Online M.S. in Computer Science Data Analytics Focus

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Your Best Value M.S. Computer Science

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M.S. in Computer Science - Data Analytics Focus Curriculum

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. Note, this course has proctored exam(s). This exams requires additional technology, if student uses online proctoring.

COMP 655 - Distributed Systems (4)

This course provides a comprehensive understanding of distributed systems, encompassing both fundamental concepts and practical skills for building modern distributed applications. The course will explore the architecture, design goals, and challenges of distributed systems, covering core principles like processes, transparency, communication, consistency, fault tolerance, and security. Throughout the course, students will gain hands-on experience through labs and a team project, where they will design, develop, containerize and deploy a microservice-based cloud native application using industry-standard tools and technologies. Through this course, students will gain in-depth understanding of core concepts of distributed computing, including study of both abstract concepts and practical techniques for building modern distributed applications.

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 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.

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 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.

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.)

CYSC 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. Note, this course has proctored exam(s). This exam requires additional technology, if student uses online proctoring.

CYSC 620 - Software and App Security (4)

Today, software is at the heart of the business processes 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.

CYSC 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 be examined as a way of preserving data integrity.

COMP 680 - Special Topics in Graduate Computer Science (4)

A variable content course in an advanced topic in the field of computer science in which students may pursue current topics or subjects not found in the regular curriculum. A complete description will be published online in the Course Schedule for the trimester the course is offered. This course counts as an elective in the graduate program.

AND

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

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 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.

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 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:

CYSC 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. Note, this course has proctored exam(s). This exam requires additional technology, if student uses online proctoring.

CYSC 620 - Software and App Security (4)

Today, software is at the heart of the business processes 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.

CYSC 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 be 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 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.

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. Note, this course has proctored exam(s).

AND

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.

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

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. Note, this course has proctored exam(s).

AND

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 exponentiation; 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 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.

AND

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.)

AND

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.

M.S. in Computer Science - Data Analytics Focus Program Details

Tuition Costs

2025 - 2026 Tuition	Cost Per Credit
Standard tuition per credit hour	$670
Nursing programs MSN-FNP, MSN-PMHNP, MSN-AGPCNP, Post-Graduate FNP Certificate, Post-Graduate AGPCNP Certificate and Post-Graduate PMHNP Certificate	$670
MSN-Generalist, MSN-Nurse Administrator and MSN-Nurse Educator	$598
RN-MSN Pathway (NURS 500)	$298
Current service members	$536
Master's Prerequisite Courses (500 level)	$398
Master's Prerequisite Courses (500 level) - International Students	$526

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Program Learning Outcomes

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.

Degree Requirements

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:
1. M.S. - Computer Science students must earn at least 20 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.

Admission Requirements

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 the Accrediting Commission for Community and Junior Colleges, Western Association of Schools and Colleges (ACCJC), Higher Learning Commission (HLC), Middle States Commission on Higher Education (MSCHE), New England Commission on Higher Education (NECHE), Northwest Commission on Colleges and Universities (NWCCU), Southern Association of Colleges and School Commission on Colleges (SACSCOC), WASC Senior College and University Commission (WSCUC), or a Council for Higher Education Accreditation (CHEA)-recognized accrediting organization with a GPA of at least a 2.50 on a 4.0 scale.

Applicants who earned at least a 2.25 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 2.25 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 and résumé 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. Students admitted to the programs listed below may take two 12-week classes concurrently and would be required to earn a final grade of “B” in each course. 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. Programs that allow for concurrent enrollment include Business Analytics, Computer Science, Cybersecurity, Health Informatics, Data Analytics, Information Technology, and Information Systems.

English Language Testing & TOEFL IELTS

Prospective graduate students must demonstrate English language proficiency. A student who meets at least one of the following criteria is eligible for admission as a degree-seeking student:

The applicant is a citizen of a country where English is the primary language for education. A list of these countries can be found below.*
The applicant has provided official documentation that meets Franklin graduate admission requirements from an institution in which English is the medium of instruction.
Has submitted an official score report meeting the minimum requirements for admission (see Minimum Scores below) from one of the following exams approved by Franklin University: Accuplacer ESL, Cambridge English Qualifications, Duolingo English Test (DET), International English Language Testing System Academic (IELTS), Pearson Test of English Academic (PTE Academic), Test of English as a Foreign Language (TOEFL). Other officially recognized exams may also be considered. English proficiency scores must be less than two years old.

*For more information about English proficiency, including a list of English-speaking nations, please visit our International Students section.

Program Advisory Board

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

Knowledge & Skillsets

Gain in-demand skills sought by employers with curriculum that teaches you:

Data Visualization & Reporting

Enhance data collection procedures to include information that is relevant for building analytic systems
Clearly and effectively present completed analysis with new insights and recommendations to the business partners
Create interactive dashboards and stories from multiple data sets

Applied Machine Learning

Collaborate with different departments to build complex predictive models to support overall business objectives and business needs
Process, clean, investigate and verify the integrity of data used for analysis
Research and maintain awareness of best practices and techniques of statistical analysis
Select features, build and optimize classifiers using machine learning techniques

Algorithm Analysis

Evaluate algorithm efficiency and determine the most elegant program logic for problems of varying complexity
Establish algorithm requirements in support of software development roadmaps
Assist in the development of advanced algorithms for next-generation functions
Prototype, design, develop, unit test and release software-utilizing algorithms in support of goals, strategies, technologies and concepts

Distributed Systems

Build and maintain high-performance distributed systems to meet organizational needs
Create efficient data structures and analyze distributed algorithms to enable scalable applications
Develop scalable, robust, distributed data architecture to support data analytics in real time
Solve technical challenges and issues around distributed systems
Process large amounts of data and tackle challenging technical problems

Acceptance Testing

Create detailed plans, cases and scripts to test applications
Ensure software meets design specifications and performance standards
Review requirements specifications to ensure testability and compliance with quality control standards

Database Design & Administration

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

Which Data Analytics Program is Best for You?

M.S. in Computer Science Data Analytics Focus

M.S. in Information Technology - Data Analytics Focus

M.S. in Data Analytics

Focus:
Enhance your expertise as you combine the principles of computer science with the elements of data modeling to facilitate knowledge discovery and application.

Skills:
Develop in-demand skills in the areas of database design, data mining, statistical analysis and visual storytelling.

Careers:
Put your M.S. in Computer Science-Data Analytics to work helping organizations leverage data-informed insights for business growth and success.

How many courses are in the program?
Nine 12-week courses

How quickly can I complete the program?
20 months

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Focus:
Grow your skills as a technologist that uses data and statistical reasoning to identify trends, make predictions and inform decision making.

Skills:
Strengthen your understanding and application of statistical inference methodologies, data mining tools and techniques, and visual-based reporting.

Careers:
Use your M.S. in IT-Data Analytics to help organizations understand and apply data to enhance position, demonstrate value, and improve profitability.

How many courses are in the program?
Nine 12-week courses

How quickly can I complete the program?
16 months

Learn More

Focus:
Develop proficiency in using data as a predictive tool for solving business challenges and creating a competitive advantage.

Skills:
Learn to use and apply descriptive and predictive analytics, data visualization and computer algorithms.

Careers:
Apply your M.S. in Data Analytics to help organizations use data to maximize operations, inform decision making and optimize financial performance.

How many courses are in the program?
Eight 12-week courses

How quickly can I complete the program?
19 months

Learn More

Get College Credit for What You Already Know

Frequently Asked Questions

What is a M.S. in Computer Science degree with a focus in Data Analytics?

The M.S. in Computer Science with a focus in Data Analytics is a theory-to-practice master’s-level program built on a solid foundation of computer science principles, including advanced data structure and programming, algorithm analysis and distributed systems, and combined with applied skills in data analytics.

What can you do with a M.S. in Computer Science degree with a focus in Data Analytics?

With an M.S. in Computer Science with a focus in Data Analytics, you’ll gain applied skills in data analytics within a computer science framework that can prepare you for a variety of positions including data engineer, machine learning engineer and data scientist.

Why get a M.S. in Computer Science degree with a focus in Data Analytics?

With an M.S. in Computer Science with a focus in Data Analytics, you’ll be equipped to combine computer science and data modeling to uncover new opportunities for your organization.Building on the core principles of a master’s-level computer science program, the cybersecurity focus enables you to customize your degree to your career aspirations.

What makes Franklin’s M.S. in Computer Science degree with a focus in Data Analytics unique?

Franklin’s M.S. in Computer Science with a focus in Data Analytics 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.

When can I start Franklin’s M.S. in Computer Science degree with a focus in Data Analytics?

Choose from three start dates each year – fall (August), spring (January) or summer (May).

How much will my M.S. in Computer Science degree with a focus in Data Analytics cost?

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 2024-2025 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.

How long does it take to get a M.S. in Computer Science degree with a focus in Data Analytics?

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 Data Analytics in as few as 20 months.

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M.S. in Computer Science - Data Analytics Focus Overview

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Dimitri V.

M.S. Computer Science '22

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