Statistics, MS
Graduate School Policies and Procedures apply to this program.
Please click here to see Mathematical and Statistical Sciences department information.
Introduction
The explosive growth in data collection over the past 10 years is unlikely to slow any time soon. This has created a dramatic increase in demand for individuals who can understand how to make decisions and predictions in the context of uncertainty through use of experimental design, statistical methods and programming, especially in the context of large data sets. This need spans many fields such as national security applications (including real-time monitoring of internet trends), environmental applications of climate modeling over space and time, medical and genomic applications that use electronic medical records to correlate demographics, genetic data and clinical outcomes over millions of individuals, and manufacturing with real-time monitoring of features over a variety of processes to both troubleshoot and optimize manufacturing.
Our MS in Statistics offers a general degree in statistics.
Students are exposed to a variety of coursework and have the opportunity to participate in real-world research and consulting through our innovative Statistical Consulting. Whatever specialization students choose, graduates with statistics degree will be prepared for a multitude of careers.
These program requirements are subject to periodic revision by the academic department, and the College of Liberal Arts and Sciences reserves the right to make exceptions and substitutions as judged necessary in individual cases. Therefore, the College strongly urges students to consult regularly with their program advisor and CLAS advisor to confirm the best plans of study before finalizing them.
Program Requirements
Students must present 30 hours of course work (which are broken into 4 components as detailed below) and maintain a 3.0 GPA or above for the MS degree. At least 24 of these hours must consist of graduate level (numbered 5000 or higher) courses with the MATH prefix. The remaining 6 hours must be either MATH courses numbered 5000 or above or approved courses outside the department numbered 4000 or above.
All students must complete a written project and pass a final oral exam. The project is developed as a student-centered independent research component within MATH 5960 Master's Project unless the student has chosen the thesis option. For students choosing the thesis option, 4 to 6 hours (of the 30 required hours) may be devoted to the writing of a thesis through MATH 5950 Master's Thesis. By graduate school rules, Master's students, whether enrolled full-time or part-time, must complete all degree requirements within 7 years of matriculation.
- Students must complete a minimum of 30 MATH credit hours.
- Students must complete a minimum of 24 graduate (5000-level or higher) credit hours.
- Students must earn a minimum grade of B- (2.7) in all courses taken at CU Denver and must achieve a minimum cumulative GPA of 3.0. All graded attempts in required and elective courses are calculated in the program GPA. Students cannot complete program or ancillary course requirements as pass/fail.
- Students must complete 21 credit hours with CU Denver faculty.
Program Restrictions, Allowances and Recommendations
- The remaining 6 hours must be either MATH courses numbered 5000 or above or approved courses outside the department numbered 4000 or above.
- Up to 9 semester hours of prior course work may be transferred in (subject to approval); these must be at the 5000 level or above with a B- or better grade. Courses already applied toward another degree (graduate or undergraduate) cannot be used toward the MS degree in Statistics.
- The following MATH courses will not count toward a graduate degree: MATH 5010 History of Mathematics, MATH 5012 An Advanced Perspective on Number and Operation-MATH 5015 Capstone Course for Secondary Teachers, MATH 5017 Topics in Mathematics for Teachers, MATH 5198 Mathematics for Bioscientists, and MATH 5830 Applied Statistics.
Course Requirements for the MS Degree in Statistics
The MS degree in Statistics consists of 4 components:
- core courses,
- statistics electives,
- other electives, and
- MATH 5960 Master's Project or MATH 5950 Master's Thesis.
Core Courses
| Code | Title | Hours |
|---|---|---|
| Take the following | 12 | |
| MATH 5310 | Probability | 3 |
| MATH 5320 | Statistical Inference | 3 |
| MATH 5387 | Applied Regression Analysis | 3 |
| MATH 6330 | Workshop in Statistical Consulting | 3 |
Statistics Electives
| Code | Title | Hours |
|---|---|---|
| Take nine hours of statistics electives are required. A running list is given below. Additional courses can be substituted given prior approval by the student’s advisor and the Director of the Program in Statistics. | 9 | |
| MATH 5394 | Experimental Designs | 3 |
| MATH 5792 | Probabilistic Modeling | 3 |
| MATH 6101 | Uncertainty Quantification | 3 |
| MATH 6380 | Stochastic Processes | 3 |
| MATH 6384 | Spatial Data Analysis | 3 |
| MATH 6388 | Statistical and Machine Learning | 3 |
| MATH 7384 | Mathematical Probability | 3 |
| MATH 7386 | Monte Carlo Methods | 3 |
| MATH 7393 | Bayesian Statistics | 3 |
| MATH 7826 | Topics in Probability and Statistics | 3 |
Additional MATH Electives
| Code | Title | Hours |
|---|---|---|
| Take six hours from any graduate level MATH course that can be used for an MS or Ph.D. degree in Applied Mathematics can be used as an Other Elective. While these courses could be additional statistics-focused courses, the added flexibility allows students to direct their coursework into other areas of mathematics and/or science. The following courses will not count toward the M.S. in Statistics: MATH 5010, MATH 5012-5015, MATH 5017, MATH 5198, MATH 5250 and MATH 5830. | 6 | |
| MATH 5027 | Topics in Applied Mathematics | 3 |
| MATH 5070 | Applied Analysis | 3 |
| MATH 5110 | Theory of Numbers | 3 |
| MATH 5135 | Functions of a Complex Variable | 3 |
| MATH 5337 | Intro to Statistical and Machine Learning | 3 |
| MATH 5350 | Mathematical Theory of Interest | 3 |
| MATH 5351 | Actuarial Models | 3 |
| MATH 5388 | Machine Learning Methods | 3 |
| MATH 5390 | Game Theory | 3 |
| MATH 5394 | Experimental Designs | 3 |
| MATH 5410 | Modern Cryptology | 3 |
| MATH 5432 | Computational Graph Theory | 3 |
| MATH 5446 | Theory of Automata | 3 |
| MATH 5490 | Network Flows | 3 |
| MATH 5576 | Mathematical Foundations of Artificial Intelligence I | 3 |
| MATH 5593 | Linear Programming | 3 |
| MATH 5610 | Computational Biology | 3 |
| MATH 5660 | Numerical Analysis I | 3 |
| MATH 5661 | Numerical Analysis II | 3 |
| MATH 5674 | Parallel Computing and Architectures | 3 |
| MATH 5718 | Applied Linear Algebra | 3 |
| MATH 5733 | Partial Differential Equations | 3 |
| MATH 5779 | Math Clinic | 3 |
| MATH 5791 | Continuous Modeling | 3 |
| MATH 5792 | Probabilistic Modeling | 3 |
| MATH 5793 | Discrete Math Modeling | 3 |
| MATH 5794 | Optimization Modeling | 3 |
| MATH 6023 | Topics in Discrete Math | 3 |
| MATH 6101 | Uncertainty Quantification | 3 |
| MATH 6131 | Real Analysis | 3 |
| MATH 6360 | Exploratory Data Analysis | 3 |
| MATH 6376 | Statistical Computing | 3 |
| MATH 6380 | Stochastic Processes | 3 |
| MATH 6384 | Spatial Data Analysis | 3 |
| MATH 6388 | Statistical and Machine Learning | 3 |
| MATH 6395 | Multivariate Methods | 3 |
| MATH 6398 | Calculus of Variations and Optimal Control | 3 |
| MATH 6404 | Applied Graph Theory | 3 |
| MATH 6595 | Nonlinear Programming | 3 |
| MATH 6653 | Introduction to Finite Element Methods | 3 |
| MATH 6735 | Continuum Mechanics | 3 |
| MATH 6840 | Independent Study | 1-3 |
| MATH 6960 | Research Methods in Mathematics and Statistics | 3 |
| MATH 7101 | Topology | 3 |
| MATH 7132 | Functional Analysis | 3 |
| MATH 7376 | Statistical Computing | 3 |
| MATH 7381 | Mathematical Statistics I | 3 |
| MATH 7382 | Mathematical Statistics II | 3 |
| MATH 7384 | Mathematical Probability | 3 |
| MATH 7385 | Stochastic Differential Equations | 3 |
| MATH 7386 | Monte Carlo Methods | 3 |
| MATH 7393 | Bayesian Statistics | 3 |
| MATH 7397 | Nonparametric Statistics | 3 |
| MATH 7405 | Advanced Graph Theory | 3 |
| MATH 7409 | Applied Combinatorics | 3 |
| MATH 7410 | Combinatorial Structures | 3 |
| MATH 7413 | Modern Algebra I | 3 |
| MATH 7414 | Modern Algebra II | 3 |
| MATH 7419 | Mathematical Coding Theory | 3 |
| MATH 7421 | Projective Geometry | 3 |
| MATH 7593 | Advanced Linear Programming | 3 |
| MATH 7594 | Integer Programming | 3 |
| MATH 7595 | Advanced Nonlinear Programming | 3 |
| MATH 7663 | Finite Difference Methods for Partial Differential Equations | 3 |
| MATH 7665 | Numerical Linear Algebra | 3 |
| MATH 7667 | Introduction to Approximation Theory | 3 |
| MATH 7821 | Topics in Projective Geometry | 3 |
| MATH 7822 | Topics in Linear Algebra | 3 |
| MATH 7823 | Topics in Discrete Math | 3 |
| MATH 7824 | Topics in Computational Mathematics | 3 |
| MATH 7825 | Topics in Optimization | 3 |
| MATH 7826 | Topics in Probability and Statistics | 3 |
| MATH 7827 | Topics in Applied Mathematics | 3 |
| MATH 7921 | Readings in Mathematics | 1 |
| MATH 7922 | Rdgs:Math Fndts-Cmptr Sc | 1 |
| MATH 7923 | Readings: Discrete Mathematics | 1 |
| MATH 7924 | Rdgs:Comp Mathematics | 1 |
| MATH 7925 | Readings: Optimization | 1 |
| MATH 7926 | Rdgs:Applied Prob/Stats | 1 |
| MATH 7927 | Rdgs:Comp/Math Biology | 1 |
| MATH 8660 | Mathematical Foundations of Finite Element Methods | 3 |
| MATH 8664 | Iterative Methods in Numerical Linear Algebra | 3 |
Capstone: Masters Project or Thesis
Students are required to complete a Master’s Thesis or Project as part of the degree. Students must take 3 credits of Math 5950 (Master’s Thesis) or Math 5960 (Master’s Project) while completing the project.
| Code | Title | Hours |
|---|---|---|
| Take a minimum of three credits of the following | 3 | |
| MATH 5960 | Master's Project | 3 |
| or MATH 5950 | Master's Thesis | |
To learn more about the Student Learning Outcomes for this program, please visit our website.