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About Start Dates
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Jan 4, 2027Summer 2027
May 17, 2027Start 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.
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Tailor your M.S. in computer science with a focus in data analytics
Data in its raw form is an asset, but with the help of skilled data professionals, it’s a powerful tool that fuels strategic decision making. With Franklin’s 100% online 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. By combining applied data analytics skills with the core principles of Franklin’s industry-aligned master’s-level computer science program, the data analytics focus prepares you to excel in specialized roles.
Finish Fast
Finish your master's in as few as 20 months.
Leading Architectural Tools
Get hands-on experience with R, Tableau and Python.
Customizable Program
Tailor your master's degree program to your interests.
Real-World Practitioners
Learn from experienced technology leaders.
100% Online Classes
Take classes that fit with your busy life.
Game-Changing Skills
Play an important role in communicating emerging technologies to stakeholders.
M.S. in Computer Science - Data Analytics Focus Overview
Boost your knowledge of machine learning techniques
As part of your M.S. in Computer Science-Data Analytics, you’ll earn a Data & Machine Learning Engineering digital badge that demonstrates your understanding of machine learning techniques like linear and logistic regression, probabilistic inference and Support Vector Machines. You’ll also have foundational knowledge in algorithm analysis, data modeling, database design, implementation, optimization and queries. You’ll learn techniques to collect, prepare and analyze data to create visualizations, dashboards and stories to communicate business insights.
Get hands-on experience with industry-standard data software
In an evolving field like data analytics, relevant skills matter more than ever. You’ll get an overview of current data analytics methods, concepts and current practices. You’ll be able to employ data mining principles to identify patterns in data. You’ll learn how to apply inferential statistical analysis methods, including t-tests and ANOVA to make decisions. Assignments will provide opportunities to use R, Tableau, Python, SAS or SPSS to conduct analysis and interpret results.
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.
Affordable Tuition
Affordable tuition rates for the M.S. in Computer Science provide value and quality.
Finish Fast
Realize your career goals sooner and reap the benefits.
Non-Profit = Student Focused
Unlike for-profit universities, Franklin invests in student success, not shareholder gain.
Partner? Pay Less.
Search below to see if you could save tuition through an employer or professional organization partnership.
(After Partner Discount)
Tuition Guarantee
Inflation-proof your degree cost by locking-in your tuition rate from day one through graduation.
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 (Spring 2025)
M.S. in Computer Science - Data Analytics 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 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.
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:
AND
Students may complete a focus area to fulfill the Major Elective requirement.
Students may complete a focus area to fulfill the Major Elective requirement.
OR
Data Analytics:
OR
Cybersecurity:
OR
Software Systems:
AND
AND
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.
Free Master's Toolkit
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M.S. in Computer Science - Data Analytics Focus Program Details
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 |
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 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.
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.
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."
Knowledge & Skillsets
Gain in-demand skills sought by employers with curriculum that teaches you:
- 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
- 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
- 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
- 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
- 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?
Find the Data Analytics Program That Fits Your Goals
If you’re interested in advancing your technology career, Franklin has several great options. Compare programs and identify your perfect match.
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
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
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
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 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.
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.
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.
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.
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 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.
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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