Computer Science
The M.S. in Computer Science (MSCS) is a 30-hour program designed to prepare students to work in computing- and technology-related fields. Courses will be eight (8) weeks in length (two sequential courses in each traditional semester) and delivered in multiple formats. The coursework will be taught using a mixture of synchronous and asynchronous online delivery, as well as in a hybrid format. While most work will be completed online, students will come to LaGrange’s campus 1 – 2 times every semester for instruction.
The MSCS is designed for recent baccalaureate graduates who wish to earn a master’s degree in preparation for industrial or government employment or for ongoing studies in computer science. The program will be promoted to those who possess a baccalaureate degree in computer science or a related field and wish to advance their studies in the field.
Students will be required to have:
- A completed application for admission
- A bachelor's degree in computer science or a related field from an accredited institution prior to matriculation in this program, and
- An undergraduate cumulative GPA of at least 3.0 on a 4.0 scale (or equivalent).
Majors and Minors
Major Minor
M.S. in Computer ScienceCourses
Study of abstract models of computation, unsolvability, complexity theory, formal grammars and parsing, and other advanced topics in theoretical computer science.
Introduction to machine learning, including computational learning theory, major approaches to machine learning, evaluation of models, and current research.
This course provides a graduate-level introduction to computer and network security and privacy. Students successfully completing this class will be able to evaluate works in academic and commercial security, and will have rudimentary skills in security research. The course covers four key topic areas: basics of cryptography and crypto protocols, network security, systems security, and privacy. Readings primarily come from seminal papers in the field.
A study of high-performance computing for advanced scientific research on modern processors. Topics include high-performance computing techniques, floating point properties, and advanced numerical methods.
Algorithm behavior and applicability. Effect of roundoff errors, systems of linear equations and direct methods, least squares via Givens and Householder transformations, stationary and Krylov iterative methods, the conjugate gradient and GMRES methods, convergence of method.
The practicum serves as a bridge between academia and industry to build practical experience in the field of computer science. Students will collaborate with approved industry professionals, research institutions, or technology companies to enhance their communication, teamwork, and problem-solving skills. May be taken for variable amounts of credit.
Techniques for designing efficient algorithms; analysis of algorithms; lower bound arguments; and algorithms for sorting, selection, graphs, and string matching.
Introduction to operating systems concepts. Topics include multiprogramming, resources allocation and management, and their implementation.
Techniques used in large scale scientific or technical software development, including requirements analysis, specification, systems design, implementation, testing, validation, verification, and maintenance.
Introduction to computer crime and the study of evidence for solving computer-based crimes. Topics: computer crime, computer forensics and methods for handling evidence.
Students will complete a group project on a computing-related issue and provide a public presentation to the college community.
