Mathematics, 4+1 Year BS/ Statistics, MS
Introduction
Please click here to see Mathematical and Statistical Sciences department information.
This is a unique program where a student can obtain both a BS in Mathematics and MS in Statistics in five years through a specialized course sequence. The program requires 12 fewer credits than if both degrees were earned separately.
These degree 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 major and graduate advisor and CLAS advisor to confirm the best plans of study before finalizing them.
Program Delivery
- This is an on-campus program.
Declaring This Major
- Click here to go to information about declaring a major.
General Requirements
To earn a degree, students must satisfy all requirements in each of the three areas below, in addition to their individual major requirements.
- CU Denver General Graduation Requirements
- CU Denver Core Curriculum
- College of Liberal Arts & Sciences Graduation Requirements
- Click here for information about Academic Policies
Program Requirements
While students are completing a BS degree in Mathematics, they may also complete some of the requirements for an MS degree in Statistics by participating in the BS/MS program using the following guidelines:
- The student must apply and be accepted for participation in the BS/MS program prior to completion of the BS degree in consultation with both the undergraduate and graduate advisors. Students must complete a 4+1 intent form to formally declare this program, as they work very closely with undergraduate and graduate advisors to ensure they are on track and completing requirements as necessary.
- Students should declare their intent to complete this program in their junior or senior year to the Director of the Program in Statistics after completing MATH 1401 Calculus I, MATH 2411 Calculus II, MATH 2421 Calculus III, MATH 3000 Introduction to Abstract Mathematics, MATH 3191 Applied Linear Algebra, MATH 3382 Statistical Theory. A 3.0 grade point average (GPA) is required over all mathematics courses.
- Students must complete a total of 45 credit hours, including a minimum of 42 MATH credit hours.
- Students must complete at least 30 upper-division (3000-level and above) credit hours in the major.
- Students must earn a minimum grade of C- (1.7) in all undergraduate courses that apply to the major and must achieve a minimum cumulative undergraduate major GPA of 2.25. All graded attempts in required and elective courses are calculated in the major GPA. Students must earn a minimum grade of B- (2.7) in all graduate courses and must achieve a minimum cumulative major GPA of 3.0, for all courses that will apply to the MS. Courses taken using P+/P/F or S/U grading cannot apply to major and graduate requirements.
- Students must complete a minimum of 15 upper-division level MATH credit hours and all graduate level credit hours with CU Denver faculty.
- Up to 12 semester hours of graduate-level course work may be taken as an undergraduate and applied toward the MS degree in Statistics.
- In the semester in which the student intends to complete their BS, students must apply for admission into MS degree in Statistics.
- Students must complete a 4+1 intent form to formally declare this program, as they work very closely with undergraduate and graduate advisors to ensure they are on track and completing requirements as necessary. Students must apply and be accepted to the Statistics, MS during the last semester of their undergraduate career. A maximum of 12 credit hours of graduate level courses that are applied to the undergraduate degree will apply to the graduate degree.
Mathematics, BS Course Requirements
Programming Courses
| Code | Title | Hours |
|---|---|---|
| Complete one of the following programming requirements: | 3-4 | |
| Fundamentals of Computing and Fundamentals of Computing Laboratory | ||
| Programming for Data Science | ||
Mathematics Courses
| Code | Title | Hours |
|---|---|---|
| Complete all of the following Mathematics courses: | 27 | |
| Calculus I | ||
| Calculus II | ||
| Calculus III | ||
| Introduction to Abstract Mathematics | ||
| Applied Linear Algebra | ||
| Statistical Theory | ||
| Introduction to Real Analysis I | ||
| Math Clinic | ||
Electives
| Code | Title | Hours |
|---|---|---|
| Complete one approved MATH elective (at least six credit hours) above the 3000 level, excluding MATH 3041, MATH 3195, MATH 3511, MATH 3800, MATH 3999, and MATH 4830. | 3 | |
| Elementary Differential Equations | ||
| Introduction to Optimization | ||
| Simulation in Operations Research | ||
| Data Wrangling & Visualization | ||
| Introduction to Symbolic Logic | ||
| Introduction to Probability | ||
| History of Mathematics | ||
| Topics in Mathematics | ||
| Theory of Numbers | ||
| Introduction to Modern Algebra | ||
| Introduction to Real Analysis II | ||
| Intro to Statistical and Machine Learning | ||
| Applied Regression Analysis | ||
| Machine Learning Methods | ||
| Game Theory | ||
| Experimental Designs | ||
| Applied Graph Theory | ||
| Applied Combinatorics | ||
| Complex Variables | ||
| Numerical Analysis I | ||
| Numerical Analysis II | ||
| Partial Differential Equations | ||
| Continuous Modeling | ||
| Probabilistic Modeling | ||
| Discrete Math Modeling | ||
| Optimization Modeling | ||
MATH numbered 5000 or above
Note that the courses below will be used to satisfy requirements for both the BS in Mathematics and the MS in Statistics.
| Code | Title | Hours |
|---|---|---|
| Complete the following: | 9 | |
| Probability | ||
or MATH 5792 | Probabilistic Modeling | |
or MATH 6380 | Stochastic Processes | |
| Statistical Inference | ||
| Applied Regression Analysis | ||
Take one of | ||
| Intro to Statistical and Machine Learning | ||
| Machine Learning Methods | ||
| Experimental Designs | ||
| Probabilistic Modeling | ||
| Uncertainty Quantification | ||
| Stochastic Processes | ||
| Spatial Data Analysis | ||
| Statistical and Machine Learning | ||
| Mathematical Probability | ||
| Monte Carlo Methods | ||
| Bayesian Statistics | ||
| Topics in Probability and Statistics | ||