH 7990, STAT 7990, MATH 6990, and STAT 6990. No more than 30 of the credits may be completed in MATH 7970 or STAT 7970 (Dissertation Research). At least 20 of the credits should be in mathematics and/or statistics, of which at least 6 should be in seminars and classes at the 7000 level. An additional 10 credits in the student’s chosen interdisciplinary area are also required. Students in this specialization may take a qualifying examination in Real Analysis or in Probability and Mathematical Statistics, depending on whether the majority of their coursework is in mathematics or in statistics. The student’s PhD supervisory committee should include two persons in the student’s selected interdisciplinary area, and the comprehensive examination should have a significant interdisciplinary component. The dissertation for a student in this specialization should involve the development and application of mathematical or statistical methods to solve problems in the chosen interdisciplinary area, and should be publishable in journals in that area.

Masters Programs

Mathematics

Prepares students to work as mathematicians in government, business, and industry and serves as a “stepping stone” to doctoral programs in mathematics or related subjects.

This program prepares students to work as mathematicians in government, business, and industry. This degree may also be a “stepping stone” for students who ultimately wish to pursue a doctorate in mathematics or a closely related subject.

This degree requires 30 credits of approved coursework at or above the 5000 level. At least 18 of these credits must be at the 6000 level or above, excluding MATH 6990 and MATH 7990 (Continuing Graduate Advisement). Generally, most of the coursework will be in mathematics, but the student’s supervisory committee may approve courses in statistics, physics, engineering, or any other discipline, if it seems such coursework is appropriate for the student’s program of study.

Students in the MS Mathematics program must obtain a minimum grade of "B" or better in at least two topic tracks from the 5000-level or the 6000-level topic tracks listed in Appendix C of the Graduate Program Handbook. MS Mathematics students may substitute one topic track if they can document they have had something equivalent in a previous degree program.

The MS in mathematics has two options. The Plan A or the thesis option requires taking 6 credits of MATH 6970 (Thesis and Research) and working with a faculty member on a substantial research project. The research must be presented in a thesis, which must be approved by the student’s supervisory committee and the dean of the School of Graduate Studies. An oral defense of the thesis must be arranged through the School of Graduate Studies.

The Plan B or project option requires taking 3 credits of MATH 6970 and working with a faculty member on a smaller research project. A written report of the research must be approved by the student’s supervisory committee. An oral defense of the report must be scheduled through the School of Graduate Studies.

All students in the MS program in Mathematics must pass a written qualifying examination covering the introductory analysis and advanced calculus material presented in MATH 4200, MATH 5210, and MATH 5220. Students may take this exam before beginning formal coursework in the MS program, and must take the exam at the end of the first full year of matriculation. The exam is typically given twice a year, in May and October. Matriculated students who fail on their first try must pass the exam at the next scheduled opportunity. A detailed exam syllabus is contained in the Graduate Handbook, available from the department.

Statistics

Applied statistics and data science for careers in government, business, and industry, as well as preparation for a variety of doctoral programs (statistics, computer science, economics, biology, ecology, natural resources).

This program is primarily designed to prepare students for careers in business, industry, and federal, state, and local government. Students pursuing graduate degrees in other disciplines, such as biology, natural resources, engineering, business, economics, epidemiology, and the social sciences, may elect to earn an MS in statistics concurrent with their other degree programs. For most students, the MS in statistics will prove sufficient for career preparation. However, some graduates may ultimately pursue a doctorate in statistics, biostatistics, or a closely related discipline.

This degree requires 30 credits of approved coursework at or above the 5000 level. At least 18 credits must be at the 6000 level or above, excluding STAT 6990 and STAT 7990 (Continuing Graduate Advisement). All students must pass either the Math 5710/Math 5720 sequence or the STAT 6710/STAT 6720 sequence (Mathematical Statistics I and II). Generally, most of the coursework will be in statistics, but the student’s supervisory committee may approve courses in mathematics, biology, economics, or any other discipline if it deems such coursework to be appropriate for the student’s program of study.

The MS in Statistics has Plan A (thesis) and Plan B (report) options.. The Plan A and Plan B options require students to work with a faculty member on a research project, taking 6 or 3 credits of STAT 6970, respectively, and presenting the results of the research in a written report. For both the Plan A and Plan B options, the report must be approved by the student’s supervisory committee. A Plan A report (thesis) must also be approved by the dean of the School of Graduate Studies. Both Plan A and Plan B reports require an oral defense that must be scheduled through the School of Graduate Studies.

There is no qualifying examination for students in the MS program in Statistics. The qualifying requirement is that students must earn a B or better for both semesters of either the MATH 5710/MATH 5720 sequence or the STAT 6710/STAT 6720 sequence.

Industrial Mathematics

Applied and computational mathematics and statistics for students seeking employment in government, business, and industry.

The Industrial Mathematics master’s degree is designed to broaden the learning experiences and job opportunities for master’s students in mathematics. The program of study incorporates fundamental applied mathematics and interdisciplinary coursework in support of an industrial internship experience.

This degree requires 36 credits at or above the 5000 level -- 30 credits in coursework, 3 credits for research (6970, plan B), and 3 credits for internship (6250). A maximum of 3 of these credits may be taken at the 5000-level (i.e., one 3-credit course in another department). See the departmental website or the Graduate Handbook for more detailed information about coursework requirements.

Students are also required to complete a final project based on work done during an internship, either with a company or possibly with another department on campus. The project will include a technical write-up suitable to the industry/field, and presentation to the involved faculty and students in the program. This follows the Plan B option listed for the Master of Science in Mathematics degree.

Masters of Mathematics

A Broad background in mathematics, statistics and pedagogy designed specifically for secondary teachers.

This program is designed specifically for secondary school teachers of mathematics. The purpose of this degree is to provide students with a broad background in mathematics.

This program requires at least 36 credits approved by the Graduate Committee within the Department of Mathematics and Statistics. At least 21 of these credits must come from mathematics classes numbered above 5000, and the remaining credits must be chosen from approved courses offered within the Emma Eccles Jones College of Education and Human Services. The GPA for the 36 credits and for the 21 math credits must be at least 3.0.

All students in the Master of Mathematics program must pass a written qualifying examination. They may take the Advanced Calculus Exam, covering the introductory analysis and advanced calculus material presented in MATH 4200, MATH 5210, and MATH 5220, or the qualifying exam in Mathematics Teaching. Students may take these exams before beginning formal coursework in the program, but must take these exams before the end of the first year of matriculation. The Advanced Calculus exam is typically given twice a year, in May and October, while the Mathematics Teaching exam is given in May. Matriculated students who fail on their first try must pass the exam at the next scheduled opportunity.

Masters of Data Analytics

Training the next generation of statisticians, business analysts, and computer scientists to meet the demand for individuals with data management and analysis skills in Utah and the United States.

Visit the full program page

Do you love using quantitative skills to make a difference? If you are interested in big data, data science, or analytics, and have an undergraduate background in statistics, computer science, economics, or another related quantitative field, we'd love to have the chance to work with you and help you achieve your potential in this exciting degree program!

This professional M.Data program integrates coursework in four departments (Mathematics and Statistics, Management Information Systems, Economics and Finance, and Computer Science) to give graduates a broad but focused collection of tools for the management and analysis of data, particularly Big Data.

Suggested preparation for this program is coursework in a quantitative field such as statistics or management information systems, along with some programming training involving algorithms and data structures. Previous work experience is not required, but would definitely be helpful if it involved data analytics or database interaction.

This program requires at least 33 credits total, including 17 credits from the program core. The remaining 16 credits are determined by the emphasis or track a student chooses – Statistics, Management Information Systems, or Economics. In addition, students are expected to already have the equivalent of STAT 5100 Linear Regression and Time Series; if not, students will need to take STAT 5100, and their program of study will include 36 credits. A student’s committee may approve substitutions for elective courses from another track.

M.Data Program Core: CS 3430 Computational Science: Python and Perl Programming [3 cr]; STAT 5050 Introduction to R [1 cr]; STAT 5650 Statistical Learning and Data Mining I [2 cr]; STAT 5560 Statistical Visualization I [2 cr]; ECN 5330 Applied Econometrics [3 cr]; MIS 6230 Database Management [3 cr]; plus a 6000-level STAT/MIS/ECN Capstone Project and Internship in Data Analytics [3 cr].

M.Data Statistics emphasis: (in addition to the 17-credit program core) Each of STAT 5080 Data Technologies, STAT 5150 SAS Predictive Analytics, SAS 5680 Statistical Thinking for Big Data, STAT 6560 Statistical Visualization II, and STAT 6650 Statistical Learning and Data Mining II. Choose two elective courses from STAT 5120 Categorical Data Analysis, STAT 5410/6410 Applied Spatial Statistics, STAT 5500/6500 Biostatistics Methods, STAT 5570/6570 Statistical Bioinformatics, STAT 6100 Advanced Regression Analysis, CS 5665 Introduction to Data Science, CS 5810 Applied Data Science Incubator, CS 6665 Data Mining, or CS 6675 Advanced Data Science and Data Mining.

M.Data Management Information Systems emphasis: (in addition to the 17-credit program core) Each of MIS 5150 Emerging Technologies: Data Cleansing, MIS 5150 Emerging Technologies: Tableau Business, MIS 6500 Advanced Business Intelligence and Data Mining. Choose three elective courses from STAT 5080 Data Technologies, STAT 5150 SAS Predictive Analytics, MIS 6330 Database Implementation, or CS 5664 Introduction to Data Science.

M.Data Economics emphasis: (in addition to the 17-credit program core). Each of ECN 5600 Financial Economics and FIN 6320 Computational Methods. Choose four elective courses from FIN 5100 Financial Markets and Training, FIN 6300 Fixed Income*, FIN 6460 Investment Analysis*, FIN 6470 Derivatives Markets*, ECN 7310 Econometrics I, ECN 7320 Econometrics II, STAT 5080 Data Technologies, or STAT 5150 SAS Predictive Analytics. (* - must select at least one of these)

Summary Requirements

Program Credits Exam or Other Qualifying Requirements Research
M.S. Mathematics 30 credits(at least 18 credits ata 6000 level or above including research) Comprehensive exam in Advanced Calculus, and B or better in at least two topic tracks Plan A: 6 credits research Plan B: 3 credits research
M.S. Statistics 30 credits(at least 18 credits ata 6000level or above including research) B or better for both semester of either Math 5710 & 5720 or STAT 6710 & 6720. Plan A: 6 credits research Plan B: 3 credits research
M.S. Industrial Mathematics 36 credits (at least 15 credits at 6000 level including reseach, 9 credits outside Math & Stat) B or better in one of Math 5410 or 5420, one of Math 5610 or 5620 and STAT 5100. Professional internship (MATH 6250) also required. Plan B: 3 credits research
Master of Mathematics 36 credits Comprehensive exam inMathematics Teaching or Comprehensive exam in Advanced Calculus No research credits required

Master of Data Analytics

33 credits (plus STAT 5100 if not already taken; 17 credits from program core,and 16 from emphasis area

No exam, but a Capstone Project and Internship in Data Analytics is required of all students.

No research credits required.
Ph.D. 45 with prior Masters 72 without prior Masters, (no more than 15 at 5000 level) Comprehensive exam determined by committee; for "Pure and Applied" and "Interdisciplinary Studies" options, also B or better in at least two 6000-level topic tracks 18 - 24 credits research

Graduate Certificates

The Department of Mathematics and Statistics partners with the Department of Computer Science to offer a Graduate Certificate in Data Science, specifically for students in STEM disciplines.  For details, please visit www.math.usu.edu/datascience.

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