M.S. Computer Science
36
Credit Hours
20
Months (Avg.)
Class Type
Face-to-face, Online coursework
Next Start Date
Jan 3, 2022
Placement Tests
GMAT/GRE not required for admission

Upgrade your organizational impact with a master's in computer science

No ifs, ands or buts -- business depends on technology. Perhaps that explains the high demand for computer science leaders. With an 13% projected job growth rate, many organizations (from Fortune 500 companies to startups) need qualified computer science professionals to help them meet their software needs. These powerhouse organizations rely on prepared practitioners to deliver robust software solutions to help them increase efficiency, reduce turnaround time, and maximize investment.

Take the next step toward your degree!

Request free program information or submit your online application.

20-Month Completion

Finish your master's in computer science faster.

Leading Architectural Tools

Get hands-on experience with industry-standard technologies.

Customizable Program

Tailor your master's degree program to your interests.

Real-World Practitioners

Learn from experienced technology leaders.

100% Online Classes

Earn your degree around your schedule.

Game-Changing Skills

Play an important role in communicating emerging technologies to stakeholders.

Program Overview

Architect the software futures of Fortune 500 companies

If there’s one certainty in business, it’s this: business depends on technology. Which explains why the demand for computer science jobs is at an all-time high. In fact, job growth in the field is projected to increase by an astounding 13% through 2031.*

Fortune 500 companies, in particular, need computer science graduates to help them meet their large-scale software needs. That’s because getting their systems solutions right is often the difference between success and failure. These powerhouse organizations rely on qualified practitioners to deliver robust software design, architecture and implementation that increases efficiency and reduces turnaround time, all while maximizing reuse and minimizing rework.

Deepen your skills in software design and development with hands-on coursework

Franklin University's transfer-friendly online M.S. in Computer Science (MSCS) program will teach you to interface with organizational stakeholders and translate an evolving set of needs into high-level systems requirements. You'll learn how to integrate new systems within the broader hardware and software environment, too, as well as implement the solution with the agile software engineering process.  

Through our practical, hands-on coursework you'll gain experience with leading implementation tools and cutting edge software analysis. And you'll be introduced to other critically needed skills, such as algorithm analysis, distributed systems, verification and testing, and database design.

Tailor your master's program around your areas of interest

Franklin lets you further increase the relevance to your job or career path with program electives in your area of interest. Choose from pathways in data analytics, cybersecurity or software systems. Other elective options include internship and independent study opportunities.

Our Computer Science master's degree program curriculum is regularly reviewed by an advisory board strongly represented by leading companies in industry, including Herb Berger, Director, Enterprise Architecture for Cardinal Health. That means what you learn at Franklin is relevant to the needs of the industry now and stays relevant over time.

Complete our career-enhancing program in as few as 20 months

With Franklin's M.S. in Computer Science program, you'll get the expertise you need to take on the technology challenges facing business. Whether you're looking to advance your career to a senior level or work with a larger organization with more sophisticated needs, Franklin's reputation will prepare you for your next move. 

With no GRE requirement, you can start immediately and faster, too. Plan your education around your life by taking just two classes over five, 12-week terms. Most students are able to complete Franklin's M.S. in Computer Science program in as few as 20 months. 

If you’ve already taken graduate-level computer science courses, Franklin offers course-for-course credit that saves you time and money toward your master’s degree. To see if your previous coursework will transfer, you’ll need to submit a syllabus for the course(s) you’d like to have evaluated for transfer credit. Your admissions advisor will be happy to assist you in any way.  

Earn your degree from a university built for busy adults

Earn your degree on your terms by taking classes 100% online. Accredited and nonprofit, Franklin was built from the ground-up to satisfy the needs of adult learners. Our seamless admission process and team of academic advisors will help ease your transition to becoming a student, while our flexible course schedules help to balance your education with work, family and life. Get started on your future today.

*Source information provided by Economic Modeling Specialists International (EMSI)

Read more >

Mahlet B.

M.S. Computer Science '20

"Receiving my degree from Franklin University means a lot to me. I was always taught that education is a path to reach ones' destiny, create a cultivated mind and enable growth personally and professionally."

Curriculum & Course Descriptions

36 Semester Hours
Major Area Required
COMP 611 - Advanced Data Structures and Programming (4)

This course covers key knowledge and skills for advanced software development using the object-oriented approach. The student learns, manipulates and reflects on nonlinear data structures such as trees and heaps. Recursive algorithms, sorting algorithms, algorithm efficiency, and advanced design patterns are addressed. To support the advanced concepts and principles of software development, the student will design, code, test, debug, and document programs with increased scale and complexity using industry's best practices (such as GitHub) and the Java programming language.

COMP 620 - Analysis of Algorithms (4)

This course covers various algorithm design paradigms, mathematical analysis of algorithms, empirical analysis of algorithms and NP-completeness.

COMP 630 - Issues in Database Management (4)

This course focuses on the fundamental design considerations in designing a database. Specific topics include performance analysis of design alternatives, system configuration and the administration of a popular database system. The course also offers an in-depth analysis of the algorithms and machine organizations of database systems.

COMP 655 - Distributed Systems (4)

This course introduces the design of distributed computing systems and distributed application programming. Major concepts of distributed systems covered include: transparency, heterogeneity, process communication, consistency, fault tolerance, and security. Students will also learn to develop a real-world distributed application as a RESTful Web-service on an application server.

COMP 671 - Verification and Testing (4)

This course focuses on the issues of delivering high quality software, especially in large complex systems. Topics covered include testing strategies (black box, white box, regression, etc.), unit testing, system integration, system verification and support tools. It also will reinforce the need for requirements that are testable and traceable from the early design stages.

COMP 691 - Capstone (4)

This course, the final one in the Master of Science - Computer Science program, challenges students to research a current topic of interest in Computer Science and produce an original paper and presentation on the topic. In addition to the research paper, students are introduced to the economics of software development and the tools needed to estimate the cost of a software development project for management in a corporate environment. The last topic in the course is a discussion of ethics as it relates to Information Technology. Current topics in ethics will be discussed through the use of relevant case studies.

Major Electives

At least 12 credits from the following courses:

MATH 601 - Introduction to Analytics (4)

This course provides an introductory overview of methods, concepts and current practices in the growing field of Data Analytics. Topics to be covered include data collection, analysis and visualization as well as statistical inference methods for informed decision-making. Students will explore these topics with current statistical software. Some emphasis will also be given to ethical principles of data analytics.

DATA 605 - Data Visualization & Reporting (4)

This course focuses on collecting, preparing, and analyzing data to create visualizations, dashboards, and stories that can be used to communicate critical business insights. Students will learn how to structure and streamline data analysis projects and highlight their implications efficiently using the most popular visualization tools used by businesses today.

DATA 611 - Applied Machine Learning (4)

This course explores two main areas of machine learning: supervised and unsupervised. Topics include linear and logistic regression, probabilistic inference, Support Vector Machines, Artificial Neural Networks, clustering, and dimensionality reduction, and programming.

ISEC 610 - Information Assurance (4)

This course covers the fundamentals of security in the enterprise environment. Included are coverage of risks and vulnerabilities, threat modeling, policy formation, controls and protection methods, encryption and authentication technologies, network security, cryptography, personnel and physical security issues, as well as ethical and legal issues. This foundational course serves as an introduction to many of the subsequent topics discussed in depth in later security courses.

ISEC 620 - Software and App Security (4)

Today, software is at the heart of nearly every business from finance to manufacturing. Software pervades everyday life in expected places like phones and computers but also in places that you may not consider such as toasters, thermostats, automobiles, and even light bulbs. Security flaws in software can have impacts ranging from inconvenient to damaging and even catastrophic when it involves life-critical systems. How can software be designed and built to minimize the presence of flaws or mitigate their impacts? This course focuses on software development processes that identify, model, and mitigate threats to all kinds of software. Topics include threat modeling frameworks, attack trees, attack libraries, defensive tactics, secure software development lifecycle, web, cloud, and human factors.

ISEC 640 - Cryptography (4)

The cryptographic primitives of enciphering/deciphering and hashing are the two main methods of preserving confidentiality and integrity of data at rest and in transit. As such, the study of cryptographic techniques is of primary interest to security practitioners. This course will cover the important principles in historical and modern cryptography including the underlying information theory, mathematics, and randomness. Important technologies such as stream and block ciphers, symmetric and asymmetric cryptography, public key infrastructure, and key exchange will be explored. Finally, hashing and message authentication codes will examined as a way of preserving data integrity.

COMP 645 - Object-Oriented Design & Practice (4)

This course surveys current practices in software development and software design, especially in the area of object-oriented design. The course will examine and contrast current and leading edge methodologies and practices, including agile, extreme programming, test-driven design, patterns, aspect-oriented programming, model-driven architecture, Unified Modeling Language, and integrated development environments.

COMP 650 - System Architecture & Engineering (4)

This course covers topics in software systems engineering. Its scope is the design of the overall architecture for software systems with emphasis on distributed architectures. The issues in an architecture centered software development cycle and project management are addressed.

COMP 670 - Application of Artificial Intelligence (4)

This course is an introduction to Artificial Intelligence (AI) from an applied perspective. After an introduction of some basic concepts and techniques (such as searching and knowledge representation), the course illustrates both the theoretical foundation and application of these techniques with examples from a variety of problems. The course surveys a wide range of active areas in AI such as machine learning, artificial neural networks, evolutionary computing, robotics, intelligent agents and bio-inspired AI approaches. It strikes a balance between engineering approaches and theory. Exercises include hands-on application of basic AI techniques as well as selection of appropriate technologies for a given problem. The principal topics in the selected areas are also coupled with projects where groups of students will participate in the creation of AI-based applications.

COMP 610 - Internship in Computer Science (1-4)

This course provides MSCS students the opportunity to further their education with relevant work experience in the field of Computer Science. This internship is an ongoing seminar between the student, faculty and the employment supervisor. It involves a Learning Contract (Curricular Practical Training [CPT] Information, or other), periodic meetings with the faculty representative, and professional experience at a level equivalent to other electives of the MSCS program. Specification of the materials to be submitted is established in the learning contract. Participation cannot be guaranteed for all applicants.

COMP 699 - Independent Studies in Graduate Computer Science (1-4)

Independent studies courses allow students in good academic standing to pursue learning in areas not covered by the regular curriculum or to extend study in areas presently taught. Study is under faculty supervision and graded on Pass/No Credit basis. For international students, curricular practiced training may be used as an independent study with approval of program chair. (See the "Independent Studies" section of the Academic Bulletin for more details.)

Optional Focus Areas

Students may complete a focus area to fulfill the Major Elective requirement.

OR

Data Analytics:

MATH 601 - Introduction to Analytics (4)

This course provides an introductory overview of methods, concepts and current practices in the growing field of Data Analytics. Topics to be covered include data collection, analysis and visualization as well as statistical inference methods for informed decision-making. Students will explore these topics with current statistical software. Some emphasis will also be given to ethical principles of data analytics.

DATA 605 - Data Visualization & Reporting (4)

This course focuses on collecting, preparing, and analyzing data to create visualizations, dashboards, and stories that can be used to communicate critical business insights. Students will learn how to structure and streamline data analysis projects and highlight their implications efficiently using the most popular visualization tools used by businesses today.

DATA 611 - Applied Machine Learning (4)

This course explores two main areas of machine learning: supervised and unsupervised. Topics include linear and logistic regression, probabilistic inference, Support Vector Machines, Artificial Neural Networks, clustering, and dimensionality reduction, and programming.

OR

Cybersecurity:

ISEC 610 - Information Assurance (4)

This course covers the fundamentals of security in the enterprise environment. Included are coverage of risks and vulnerabilities, threat modeling, policy formation, controls and protection methods, encryption and authentication technologies, network security, cryptography, personnel and physical security issues, as well as ethical and legal issues. This foundational course serves as an introduction to many of the subsequent topics discussed in depth in later security courses.

ISEC 620 - Software and App Security (4)

Today, software is at the heart of nearly every business from finance to manufacturing. Software pervades everyday life in expected places like phones and computers but also in places that you may not consider such as toasters, thermostats, automobiles, and even light bulbs. Security flaws in software can have impacts ranging from inconvenient to damaging and even catastrophic when it involves life-critical systems. How can software be designed and built to minimize the presence of flaws or mitigate their impacts? This course focuses on software development processes that identify, model, and mitigate threats to all kinds of software. Topics include threat modeling frameworks, attack trees, attack libraries, defensive tactics, secure software development lifecycle, web, cloud, and human factors.

ISEC 640 - Cryptography (4)

The cryptographic primitives of enciphering/deciphering and hashing are the two main methods of preserving confidentiality and integrity of data at rest and in transit. As such, the study of cryptographic techniques is of primary interest to security practitioners. This course will cover the important principles in historical and modern cryptography including the underlying information theory, mathematics, and randomness. Important technologies such as stream and block ciphers, symmetric and asymmetric cryptography, public key infrastructure, and key exchange will be explored. Finally, hashing and message authentication codes will examined as a way of preserving data integrity.

OR

Software Systems:

COMP 645 - Object-Oriented Design & Practice (4)

This course surveys current practices in software development and software design, especially in the area of object-oriented design. The course will examine and contrast current and leading edge methodologies and practices, including agile, extreme programming, test-driven design, patterns, aspect-oriented programming, model-driven architecture, Unified Modeling Language, and integrated development environments.

COMP 650 - System Architecture & Engineering (4)

This course covers topics in software systems engineering. Its scope is the design of the overall architecture for software systems with emphasis on distributed architectures. The issues in an architecture centered software development cycle and project management are addressed.

COMP 670 - Application of Artificial Intelligence (4)

This course is an introduction to Artificial Intelligence (AI) from an applied perspective. After an introduction of some basic concepts and techniques (such as searching and knowledge representation), the course illustrates both the theoretical foundation and application of these techniques with examples from a variety of problems. The course surveys a wide range of active areas in AI such as machine learning, artificial neural networks, evolutionary computing, robotics, intelligent agents and bio-inspired AI approaches. It strikes a balance between engineering approaches and theory. Exercises include hands-on application of basic AI techniques as well as selection of appropriate technologies for a given problem. The principal topics in the selected areas are also coupled with projects where groups of students will participate in the creation of AI-based applications.

Corequisites
COMP 501 - Foundations of Programming (4)

This course covers fundamental programming principles. Students will learn about the basic elements of a computer program such as data types, assignments, conditional branching, loops, functions, recursion, basic data structures, program debugging, and testing.

OR ITEC 136 - Principles of Programming (4)

This course covers fundamental programming principles for individuals with at least some programming background. Major themes are structured programming, problem solving, algorithm design, top-down stepwise refinement, and software lifecycle. Topics will include testing, data types, operators, repetition and selection control structures, functions, arrays, and objects. Students will design, code, test, debug, and document programs in a relevant programming language.

OR COMP 111 - Introduction to Computer Science & Object-Oriented Programming (4)

This course provides an introduction to software construction using an object-oriented approach. The student learns and reflects on problem analysis, object-oriented design, implementation, and testing. To support the concepts and principles of software construction, the student will design, code, test, debug, and document programs using the Java programming language. Basic data types, control structures, methods, and classes are used as the building blocks for reusable software components. Automated unit testing, programming style, and industrial practice are emphasized in addition to the object-oriented techniques of abstraction, encapsulation, and composition.

COMP 511 - Foundation Data Struc & Obj Orntd Design (4)

This course continues the object-oriented approach to intermediate-level software development. The student will learn and reflect on fundamental object-oriented analysis techniques, basic design patterns, and linear data structures such as lists and queues. To support the concepts and principles of software development, the student will design, code, test, debug, and document programs using the Java programming language.

OR COMP 121 - Object-Oriented Data Structures & Algorithms I (4)

This course continues the object-oriented approach to software construction. The student learns and reflects on advanced object-oriented techniques, algorithm efficiency, class hierarchies, and data structures. To support the concepts and principles of software construction, the student will design, code, test, debug, and document programs using the Java programming language. Design principles, I/O, exception handling, linear data structures (lists, stacks, and queues), and design patterns are emphasized in addition to the object-oriented techniques of inheritance and polymorphism.

MATH 503 - Foundations of Mathematics for Computing (4)

This course introduces students to fundamental algebraic, logical, and combinational concepts in mathematics that are needed in upper division computer science courses. Topics include integer representation; algorithms; modular arithmetic and exponentation; discrete logarithms; cryptography; recursion; primality testing; number theory; graphs and directed graphs; trees; and Boolean Algebra.

OR MATH 320 - Discrete Mathematics (4)

This course introduces students to fundamental algebraic, logical and combinational concepts in mathematics that are needed in upper division computer science courses. Topics include logic; sets, mappings, and relations; elementary counting principles; proof techniques with emphasis on mathematical induction; graphs and directed graphs; Boolean algebras; recursion; and applications to computer science. Please note: A book fee will be included in your tuition charges for required course materials.

Students with an undergraduate degree in computer science will be admitted without future prerequisites. However, the students will be expected to possess intermediate Java programming skills as determined by completing COMP 121 or COMP 511, having a Java SE 8 programmer certification from Oracle, or a portfolio of Java-related examples that would include the fundamentals of object-oriented programming, linear and non-liner data structures (stacks, queues, lists, etc.)

Students without a computer science degree will need to have credit for the above Franklin University courses or the equivalent undergraduate course work for the prerequisites at an institutionally (formerly regionally) accredited institution OR appropriate relevant work experience. Graduate prerequisite courses (500 level) must be completed with a grade of "C" or better. Undergraduate prerequisite courses must be completed with a grade of "C" or better. Work experience as a software engineer, developer, or programmer analyst will be evaluated by the program chair upon request. Resumes, work samples, and personal interviews may all be used to determine the depth of knowledge in these areas.

Microcredentials Align with Job Essentials

In today’s dynamic work environments, adaptive professionals thrive. A microcredential - either as a stand-alone course or integrated into your degree program - is a short, skill-specific recognition that enables you to demonstrate your competency in a distinct area. Like Franklin’s degree programs, microcredentials are aligned with market and industry demand to ensure what you learn can be put to use right away. Microcredentials are easily shared via digital badges and can be stacked to create a unique portfolio of in-demand skills.

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Program Details

Manasa K.

M.S. Computer Science '20

"Thank you Franklin University, for helping me reach this important milestone in my career."

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Employment Outlook

13%

From 2021-2031 jobs in Computer Science are expected to increase by 13%

All Occupations

2021
5,400,282 jobs
2031
6,080,567 jobs
Show Details >

Computer and Information Systems Managers

2021
493,607 jobs
2031
549,484 jobs

Software Developers and Software Quality Assurance Analysts and Testers

2021
1,600,098 jobs
2031
1,924,125 jobs

Web Developers and Digital Interface Designers

2021
198,907 jobs
2031
222,454 jobs

Computer User Support Specialists

2021
699,494 jobs
2031
769,787 jobs

Medical Dosimetrists, Medical Records Specialists, and Health Technologists and Technicians, All Other

2021
337,182 jobs
2031
371,327 jobs


Source information provided by Economic Modeling Specialists International (EMSI).

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