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Computer Science PSM

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 or online, 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.

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

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 38 credits required to graduate.

Application instructions

Questions?

For questions about applying to the Computer Science PSM program, email graduate.studies@metrostate.edu (preferred) or call 651.793.1302.

Deadlines

Fall semester

June 15: United States applicants
May 1: Overseas international applicants

Spring semester

November 15: United States applicants
October 1: Overseas international applicants

There are no summer admissions. The CSC Department graduate program director will inform applicants of the admission decision for fall semester by July 1 and for spring semester by November 30.

Application file

A complete application file consists of two parts (three for international students). Begin the application process by first applying online using the online graduate application. All application materials become the property of Metropolitan State University and are not returned. All application materials must be received in the Graduate Studies Office by the application deadline, and will be made available to the MSCS Graduate Admissions Committee for review.

Part One - Apply Online

  • Complete the Online Graduate Programs Application. You will use your Minnesota State StarID to complete the application.
    • If you don't have a Minnesota State StarID, you will create one at the beginning of the application.
    • If you encounter difficulty with the online application please email graduate.studies@metrostate.edu.
  • Pay the non-refundable application fee online using the online application. The current fee of $20 is waived for graduates of Metropolitan State University.
  • Submit official transcripts or transcript evaluation showing a baccalaureate degree or equivalent earned from an accredited institution with grade-point average or narrative description describing courses completed.
    • Transcripts from ALL schools attended after high school and from any graduate or professional programs are required.
    • All transcripts from non-U.S. schools must be evaluated (course-by-course) by:
    • Educational Credential Evaluators (third-party website) (preferred)
    • World Education Services (third-party website)
    • Electronic transcripts (preferred) sent to graduate.studies@metrostate.edu
    • Paper transcripts sent directly from the sending institution (preferred) to:
    • Metropolitan State University
      Attn: Graduate Admissions
      700 East Seventh Street
      Saint Paul MN 55106

Part two - upload remaining documents

Once you've applied online you can login to the Applicant Portal using your StarID and password and upload remaining documents and check on the status of your application. To be considered for admission you must provide:

  • Two Letters of Recommendation (required); Upload
    • From professional sources, at least one of which is a current or former college instructor, and at least one of which is a current or former work supervisor.
  • Current Resume (required); Upload
    • Documenting relevant work experience
  • Purpose Essay (required); Upload
    • Describing why you wish to enroll in the program
    • Address why you feel you have the necessary background to succeed in the graduate program (for applicants without a computer science major)
  • Official GRE Scores (recommended);
    • General or Computer Science Test
    • Send to Graduate Studies Office at address below

Part three - international students only

In addition to parts one and two, international applicants in the U.S. or abroad, on any type of visa, are required to provide this information for admission to the university.

  • Proof of English proficiency (official TOEFL, IELTS, or Duolingo score)
    • Minimum scores: TOEFL 80 IBT, 213 CBT, 550 PBT, IELTS 6.5, or Duolingo 105-110
    • IBT=internet based; CBT=computer based; PBT=paper based
  • Financial statement (F1 visa only)
  • Immunization records
  • Copies of passport, visa, I-94, and international home address and phone numbers

Courses and Requirements

SKIP TO COURSE REQUIREMENTS

Guidelines for completing the Computer Science PSM 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 38 credits of approved work, which include:

  • one course in computer security (4 credits) 
  • one course in distributed systems (4 credits) 
  • one course in Software Engineering / Data and Database Management (4 credits)
  • two courses in Team and Project Management (6 credits)
  • one course in writing and communications (4 credits)
  • a set of elective courses covering advanced material in computer science and information technology.  (12 credits) 
  • completion of a contemporary project/study in computer science and information technology through either an internship or an student designed independent study (SDIS) course with a CSC department graduate faculty member as the evaluator along with a written report and a public presentation (4 credits)

Program Requirements (38 credits)

+ Core (22 credits)
Group I: Distributed Systems (4 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

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

Group II: Computer Security (4 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

Group III: Software / Engineering / Data Management (4 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

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

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

Group IV: Team and Project Management (4 credits)

Graduate courses covering topics such as team and project management, decision making, and related topics. Take one of the suggested courses listed below or a related course approved by the director of the program.

This course provides a systematic and comprehensive overview of project leadership and management. Topics covered include all aspects of project management from project initiation issues, RFP formulation, proposal decisions, preparation, and evaluation, project planning and implementation to organization, risk assessment, negotiation, and conflict resolution. Also included are project planning techniques such as PERT, CPM, Earned Value Analysis, and project monitoring and simulation using Microsoft Project software.

Full course description for Project Management

This course provides a comprehensive study of project risk management, including concepts, methodologies, and applications. It includes systematic approaches to risk identification, risk modeling, risk impact assessment, respond planning, and documentation. Decision science methods such as System Dynamics, Monte Carlo Simulation, Decision Analysis, Probability Analysis, Analytic Hierarchy Process, and Scenario Analysis will be utilized in risk assessment. Use of computer software in risk analysis will also be emphasized.

Full course description for Project Risk Management

Group V: Writing, Communications, and Research (6 credits)

All students will be evaluated before placement into graduate writing courses. A student lacking the necessary writing skills will be required to complete additional writing classes, where those credits will not be applied toward this graduate program.

Choose the following courses or a related course approved by the director of the program. All students need to take ICS 698.

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

+ Electives (12 credits)

All ICS graduate courses or the courses approved by the director of the program can be used as electives. In addition, up to two graduate Math courses, or 8 credits, can be counted as electives. No single course can be used twice for meeting the requirements.

+ Internship or Independent Study on Contemporary Issues (4 credits)

Students are highly encouraged to take a 4-credit internship at a graduate level with their best effort . However, students may alternatively take a 4-credits of Student Designed Independent Study (SDIS) course (ICS 660I) on a contemporary issue in computer science and information technology with a CSC graduate faculty member as an evaluator. A proposal for taking this SDIS must be approved by the faculty who works as the evaluator and the director of the program. The completion of this SDIS course must include a written report and a public presentation, and is approved by the faculty who works as the evaluator and the director of the program. For more information, please contact the director of the program.

Students obtain internships in selected areas of study to gain deeper understand of knowledge, skills and the context of a given field. Site supervisors give guidance and direction to customized internship projects. Faculty members serve as liaisons between the internship sites and the university, providing information to students and potential supervisors and supervising the learning experience. Students should contact the Institute for Community Engagement and Scholarship (ICES) at Metropolitan State University for more information.

Full course description for Information and Computer Sciences Individual Internship

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

Internship Guidelines

1) Requires an approval by the director of CSC graduate programs in Computer Science;

2) Should involve at least 300 hours of work of which at least 250 hours must be spent on highly technical activities involving design or implementation or both;

3) The level and quality of the work must be appropriate for a professional with a master's degree in computer science;

4) Must culminate in a technical project to be evaluated by a CSC graduate faculty member and the industry partner offering the internship. The outcome of the internship must include a written report and a public presentation.