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Computer Science (MS)

College of Sciences / Computer Science and Cybersecurity
Graduate degree / Master of Science

About The Program

The Master of Science in Computer Science (MSCS) is a 34-credit program that provides advanced study in the theory and practice of Computer Science. It focuses on two of the key areas in modern computing: distributed systems and computer security. The MS in Computer Science program, available on campus in Minnesota, has been designed to:

  • Strike a healthy balance between theory and practice
  • Help students acquire the ability to read and assimilate highly technical material
  • Deepen students' technical knowledge
  • Enable students to solve complex problems
  • Help students effectively respond to rapid technological changes
  • Help students develop well organized presentations and written materials
  • Enhance students' careers in computing

The MS in Computer Science program consists of 28 credits of coursework, which includes 12 credits focused on distributed computing and computer security and 16 credits of electives.

All MSCS students will learn about research methodologies, scholarly research, and professional writing in a 2-credit ICS 698 Research Seminar course. An applied project or original thesis in computer science (4 credits) must also be completed.

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Student Outcomes

Students who successfully complete Metro State’s in-person or online computer science master’s degree program will have the following career-benefiting skills:

  • Solid foundation in the concepts of distributed systems and computer security
  • Good knowledge of the major research areas in computer science
  • Ability to survey literature related to research problems in computer science, and to obtain the necessary background information to further explore the problems
  • Skills to write up research results and present them orally

Advance Your Education and Career

How to enroll

Program eligibility requirements

The Computer Science and Cybersecurity (CSC) department bases admission decisions on the applicant's prior academic work (especially in Computer Science), professional or other non-academic background and experience in Computer Science, and recommendation letters. The following three items are the minimum criteria for the CSC department to consider an applicant for potential admission. Meeting these requirements is not a guarantee of admission.

  1. Bachelor's degree in Computer Science or a related discipline from a regionally accredited institution with either a cumulative undergraduate grade point average (GPA) of at least 3.0 (on a 4.0 scale), or an undergraduate GPA of at least 3.0 in all Computer Science and Mathematics or related courses. Applicants without a formal degree in computer science should have completed coursework in the following topics: 1) Discrete mathematics 2) Problem solving using a modern programming language such as C, C++, or Java 3) Data structures (stacks, queues, trees, graphs, etc.), algorithms, and computation complexity 4) Object-oriented programming and design. Note: In rare circumstances, an applicant not meeting the GPA requirements might be considered if their other application materials are stellar (e.g., outstanding recommendations, excellent GRE scores, etc.)
  2. Two positive recommendations from people qualified to judge the applicant's ability for graduate studies
  3. English language proficiency or permanent resident status, documented/demonstrated in one of the following ways:
  • Is a US citizen or permanent resident
  • Has a bachelor's, master's, or doctoral degree from an English-speaking institution in the United States, Canada, the United Kingdom, Ireland, Australia, or New Zealand
  • Has a minimum TOEFL score of 80 (Internet-based), or 550 (paper-based) achieved within 24 months of intended matriculation
  • Has an IELTS score of 6.5 or higher achieved within 24 months of intended matriculation

Applicants must have an undergraduate degree in Computer Science or a related field. Applicants are expected, at a minimum, to have intermediate programming skills with a good knowledge of data structures and concomitant mathematical background. Applicants who do not have such a background will need to take remedial courses before being admitted to the program. This would be the equivalent of having completed ICS 140, 141, 232, 240, 340, 372, and MATH 215 in our undergraduate program (further work in Computer Science would be preferable).

Applicants lacking background in upper level computer science courses would be required, as a condition of admission, to take one or more following courses (will be indicated on the acceptance letter) with a grade of B- or better as part of their program study plans,

  • ICS 440 Parallel and Distributed Algorithms (4 credits)
  • ICS 460 Computer Networks and Security (4 credits)
  • ICS 462 Operating Systems (4 credits)

Up to 8 credits of those 400-level courses may count as electives toward the 34 credits required to graduate.

Application instructions

Metro State University is participating in the common application for graduate programs (GradCAS). Applications are only accepted via the CAS website.

CAS steps

  1. Select the term for which you are seeking admission (below), and navigate to the CAS website. Open applications include:
  2. Create or log in to your account and select the Computer Science (MS) program.
  3. Carefully review all instructions and complete all four sections of the application.

Specific application requirements for individual programs can be found on each program page in CAS. Carefully read the instructions that appear throughout the application pages. You can only submit your application once. If you need to update information you have submitted, please notify graduate.studies@metrostate.edu

Application fee

A nonrefundable $38 fee is required for each application.
Applications will not be processed until this fee is received.

Active-duty military, veterans, and Metro State alumni can receive an application fee waiver. Contact graduate.studies@metrostate.edu.

Courses and Requirements

SKIP TO COURSE REQUIREMENTS

Guidelines for completing the Masters of Science in Computer Science (MSCS) program

Admission into the masters program and transfer coursework equivalency is determined by the Computer Science and Cybersecurity (CSC) Department and initially evaluated upon admission. Once admitted into the program, the student must complete 34 credits of approved work, which include:

  • one course in computer security (4 credits)
  • one course in distributed systems (4 credits)
  • a second course in either computer security or distributed systems (4 credits)
  • the research seminar course (2 credits)
  • a set of elective courses covering advanced material in computer science. (Electives may include additional work in distributed systems or security or may be taken from other advanced topics.) (16 credits)
  • completion of a practical research project (project option) or theoretical problem (thesis option) under the guidance of a CSC resident faculty member of the department. The student must submit a written report of his/her work to a graduate committee and later make an oral defense of the work. (4 credits)

Program Requirements (34 Credits)

+ Prerequisites

Applicants lacking background in upper level computer science courses would be required, as a condition of admission, to take one or more following courses as part of their program study plans. Up to eight credits (8) of this 400-level course work may count as electives toward the 34 credits required to graduate.

Covers design and development of parallel and distributed algorithms and their implementation. Topics include multiprocessor and multicore architectures, parallel algorithm design patterns and performance issues, threads, shared objects and shared memory, forms of synchronization, concurrency on data structures, parallel sorting, distributed system models, fundamental distributed problems and algorithms such as mutual exclusion, consensus, and elections, and distributed programming paradigms. Programming intensive.

Full course description for Parallel and Distributed Algorithms

Principles and practices of the OSI and TCP/IP models of computer networks, with special emphasis on the security of these networks. Coverage of general issues of computer and data security. Introduction to the various layers of network protocols, including physical, data link, network, and transport layers, flow control, error checking, and congestion control. Computer system strengths and vulnerabilities, and protection techniques: Topics include applied cryptography, security threats, security management, operating systems, network firewall and security measures. Focus on secure programming techniques. Programming projects.

Full course description for Networks and Security

Principles, techniques, and algorithms for the design and implementation of modern operating systems. Topics include operating system structures, process and thread scheduling, memory management including virtual memory, file system implementation, input output systems, mass storage structures, protection, and security. Students will implement process, memory, and file management algorithms.

Full course description for Operating Systems

+ Core (12 credits)
Distributed Systems (4-8 credits)

Choose one

This course covers the fundamental issues of distributed databases with focus on data fragmentation and allocation, query optimization and transaction processing. Topics include: Distributed database management systems architecture and design; data fragmentation, replication, and allocation; database security, authorization and integrity control; query optimization; transaction management; distributed concurrency control and replica control; distributed object database management systems; multidatabase systems.

Full course description for Distributed Database Systems

The field of computer science is experiencing a transition from computation-intensive to data-intensive problems, wherein data is produced in massive amounts by large sensor networks, simulations, and social networks. Efficiently extracting, interpreting, and learning from very large datasets requires a new generation of data management technologies. This course gives an introduction to the Hadoop ecosystem as de facto big-data-management system and special consideration will be made to the Apache Spark data analysis framework. The fundamental concepts on which the emerging big data management systems are based are discussed first. Once a foundation is defined, technologies and algorithms that are used to work with big data sets are studied. Tentative topics covered include: distributed file system, map-reduce programming paradigm, Apache Spark basics, SparkSQL, Pig, Hive, Impala, and Scoop. The course is programming intensive and includes several programming assignment projects using…

Full course description for Introduction to Big Data Computing Systems

This course introduces XML technologies, web services and service-oriented architectures. Current approaches to web service design and implementation will be discussed. Models for designing and implementing a service-oriented architecture will be discussed. Security considerations and emerging trends will be explored. Students will implement web services.

Full course description for Web Services and Service-Oriented Architectures

Study of distributed algorithms that are designed to run on networked processors and useful in a variety of applications., such as telecommunications, information processing, and real-time process control. Specific algorithms studied include leader election, distributed consensus, mutual exclusion, resource allocation, and stable property detection. Both asynchronous and synchronous systems will be covered and fault tolerance will be the major theme. Algorithms will be analyzed for complexity and proofs of corrections will be studied.

Full course description for Distributed Algorithms

This course is a Study of the theory and methodologies used in the construction of wireless networks. Topics include: Overview of computer networks and wireless systems; cellular concepts and design fundamentals; physical layer fundamentals; data link control protocols; security related concepts including authentication and privacy with message integrity; wireless medium access control (MAC) protocols; radio resource management (power control); resource allocation and call admission control; mobility management; wireless networking; wireless LAN; wireless mobile ad hoc networks and wireless sensor networks.

Full course description for Wireless Technologies

Through analysis of research literature, lectures, and hand-on projects, this course aims to equip students with the fundamental knowledge and skills necessary to design, develop, deploy, and manage cloud-based solutions. Topics covered include fundamental cloud concepts (e.g., virtualization, multi-tenancy, elasticity, scalability, fault tolerance, and reliability), categories of cloud services (e.g., compute, storage, networking, data management and analytics), and cloud programming models and frameworks (e.g., containers, serverless, Hadoop, and web services). Students will develop a software system utilizing cloud services provided by popular cloud service providers (e.g., Amazon Web Services (AWS), Google Cloud Platform (GCP) or Microsoft Azure).

Full course description for Cloud Application Design and Development

This course provides an introduction to embedded computing, control systems, single-board computers and microcontrollers and Internet of Things. Topics include embedded software development, networked devices, protocols and controls, security, and monitoring. This course will focus on recent research and will include a hands-on/in-class lab component, involving digital logic design and analysis and will include a multi-week group design and development project. (Prerequisite: graduate standing in computer science)

Full course description for Embedded Computing with Control Systems and Internet of Things

In this course, students will examine the scope of cloud computing and forensics as a multi-disciplinary field, including its foundations, methodologies, standards, procedures, applications, and then conduct an in-depth study and research in its challenges, impacts, and future trends through weekly exercises and discussions, extensive reading and writing, comparative analysis and research, and case studies and critiques. Competence Statement: Students in this course will study and comprehend the foundations, principles, theories, techniques and practice of this cutting edge field well enough to be able to define the scope of the field, outline the new procedures, familiar with the advanced technology, and conduct preliminary research on a self-framed emerging problem in the field.

Full course description for Cloud Forensics

Computer Security (4-8 credits)

Choose one

Database security has an immense impact on the design of today's electronic information systems. This course will provide an overview of database security concepts and techniques and discuss new directions of database security in the context of a connected commercial world. This course provides the information needed to develop, deploy and maintain a secure database solution. It exposes the pitfalls of database design, their means of identification and the methods of exploiting vulnerabilities.

Full course description for Database Security

In this course, students will examine the scope of cloud computing and forensics as a multi-disciplinary field, including its foundations, methodologies, standards, procedures, applications, and then conduct an in-depth study and research in its challenges, impacts, and future trends through weekly exercises and discussions, extensive reading and writing, comparative analysis and research, and case studies and critiques. Competence Statement: Students in this course will study and comprehend the foundations, principles, theories, techniques and practice of this cutting edge field well enough to be able to define the scope of the field, outline the new procedures, familiar with the advanced technology, and conduct preliminary research on a self-framed emerging problem in the field.

Full course description for Cloud Forensics

This course is the first of a two-course series that introduces the interdisciplinary field of cyberspace security. The technical foundation for the cybersecurity defender is a particular combination of network, operating system, hardware (mobile/desktop/server) and software engineering skills, all of which are required to protect and defend modern systems, networks and information assets. Students will explore in-depth technical foundations which underpin cybersecurity threats and corresponding defenses. Through hands-on training students will gain necessary skills to begin supporting and implementing cyberspace security. This course will cover the following topics: Security and Risk Management (security governance principles, compliance, legal and regulatory issues, professional ethic, and security policies), Asset Security (information and asset classification and ownership, data security controls and handling requirements), Security Engineering (secure Engineering processes,…

Full course description for Cyberspace Security Engineering I

This course will be the second of a two-course series that introduces the interdisciplinary field of cyberspace security. The technical foundation for the cybersecurity defender is a particular combination of network, operating system, hardware (mobile, desktop, and server) and software engineering skills, all of which are required to protect and defend modern systems, networks and information assets. Students will explore in-depth technical foundations which underpin cybersecurity threats and corresponding defenses. Through hands-on training using Cyber Range students will gain necessary skills to begin supporting and implementing cyberspace security. This course will cover the following topics: Identity and Access Management (Physical and logical assets control, authentication, access control attacks, and access provisioning lifecycle), Security Assessment and Testing (Assessment and test strategies, security process data, and security control testing), Security Operations …

Full course description for Cyberspace Security Engineering II

The course will provide students with foundational concepts and practical skills in the field of cyber threat intelligence that can be leveraged to defend against sophisticated network intrusions and loss of proprietary information. The course will discuss various phases of the intelligence lifecycle including developing intelligence requirements, collecting, analyzing, and disseminating information; and using cyber threat intelligence to improve security at the tactical, operational, and strategic levels.

Full course description for Cyber Threat Intelligence

Choose an additional course in Distribute Systems or Computer Security courses listed above (4 credits)
+ Electives (16 credits)

Electives may include additional course work in Distributed Systems and Computer Security listed above or may be taken from other advanced topics such as the courses listed below. Up to 8 credits of graduate work in Math/Stat are acceptable.

This course is the study of fundamentals of computer simulation modeling and queuing theory at graduate level. Computer simulation can be an extremely powerful tool, yet few in industry seem well trained in the design, implementation, and interpretation of a useful simulation experiment. The instructional materials in this course are designed to familiarize the students with the use of computer simulation and queuing theory. Students will be taught to focus simulation studies on tractable and intractable questions, to draw conclusions from simulations results, and to bring these conclusions into appropriate domain context. This is a hands-on course. Students are taught simulation theory through the practice of developing models and of writing software. Examples of application areas include: Computer Networks, Bioinformatics, Public Health Issues, Trends in Education, Trends in Industry and many, many more. Topics include: Introduction to Simulation; Introduction to the Arena…

Full course description for Simulation Modeling and Queuing Theory

Artificial Intelligence (AI) is the field of studying the synthesis and analysis of computational agents that act intelligently. AI has several areas of study, such as Searching, Reasoning, Learning, and Knowledge Representation. Searching helps the agent to reason and decide what to do, to determine the sequence of actions that will take to achieve its goals. Learning is the ability of the agent to improve its behavior based on experience. And knowledge representation is used to represent the individuals and the relationships between them, so the agent will be able to represent its own reasoning and use it to build knowledge¿ based systems. This course focuses on searching algorithms, machine learning algorithms, and ontologies and knowledge¿based systems.

Full course description for Artificial Intelligence and Machine Learning

Through analysis of research literature, lectures, and hand-on projects, this course aims to equip students with the fundamental knowledge and skills necessary to design, develop, deploy, and manage cloud-based solutions. Topics covered include fundamental cloud concepts (e.g., virtualization, multi-tenancy, elasticity, scalability, fault tolerance, and reliability), categories of cloud services (e.g., compute, storage, networking, data management and analytics), and cloud programming models and frameworks (e.g., containers, serverless, Hadoop, and web services). Students will develop a software system utilizing cloud services provided by popular cloud service providers (e.g., Amazon Web Services (AWS), Google Cloud Platform (GCP) or Microsoft Azure).

Full course description for Cloud Application Design and Development

This course is the study of fundamentals of design and implementation of real-time operating systems. Most embedded computer systems have dedicated microprocessors as their computational and controlling elements and run real-time operating systems. This course covers concepts, programming languages, tools, hardware, and methodologies used in the construction of real-time operating systems and their peripheral components. Topics include: applications of real-time operating systems; communications between PC computers and embedded systems; fundamental concepts of scheduling (multitasking and interruptions); introduction of basic hardware components used in most real-time operating systems; Hardware description language[VHDL]; and the writing of a real-time operating system [RTOS] using industrial standard C language, debugging, and loading the code to the target hardware.

Full course description for Real Time Operating Systems

This course provides an introduction to embedded computing, control systems, single-board computers and microcontrollers and Internet of Things. Topics include embedded software development, networked devices, protocols and controls, security, and monitoring. This course will focus on recent research and will include a hands-on/in-class lab component, involving digital logic design and analysis and will include a multi-week group design and development project. (Prerequisite: graduate standing in computer science)

Full course description for Embedded Computing with Control Systems and Internet of Things

This course presents Software Engineering topics of interest to students in the graduate Computer Science program. Topics vary with each offering of this course, but will be related to Software Engineering concepts such as verification, validation, secure systems, quality control, or formal methods. Check the class schedule for details about topics and course prerequisites.

Full course description for Contemporary Issues in Software Engineering

This graduate course studies the logical foundations of mathematical analysis using fractal examples to direct our intuition. The tools of analysis give us the machinery for constructing the most complicated mathematical objects, which are used to solve the problems in differential equations, probability, geometry, calculus and functional analysis. Learning how to construct fractals of various types helps us understand the apparatus researchers use to construct solutions to differential equations, stochastic processes, and the most difficult extremal problems. These solutions form the basis of the theories of all classical hard sciences, as well as many new fields such as signal processing, control theory and systems engineering. We will explore the topics of metric spaces and point set topology, measure theory and probability, Hausdorff dimension and chaotic dynamics. This course will serve students with a bachelor's degree in mathematics or closely related fields wishing to deepen…

Full course description for Analysis and Fractals

The purpose of this course is to provide students with a sound conceptual understanding of the role that data science and analytics play in the decision-making process. The availability of massive amounts of data, improvements in analytic methodologies, and substantial increases in computing power have all come together to result in a dramatic upsurge in the use of data science and analytical methods. This course can be taken by students who have previously taken a course on basic statistical methods as well as students who have not had a prior course in statistics. Topics include models for summarizing, visualizing, and understanding historical data to assist in gaining insights for predicting possible future outcomes using descriptive, predictive and prescriptive data analytic techniques. Examples include applications in finance, human resources, marketing, health care, supply-chain, government and nonprofits, and sports.

Full course description for Data Science and Analytics

This course covers the techniques for construction, analysis and evaluation of mathematical models that are used to aid in the understanding of questions arising in the natural, physical and social sciences, business and engineering. Students will learn how to implement mathematical models on the computer and how to interpret and describe the results of their computational experiments.

Full course description for Mathematical Modeling and Its Applications

This course presents a broad introduction to the subject of dynamical systems, both continuous and discrete. We analyze the existence, uniqueness, stability, and control of linear and nonlinear systems and the topics of bifurcation, flows, limit cycles, chaos, and catastrophe theory. This course will serve students with bachelor¿s degrees in mathematics or closely related fields wishing to deepen their mathematics education, and technical professionals, high school teachers, and math instructors seeking professional development or qualifications for teaching community college courses.

Full course description for Dynamical Systems

This course covers divisibility; congruences and residues, including the Chinese Remainder Theorem; primes and their distribution; the Euler-phi function; quadratic reciprocity; public-key cryptography, particularly the RSA cryptosystem; elliptic curves and their group structure.

Full course description for Number Theory

This course is the application of statistical knowledge in reading, evaluating, and utilizing research findings. Students will know and understand the advanced statistical methods applied in the health sciences, and the students will develop the skills required to critique research, especially nursing research, and to have an understanding of the fundamental requirements of conducting their own research studies.

Full course description for Advanced Biostatistics in Health Research

+ Research Seminar and Practical Research Project or Thesis (6 credits)

After completion of the research seminar course, an applied research project or an original thesis in computer science, 4-credits of Student Designed Independent Study (SDIS) course (ICS 660I), must also be completed as approved by the student's MSCS Thesis/Project advisory committee Chair and 2 additional committee members. The completion of this SDIS must include a written report and a public presentation.

In this course, the student will perform the following activities: search the literature on specific areas, read papers in a selected area, study the methodologies used in the applied computer research, write and submit a survey paper based on the reading, and make an oral presentation of the results. It should be taken no later than the second semester.

Full course description for Research Seminar

Student-designed independent studies give Metropolitan State students the opportunity to plan their own study. This type of independent learning strategy can be useful because it allows students: to study a subject in more depth, at a more advanced level; to pursue a unique project that requires specialized study; to draw together several knowledge areas or interests into a specialized study; to test independent learning capabilities and skills; or to use special learning resources in the community, taking advantage of community education opportunities which, in themselves, would not yield a full college competence. Students should contact their academic advisor for more information.

Full course description for Information and Computer Sciences Student Designed Independent Study