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Class Type
Next Start Date
About Start Dates
Additional future start dates include:
Fall 2024
Aug 19, 2024Sep 30, 2024
Spring 2025
Jan 6, 2025Feb 17, 2025
Start dates for individual programs may vary and are subject to change. Please request free information & speak with an admission advisor for the latest program start dates.
Placement Tests
Placement Information
Broaden your skills with a M.S. in computer science with a focus in software systems
Businesses of every type rely on software for daily operations. Because software that can grow with the organization is key to profitability, skilled computer science professionals make the difference between record-breaking performance and going broke. With Franklin’s 100% online M.S. in Computer Science with a focus in Software Systems, you’ll learn how to design and build scalable modern software systems. By combining the core principles of Franklin’s contemporary master’s-level computer science program with additional courses in system architecture and artificial intelligence, you’ll be prepared for specialized roles.
Finish Fast
Finish your master's in as few as 20 months.
Leading Architectural Tools
Get hands-on experience with KNIME, Octave and Eclipse.
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.
M.S. in Computer Science - Software Systems Focus Overview
Gain foundational knowledge in the most important topics in modern software systems
Your M.S. in Computer Science-Software Systems provides hands-on coursework that ensures you’ll gain the skills needed to design the overall architecture for software systems with emphasis on distributed architectures, as well as those necessary to apply artificial intelligence. With a curriculum informed and taught by industry leaders, you’ll gain insight into challenges currently facing computer science professionals, as well as proven problem-solving strategies.
Master current practices in software development and object-oriented design
Dive deep into leading software development methodologies including agile, extreme programming, test-driven design, patterns, aspect-oriented programing, model-driven architecture, KNIME, OCTAVE, Eclipse and integrated development environments. You’ll emerge with the ability to appropriately apply current software methods to resolve design issues, as well as the ability to critique software using object-oriented principles.
Acquire a theory-to-practice understanding of artificial intelligence
You gain foundational knowledge and real-world skills in a wide range of artificial intelligence (AI) areas including machine learning, artificial neural networks, evolutionary computing, robotics, intelligent agents and bio-inspired AI approaches. You’ll put your learning to the test with hands-on group projects focused on creating AI-based applications.
Dimitri V.
M.S. Computer Science '22
"My professors taught me many valuable topics including applications of AI, testing, software architecture as well as industry best practices and insights. My classmates also helped me to learn and put the knowledge to use, and as a result, my experience at Franklin has shaped a complete perspective of the field of Computer Science for me."
Future Start Dates
Start dates for individual programs may vary and are subject to change. Please request free information & speak with an admission advisor for the latest program start dates.
Your Best Value M.S. Computer Science
Choose Franklin's M.S. Computer Science and get a high-quality degree that fits your life and your budget.
Non-Profit = Student Focused
Unlike for-profit universities, Franklin invests in student success, not shareholder gain.
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(After Partner Discount)
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Highly Recommended
98% of graduating students would recommend Franklin to their family, friends and/or colleagues.
**Source: Franklin University, Office of Career Development Student Satisfaction Survey (Summer 2023)
M.S. in Computer Science - Software Systems Focus Curriculum
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.
This course covers various algorithm design paradigms, mathematical analysis of algorithms, empirical analysis of algorithms and NP-completeness.
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. Note, this course has proctored exam(s). This exams requires additional technology, if student uses online proctoring.
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 also will learn to develop a real-world distributed application as a RESTful Web-service on an application server.
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.
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.
At least 12 credits from the following courses:
This course provides an introductory overview of methods, concepts, and current practices in the growing field of statistics and data analytics. Topics to be covered include data collection, data analysis and visualization as well as probability, statistical inference and regression 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.
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.
This course explores two main areas of machine learning: supervised and unsupervised. Topics include the fundamental concepts, roadmap of a machine learning project, classification algorithms, regression algorithms, dimensionality reduction, model evaluation, natural language processing, neural networks and deep learning, typical issues in real-world machine learning problems, and Python programming in data science.
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.
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.
The cryptographic primitives of enciphering/deciphering and hashing are the two main methods of preserving confidentiality and data integrity at rest and in transit. As such, the study of cryptographic techniques is of primary interest to security practitioners. This course will cover the essential principles in historical and modern cryptography, including the underlying information theory, mathematics, and randomness. Critical 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 be examined as a way of preserving data integrity.
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.
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.
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.
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.
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.)
Students may complete a focus area to fulfill the Major Elective requirement.
OR
Data Analytics:
This course provides an introductory overview of methods, concepts, and current practices in the growing field of statistics and data analytics. Topics to be covered include data collection, data analysis and visualization as well as probability, statistical inference and regression 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.
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.
This course explores two main areas of machine learning: supervised and unsupervised. Topics include the fundamental concepts, roadmap of a machine learning project, classification algorithms, regression algorithms, dimensionality reduction, model evaluation, natural language processing, neural networks and deep learning, typical issues in real-world machine learning problems, and Python programming in data science.
OR
Cybersecurity:
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.
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.
The cryptographic primitives of enciphering/deciphering and hashing are the two main methods of preserving confidentiality and data integrity at rest and in transit. As such, the study of cryptographic techniques is of primary interest to security practitioners. This course will cover the essential principles in historical and modern cryptography, including the underlying information theory, mathematics, and randomness. Critical 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 be examined as a way of preserving data integrity.
OR
Software Systems:
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.
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.
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.
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.
This course introduces programming to individuals with little or no programming background. The goal of this course is to introduce the fundamentals of structured programming, problem solving, algorithm design, and software lifecycle. Topics will include testing, data types, operations, repetition and selection control structures, functions and procedures, arrays, and top down stepwise refinement. Students will design, code, test, debug, and document programs in a relevant programming language.
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.
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.
This course continues the objected-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.
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 exponentiation; discrete logarithms; cryptography; recursion; primality testing; number theory; graphs and directed graphs; trees; and Boolean Algebra.
This course introduces students to fundamental algebraic, logical, and combinational concepts in mathematics that are needed in upper-division computer science courses. Topics include sets, mappings, and relations; elementary counting principles; proof techniques with an emphasis on mathematical induction; graphs and directed graphs; Boolean algebras; recursion; and applications to computer science.
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.
Free Master's Toolkit
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M.S. in Computer Science - Software Systems Focus Program Details
2023 - 2024 Tuition | Cost Per Credit |
---|---|
Standard tuition per credit hour | $670 |
Nursing programs |
$670 |
MSN-Generalist, MSN-Nurse Administrator and MSN-Nurse Educator | $598 |
RN-MSN Pathway (NURS 500) | $298 |
Current service members | $536 |
Graduate Prerequisite Courses (500 level) | $398 |
Graduate Prerequisite Courses (500 level) - International Students | $526 |
Know the Total Cost of Your Master’s from Day One
Save yourself the anxiety of annual tuition increases with Franklin’s Tuition Guarantee. The guarantee lets you lock-in what you’ll pay from first-term through graduation, as long as you remain actively enrolled. Not only will this help you maximize funding sources - like employer reimbursement or financial aid, it will keep you focused on earning your degree.
A learning outcome map functions as a roadmap to help guide students' progress through their program of study. Click HERE to view the M.S. Computer Science matrix.
To be awarded a graduate degree, students must:
- Successfully complete all required curriculum courses.
- Maintain a minimum cumulative grade point average (GPA) of 3.00.
- Complete the residency requirement:
- M.S. - Computer Science students must earn at least 24 of the 36 required credits at Franklin University – in any modality (on-site, hybrid or online).
- Complete the payment of all requisite tuition and fees;
- Not to be under disciplinary dismissal due to academic dishonesty or violation of Student Code of Conduct.
Transfer credit awarded based on experiential learning shall not count toward the residence requirement at Franklin University.
The admission process reflects Franklin University’s efforts to clearly identify the performance standards that can help predict student success in graduate level study. The selection criterion for Franklin’s graduate programs, as determined by faculty, emphasizes academic ability, contributory work experience, and personal qualities and characteristics.
Requirements for admission include having earned a bachelor's degree from an institution accredited by a Council for Higher Education Accreditation (CHEA)-recognized accrediting organization with a GPA of at least a 2.75 on a 4.0 scale.*
Applicants who earned at least a 2.5 GPA on a 4.0 scale in their earned bachelor’s degree will automatically be granted conditional enrollment status. Applicants who earned lower than a 2.5 GPA on a 4.0 scale in their earned bachelor’s degree can petition for conditional enrollment status to the program chair by submitting an essay detailing other criteria that the applicant believes should be considered to demonstrate their ability to be successful in a graduate program. This petition could include details on the applicant’s work experience, work ethic, level of professionalism, personality characteristics, level of difficulty of program of study previously completed, etc.
Conditional enrollment requires students to achieve a final grade of “B” (3.0 GPA) or better in the first graduate-level course of their program. If the terms of the conditional enrollment are not met, the student will not be allowed to advance in their program and will be unable to earn this graduate degree from Franklin University.
English Language Testing & TOEFL IELTS
Prospective students must demonstrate English Language Proficiency. The requirement is met through any of the following:
- The applicant is a citizen of a country where English is the official language.*
- The applicant has received a bachelor’s degree (or higher) from an institution located in an English-speaking country in which the courses were taught in English.*
- The applicant has earned appropriate scores on language proficiency exams taken within the last two years, as listed in the Academic Catalog.
*For more information about English proficiency, including a list of English-speaking nations, please visit our International Students section.
The University employs a team approach to planning, developing and maintaining its academic curriculum. An essential element of this process – and a key to the institution’s quality assurance practices – is the Program Advisory Board (and the associated Alumni Advisory Board). A diverse array of business and industry leaders make up these discipline-specific boards that provide guidance on theory-to-practice ideas, global business perspectives, and emerging topics in the field. Each academic year, Program Advisory Boards meet with Division Chairs and faculty for lively and engaged conversations, thus bringing members’ substantial professional experience and expertise into the classroom. In addition, some Division Chairs elect to engage Program Advisory Board members in the assessment of academic program outcomes.
Name | Organization | Title |
---|---|---|
Herbert Berger | Cardinal Health Inc. | Enterprise Architect |
David Blum | Hylant | Chief Info. + Innovation Officer |
Gary Clark | Columbus State Community College | Principal Investigator, Asst. Prof. |
Sean Erikson | Grange Insurance Companies | VP, Architecture + IT Strategy |
Mihajlo Jovanovic | JP Morgan Chase | Lead Software Engineer |
Perumal Ramasamy | NetJets | VP, Data + Quality Programs |
Srini Ramaswamy | Battelle | Head of Technology |
Gloria Rogiers | Columbus State Community College | Dean |
Paul Varner | Nationwide | Consulting IT Architect |
David Vasquez | Nationwide Insurance | Director, IT Applications |
Bradley West | HMB Inc. | Dir., Project Mgmt. Practice |
Byron White | Chemical Abstracts Service | Software Development Manager |
Manasa K.
M.S. Computer Science '20
"Thank you Franklin University, for helping me reach this important milestone in my career."
Career Opportunities
Software Developer
Software developers, also known as computer programmers, help design, create, install, test and maintain relevant and functional computer programs and device applications.
Software Architect
Software Architects collaborate in the creation and evolution of computer programs by providing software developers with platform, coding, and technical requirements.
AI Engineer
AI engineers blend data science and software engineering to create automated business solutions.
Employment Outlook
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
|
Computer and Information Systems Managers
2023 |
604,207 jobs
|
2033 |
722,584 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
2023 |
604,207 jobs
|
2033 |
722,584 jobs
|
Computer Systems Analysts
2023 |
593,007 jobs
|
2033 |
676,019 jobs
|
Source information provided by Lightcast.
Knowledge & Skillsets
Gain in-demand skills sought by employers with curriculum that teaches you:
- Translate business needs into high-level system requirements based on constraints, such as cost and schedule
- Expand and adapt architecture in support of growing user base and increasing application functionality
- Use best practice software development methodologies to ensure technical solutions development and implementation within specified timelines
- Set development, interface, and coding standards
- Evangelize software development methodologies through training, role modeling, and influencing product teams
- Oversee design, implementation, administration, and support of systems architecture, and infrastructure products and services to ensure security, availability, reliability, and scalability
- Create systems architecture design using state-of-the-art, rules-based architecture, object-oriented component analysis, and development and technologies, such as OOA, OOD, UML, and COBRA
- Propose necessary hardware and operating system cost savings, platform needs, communication protocols, middleware, interfaces, and algorithms
- Design software to maximize efficiency of current and planned hardware architectures
- Develop, maintain, and govern enterprise architecture (EA), including defining architecture review processes and integrating processes with related business methodologies and strategies
- Gain fundamental knowledge in major topics and research areas of artificial intelligence (AI) such as machine learning, artificial neural networks, evolutionary computing, robotics, intelligent agents and bio-inspired AI approaches
- Evaluate existing AI systems
- Apply techniques from AI to solve real-world problems
- Create detailed plans, cases, and scripts to test applications
- Perform functional, unit, and integration tests
- Ensure software meets design specifications and performance standards
- Review requirements specifications to ensure testability and compliance with quality control standards
- Define and track quality assurance metrics, such as defects, defect counts, test results and test status
- Develop dynamic, data-driven applications through mastery of relational database design, complex SQL queries, and transaction processing
- Write SQL database queries of medium to high complexity in support of data analysis and technical programming
- Apply relational database design best practices to efficiently build data models
- Implement data models, database designs, data access, and table maintenance codes
- Analyze large collections of data in order to inform and apply association rules and other techniques, such as genetic encoding, classification hierarchies, and regression analysis to decision making
- Apply relational algebra to the optimization of queries using heuristics
- Utilize data mining techniques to discover knowledge in large data collections
- Apply refactoring techniques to modify and improve database designs
Get College Credit for What You Already Know
The certificates and training listed below are relevant to this degree program. Search our database to view pre-evaluated credentials and see how a license, certification or professional training saves you time and money toward your degree.
Frequently Asked Questions
The M.S. in Computer Science with a focus in Software Systems is a theory-to-practice master’s-level program built on a solid foundation of computer science principles with additional courses in system architecture and artificial intelligence.
With an M.S. in Computer Science with a focus in Software Systems, you’ll gain specialized knowledge and skills within a computer science framework that can prepare you for a variety of positions including software developer,software architect or AI engineer.
With an M.S. in Computer Science with a focus in Software Systems, you’ll gain the skills needed to design the overall architecture for software systems with emphasis on distributed architectures, as well as those necessary to apply artificial intelligence.Building on the core principles of a master’s-level computer science program, the software systems focus enables you to customize your degree to your career aspirations.
Franklin’s M.S. in Computer Science with a focus in Software Systems is a 20-month, 100% online program influenced and designed by leaders in the technology industry. In Franklin’s theory-to-practice program, you’ll be taught by in-field practitioners.
Choose from three start dates each year – fall (August), spring (January) or summer (May).
Franklin University offers a quality education at a competitive cost so you can afford to invest in your future. Our per credit hour tuition rates (vs. per year or per term rates) enable you to get a realistic estimate of exactly how much your degree will cost. Our 2023-2024 tuition rate is $670 per credit hour. Use Franklin’s free MyCost Estimator to get a personalized estimate of your total degree cost. If you have any questions, ask our helpful staff about available financing options and financial aid programs.
Franklin's master's degree programs are specifically designed for busy, working adults -- that means you could finish your M.S. in Computer Science degree with a focus in Software Systems in as few as 20 months.
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