Computer Science-Mathematical Sciences (Combined Major)

This is an archived copy of the 2018-2019 catalog. To access the most recent version of the catalog, please visit http://catalog.umt.edu/.

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 60 credits in the two disciplines:

  • 30 of these credits in Computer Science courses and
  • 30 of these credits in Mathematical Sciences courses.

Each student plans a program in consultation with 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.

Bachelor of Science - Computer Science-Mathematical Science

College of Humanities & Sciences

Degree Specific Credits: 73-74

Required Cumulative GPA: 2.0

Catalog Year: 2018-2019

General Education Requirements

Information regarding these requirements can be found in the General Education Section of the catalog. 

Summary

Mathematical Science31
Computer Science30
Science Requirement9-10
Biology Sequence Option
Chemistry Sequence Option
Physics Sequence Option
Public Speaking Requirement3
Total Hours73-74

Mathematical Sciences

Rule: Complete the following subcategories. 31 total credits required.

Mathematical Sciences Core

Complete all of the following courses:
M 171Calculus I4
or M 181 Honors Calculus I
M 172Calculus II4
or M 182 Honors Calculus II
M 221Introduction to Linear Algebra4
M 273Multivariable Calculus4
M 307Introduction to Abstract Mathematics3
or M 225 Introduction to Discrete Mathematics
Total Hours19

Minimum Required Grade: C-

Mathematical Sciences Electives

Note: 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).

Complete 12 credits of the following courses:12
Ordinary Differential Equations and Systems
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
Introduction to Probability and Statistics
Probability Theory
Mathematical Statistics
Statistical Methods I
Statistical Methods II
Total Hours12

Minimum Required Grade: C-


Computer Science

Rule: Complete the following subcategories. 30 total credits required.

Computer Science Core

Complete all of the following courses:
CSCI 106Careers in Computer Science1
CSCI 135Fund of Computer Science I3
CSCI 136Fund of Computer Science II3
CSCI 205Programming Languages w/ C/C++4
CSCI 232Data Structures and Algorithms4
CSCI 332Design/Analysis of Algorithms3
CSCI 361Computer Architecture3
Total Hours21

Minimum Required Grade: C-

Computer Science Electives

Note:

  1. A total of at most three of the nine credits of Computer Science Electives may be in CSCI 398 or CSCI 498.
  2. 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).

Complete 9 credits of the following courses:9
Computers, Ethics, and Society
Software Science
Database Design
Research
Special Topics
Seminar
Internship
Advanced Web Programming
Game and Mobile App
Adv Prgrmng Theory/Practice I
Adv Prgrmng Theory/Practice II
Computer Graphics Programming
User Interface Design
Data Visualization
Artificial Intelligence
Machine Learning
Pattern Recognition
Computational Biology
Operating Systems
Applications of Mining Big Data
Networks
Simulation
Applied Parallel Computing Techniques
Research
Special Topics
Seminar
Internship
Senior Thesis/Capstone
Total Hours9

Minimum Required Grade: C-

Science Requirement

Rule: Complete 1 of the following subcategories. 9-10 total credits required. 

Biology Sequence Option

Complete all of the following courses:
BIOB 160NPrinciples of Living Systems3
BIOB 161NPrncpls of Living Systems Lab1
BIOB 170NPrincpls Biological Diversity3
BIOB 171NPrincpls Biological Dvrsty Lab2
Total Hours9

Minimum Required Grade: C-

Chemistry Sequence Option

Complete all of the following courses:
CHMY 141N
CHMY 142N
College Chemistry I
and College Chemistry I Lab
5
CHMY 143N
CHMY 144N
College Chemistry II
and College Chemistry II Lab
5
Total Hours10

Minimum Required Grade: C-

Physics Sequence Option

Complete all of the following courses:
PHSX 215NFund of Physics w/Calc I4
PHSX 216NPhysics Laboratory I w/Calc1
PHSX 217NFund of Physics w/Calc II4
PHSX 218NPhysics Laboratory II w/Calc1
Total Hours10

Minimum Required Grade: C-


Public Speaking Requirement

Complete 1 of the following courses:3
Introduction to Public Speaking
Argumentation
Total Hours3

Minimum Required Grade: C-


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

M 311Ordinary Differential Equations and Systems3
M 412Partial Differential Equations3
M 414Deterministic Models3
Select one of the following:3-4
Advanced Calculus I
Numerical Analysis
Introduction to Complex Analysis
Introduction to Real Analysis
Introduction to Probability and Statistics
Select three of the following:9
Computer Graphics Programming
Data Visualization
Operating Systems
Simulation
Total Hours21-22

Combinatorics and Optimization–Artificial Intelligence

M 361Discrete Optimization3
M 362Linear Optimization3
Select two of the following:6
Discrete Mathematics
Deterministic Models
Graph Theory
Introduction to Probability and Statistics
CSCI 446Artificial Intelligence3
CSCI 447Machine Learning3
CSCI 460Operating Systems3
Total Hours21

Data Science (Big Data Analytics)

M 461Data Science Analytics3
M 462Theoretical Basics of Big Data Analytics and Real Time Computation Algorithms3
STAT 341Introduction to Probability and Statistics3
STAT 451Statistical Methods I3
STAT 452Statistical Methods II3
Select three of the following:9
Data Visualization
Machine Learning
Pattern Recognition
Applications of Mining Big Data
Applied Parallel Computing Techniques
Total Hours24

Statistics–Machine Learning

STAT 341Introduction to Probability and Statistics3
STAT 421Probability Theory3
Select two of the following:6
Discrete Mathematics
Linear Optimization
Graph Theory
Mathematical Statistics
Select three of the following:9
Database Design
Data Visualization
Artificial Intelligence
Machine Learning
Computational Biology
Total Hours21

Algebra–Analysis

M 381Advanced Calculus I3
M 431Abstract Algebra I4
Select two of the following:7-8
Number Theory
Abstract Algebra II
Introduction to Complex Analysis
Introduction to Real Analysis
CSCI 426Adv Prgrmng Theory/Practice I3
CSCI 460Operating Systems3
CSCI Elective3
Total Hours23-24