Mathematical Sciences-Computer Science B.S. (Combined Major)
The purpose of the combined program is to provide a thorough background in both allied disciplines and to inculcate a deeper understanding of their goals and methods. A student must complete 62 credits in the two disciplines:
- 31 of these credits in Computer Science courses and
- 31 of these credits in Mathematical Sciences courses.
Each student plans a program in consultation with both a Computer Science and a Mathematical Sciences advisor. Students planning to attend graduate school in computer science or the mathematical sciences should consult with their respective advisors.
General Degree Requirements
To earn a baccalaureate degree, all students must complete successfully, in addition to any other requirements, the University of Montana General Education Requirements. Please refer to the General Education Requirements page for more information.
Additional requirements for graduation can be found on the Degree/Certificate Requirements for Graduation page.
Unless otherwise noted in individual program requirements, a minimum grade point average of 2.00 in all work attempted at the University of Montana-Missoula is required for graduation. Please see the Academic Policies and Procedures page for information on how your GPA is calculated.
Courses taken to satisfy the requirements of a major, minor, or certificate program must be completed with a grade of C- or better unless a higher grade is noted in the program requirements.
BACHELOR OF SCIENCE - MATHEMATICAL SCIENCE-COMPUTER SCIENCE
Course Requirements
| Code | Title | Hours |
|---|---|---|
| Mathematical Sciences Core | ||
| Complete all of the following courses: | ||
| M 171 | Calculus I | 4 |
| or M 181 | Honors Calculus I | |
| M 172 | Calculus II | 4 |
| or M 182 | Honors Calculus II | |
| M 221 | Introduction to Linear Algebra | 4 |
| M 273 | Multivariable Calculus | 4 |
| M 307 | Introduction to Abstract Mathematics | 3 |
| or M 225 | Introduction to Discrete Mathematics | |
| Mathematical Sciences Electives 1 | ||
| Complete 12 credits of the following courses: | 12 | |
| Introduction to Differential Equations | ||
| Discrete Mathematics | ||
| Number Theory | ||
| Discrete Optimization | ||
| Linear Optimization | ||
| Advanced Calculus I | ||
| Partial Differential Equations | ||
| Deterministic Models | ||
| History of Mathematics | ||
| Abstract Algebra I | ||
| Abstract Algebra II | ||
| Euclidean and NonEuclidean Geometry | ||
| Numerical Analysis | ||
| Statistical, Dynamical, and Computational Modeling | ||
| Data Science Analytics | ||
| Theoretical Basics of Big Data Analytics and Real Time Computation Algorithms | ||
| Introduction to Complex Analysis | ||
| Introduction to Real Analysis | ||
| Graph Theory | ||
| Probability and Simulation | ||
| Probability Theory | ||
| Mathematical Statistics | ||
| Statistical Methods I | ||
| Statistical Methods II | ||
| Computer Science Core | ||
| Complete all of the following courses: | ||
| CSCI 106 | Careers in Computer Science | 1 |
| CSCI 150 | Introduction to Computer Science | 3 |
| CSCI 151 | Interdisciplinary Computer Science I | 3 |
| CSCI 152 | Interdisciplinary Computer Science II | 3 |
| CSCI 232 | Intermediate Data Structures and Algorithms | 4 |
| CSCI 258 | Web Application Development | 3 |
| CSCI 332 | Advanced Data Structures and Algorithms | 3 |
| CSCI 340 | Database Design | 3 |
| Computer Science Electives 1, 2 | ||
| Complete 9 credits of upper-division (300-level or higher) CSCI courses. | 9 | |
| Science Requirement | ||
| Complete the coursework from one of the following subcategories. 9-10 total credits required. | 9-10 | |
Biology | ||
| Principles of Living Systems | ||
| Principles of Living Systems Lab | ||
| Principles of Biological Diversity | ||
| Principles of Biological Diversity Lab | ||
Chemistry | ||
| College Chemistry I | ||
| College Chemistry I Lab | ||
| College Chemistry II | ||
| College Chemistry II Lab | ||
Physics | ||
| Fundamentals of Physics with Calculus I | ||
| Physics Laboratory I with Calculus | ||
| Fundamentals of Physics with Calculus II | ||
| Physics Laboratory II with Calculus | ||
| Public Speaking Requirement | ||
| Complete one of the following courses: | 3 | |
| Introduction to Public Speaking | ||
| Argumentation | ||
| Total Hours | 75-76 | |
- 1
The combined nine credits of Computer Science Electives and twelve credits of Mathematical Sciences Electives must include at least three 3- or 4-credit courses numbered 400 or above with at least one chosen from each department (not including M 429 and STAT 451, STAT 452).
- 2
A total of at most three of the nine credits of Computer Science Electives may be in CSCI 398 or CSCI 498.
Suggested Curricula
Note: Students are encouraged to choose their Computer Science and Mathematical Sciences Electives according to one of the following curricula; these tracks are suggestions only and, as such, optional. Note that the suggested curricula do not include an advanced College Writing Course.
Applied Math–Scientific Programming
| Code | Title | Hours |
|---|---|---|
| M 274 | Introduction to Differential Equations | 4 |
| M 412 | Partial Differential Equations | 3 |
| M 414 | Deterministic Models | 3 |
| Select one of the following: | 3-4 | |
| Advanced Calculus I | ||
| Numerical Analysis | ||
| Introduction to Complex Analysis | ||
| Introduction to Real Analysis | ||
| Probability and Simulation | ||
| Select three of the following: | 9 | |
| Data Visualization | ||
| Operating Systems | ||
| Simulation | ||
Combinatorics and Optimization–Artificial Intelligence
| Code | Title | Hours |
|---|---|---|
| M 361 | Discrete Optimization | 3 |
| M 362 | Linear Optimization | 3 |
| Select two of the following: | 6 | |
| Discrete Mathematics | ||
| Deterministic Models | ||
| Graph Theory | ||
| Probability and Simulation | ||
| CSCI 447 | Machine Learning | 3 |
| CSCI 460 | Operating Systems | 3 |
Data Science (Big Data Analytics)
| Code | Title | Hours |
|---|---|---|
| M 461 | Data Science Analytics | 3 |
| M 462 | Theoretical Basics of Big Data Analytics and Real Time Computation Algorithms | 3 |
| STAT 342 | Probability and Simulation | 3 |
| STAT 451 | Statistical Methods I | 3 |
| STAT 452 | Statistical Methods II | 3 |
| Select three of the following: | 9 | |
| Data Visualization | ||
| Machine Learning | ||
| Applications of Mining Big Data | ||
| Applied Parallel Computing Techniques | ||
Statistics–Machine Learning
| Code | Title | Hours |
|---|---|---|
| STAT 342 | Probability and Simulation | 3 |
| STAT 421 | Probability Theory | 3 |
| Select two of the following: | 6 | |
| Discrete Mathematics | ||
| Linear Optimization | ||
| Graph Theory | ||
| Mathematical Statistics | ||
| Select three of the following: | 9 | |
| Database Design | ||
| Data Visualization | ||
| Machine Learning | ||
| Computational Biology | ||
Algebra–Analysis
| Code | Title | Hours |
|---|---|---|
| M 381 | Advanced Calculus I | 3 |
| M 431 | Abstract Algebra I | 4 |
| Select two of the following: | 7-8 | |
| Number Theory | ||
| Abstract Algebra II | ||
| Introduction to Complex Analysis | ||
| Introduction to Real Analysis | ||
| CSCI 426 | Software Design & Development I | 3 |
| CSCI 460 | Operating Systems | 3 |
| CSCI Elective | 3 | |