Requirements for a Major in Mathematics
Bachelor of Science
| Code | Title | Hours |
|---|---|---|
| MATH 132 | Calculus II | 4 |
| MATH 203 | Multivariable Calculus | 4 |
| MATH 213 | Elementary Linear Algebra | 3 |
| MATH 215 | Mathematics Resources, Opportunities, and Career Seminar | 1 |
| MATH 220 | Discrete Mathematics | 3 |
| MATH 321 | Modern Algebra I | 3 |
| MATH 415 | Mathematics and Data Science Seminar | 1 |
| MATH 421 | Real Analysis I | 3 |
| CSCI 111 | Introduction to Computer Science | 4 |
| Select five approved MATH or APMA electives above 200-level not already taken 1 | 15 | |
| Total Hours | 41 | |
- 1
PHYS 250 Mathematical Physics , CSCI 363 Theory of Computation, DATA 210 Principles of Data Science, or DATA 310 Communicating with Data may also be counted as an elective.
Requirements for a Teaching Endorsement in Mathematics
Students who are interested in earning a teaching license from the Commonwealth of Virginia to teach grades 6 –12 should double major in Mathematics nd Education. Interested students should contact both their Mathematics advisor and their Education advisor. The program requirements of a student planning to major in education for the purpose of certification in Mathematics vary from those of other Mathematics majors. Information regarding the Education program and required courses for licensure can be found in the RMC Teacher Preparation Handbook .
| Code | Title | Hours |
|---|---|---|
| Required Courses: | ||
| Calculus I | ||
| Calculus II | ||
| Multivariable Calculus | ||
| Elementary Linear Algebra | ||
| Mathematics Resources, Opportunities, and Career Seminar | ||
| Discrete Mathematics | ||
| Modern Algebra I | ||
| Statistical Inference | ||
| Mathematics and Data Science Seminar | ||
| Real Analysis I | ||
| Introduction to Computer Science | ||
| Mathematics for Elementary Educators I | ||
| Mathematics for Elementary Educators II | ||
| Optional Courses: | ||
| Differential Equations: A Modeling Perspective | ||
| Probability | ||
| Methods and Models in Applied Mathematics | ||
| Mathematical Foundations of Data Science | ||