The Master of Science degree at Montana State University is interdisciplinary program that draws on courses in three programs: Computer Science, Mathematics, and Statistics. The broad goal is to provide students with foundational training in data analysis, with equal emphasis on the principles of computer science, mathematics, and statistics, and the ability to apply these principles to a range of data-driven problems. More specifically, the learning outcomes for graduates of the program are:

  • Demonstrate knowledge of essential deterministic, randomized and approximation algorithms for data classification and clustering, dimensionality reduction, regression, and optimization.
  • Demonstrate knowledge in the principles and practice of statistical experimental design, statistical inference, and decision theory.
  • Demonstrate the ability to take a real-world data analysis problem, formulate a conceptual approach to the problem, match aspects of the problem to previously learned theoretical and methodological tools, break down the solution into a step-by-step approach, and implement a working solution in a modern software language.
  • Communicate data science problems, analyses, and solutions effectively to both specialists and non-specialists through the use of effective technical writing, presentations, and data visualizations, and teamwork and collaboration.

Required Courses

There are three essential domains in this program: Computer Science, Statistics, and Mathematics. Each student is required to take:

  • At least 2 courses (=6 credits) in each of the three essential domains
  • In each domain one of those courses must be the Foundational Course. These foundational courses are:     
    • CSCI 532 (Algorithms),
    • STAT 541 (Experimental Design),
    • M 508 (Mathematical Foundations of Machine Learning)

Additionally, students can choose among the following courses:

  • CSCI 440 (Database Systems), CSCI 540 (Advanced Database Systems), CSCI 446 (Artificial Intelligence), CSCI 447 (Machine Learning: Soft Computing), CSCI 535 (Computational Topology), CSCI 547 (Machine Learning), CSCI 548 (Reasoning Uncertainty), CSCI 550 (Data Mining),
  • STAT 408 (Statistical Computing and Graphical Analysis), STAT 511 (Methods of Data Analysis I), STAT 512 (Methods of Data Analysis II), STAT 436 (Introduction to Time Series Analysis) or 536 (Time Series Analysis), STAT 437 (Introduction to Applied Multivariate Analysis) or STAT 537 (Applied Multivariate Analysis I),
  • M 441 (Numerical Linear Algebra & Optimization), M 442 (Numerical Solution of Differential Equations), M 507 (Mathematical Optimization)

Sample Programs*

Program for a student with a dominant interest in Computer Science:

 

Year/ Domain

Computer Science

Mathematics

Statistics

Year 1

CSCI 532, CSCI 547, CSCI 540

M 441

STAT 408

Year 2

CSCI 535, CSCI 550

M 508

STAT 511, STAT 541

 

Program for a student with a dominant interest in Mathematics:

 

Year/ Domain

Computer Science

Mathematics

Statistics

Year 1

CSCI 532, CSCI 547

M 441, M 560

STAT 408

Year 2

CSCI 550, CSCI 535

M 508

STAT 511, STAT 541

 

Program for a student with a dominant interest in Statistics.

 

Year/ Domain

Computer Science

Mathematics

Statistics

Year 1

CSCI 532

M 441

STAT 408, STAT 511, STAT 512

Year 2

CSCI 547

M 508

STAT 541, STAT 537, STAT 536

 

*If students have already taken the suggested courses and have not reserved them for use in this program, then appropriate coursework will be identified, such as STAT 505 (Linear Models) and 506 (Advanced Regression Analysis) if student has completed STAT 511 and 512.

Prerequisites

  • 3 semesters of Calculus (through Multivariable Calculus (M 273) or equivalent
  • Linear Algebra (M 221) or equivalent
  • Data Structures and Algorithms (CSCI 232) or equivalent
  • Methods of Proof (M 242) or Discrete Structures (CSCI 246) or equivalent
  • Introductory Statistics (STAT 216) or equivalent (additional statistics coursework such as Intermediate Statistical Methods (STAT 217) or STAT 401 and then STAT 511, 512 preferred)
  • At least three senior level courses in mathematics, statistics, or computer science or equivalent