College of Sciences and Humanities
Department of Mathematical Sciences
Department of Computer Science
www.kent.edu/math

About This Program

Step into the forefront of innovation with Kent State's M.S. degree in Data Science, where you will gain the theoretical knowledge and hands-on experience needed to thrive in today’s data-driven world. Through an interdisciplinary curriculum blending computer science, statistics and advanced analytics, you will develop the skills to uncover insights, solve complex problems and make a meaningful impact across industries. Read more...

Contact Information

• Hassan Peyravi | gradinfo@cs.kent.edu | 330-672-9047

• Connect with an Admissions Counselor

Program Delivery

• Delivery:
  • In person
• Location:
  • Kent Campus

Examples of Possible Careers and Salaries*

^{* Source of occupation titles and labor data comes from the U.S. Bureau of Labor Statistics' Occupational Outlook Handbook. Data comprises projected percent change in employment over the next 10 years; nation-wide employment numbers; and the yearly median wage at which half of the workers in the occupation earned more than that amount and half earned less.}

For more information about graduate admissions, visit the graduate admission website. For more information on international admissions, visit the international admission website.

Admission Requirements

• Bachelor’s degree from an accredited college or university
• Minimum 3.000 undergraduate GPA on a 4.000-point scale
• Prerequisite mathematics and computer science courses¹
• Official transcript(s)
• GRE scores
• Two letters of recommendation
• English language proficiency - all international students must provide proof of English language proficiency (unless they meet specific exceptions to waive) by earning one of the following:²
  • Minimum 71 TOEFL iBT score
  • Minimum 6.0 IELTS score
  • Minimum 50 PTE score
  • Minimum 100 DET score

¹

Students entering the program are expected to have previously completed courses in linear algebra (equivalent to MATH 21001 or MATH 21002), statistics (equivalent to MATH 20011), advanced calculus (equivalent to MATH 22005), discrete mathematics/structures (equivalent to MATH 31011 or CS 23022), programming and data structures (equivalent to CS 23001) and database systems (equivalent to CS 33007). Applicants have not completed all the prerequisite courses may be admitted conditionally (based on a holistic review of their application) until they complete the remaining courses being before beginning the program’s coursework.

²

International applicants who do not meet the above test scores may be considered for conditional admission.

Application Deadlines

• Fall Semester
  • Application deadline: June 15
• Spring Semester
  • Application deadline: November 1
• Summer Term
  • Application deadline: April 1

All application materials (including applicable fee, transcripts, recommendation letters, etc.) submitted after these deadlines will be considered on a space-available basis.

Program Requirements

Major Requirements

¹

With graduate coordinator approval, students may substitute a relevant graduate-level course (50000 or 60000 level) in either Computer Science (CS) or Mathematics (MATH).

²

If thesis is selected, students must continually register for DATA 69199 for maximum 6 credit hours toward the degree. Students may need to register for DATA 69299 to complete the thesis requirement; however, those credit hours do not, whatsoever, count toward the degree.

Graduation Requirements

• No more than one-half of a graduate student’s coursework may be taken in 50000-level courses.
• Grades below C are not counted toward completion of requirements for the degree.

Culminating Experience

The culminating experience requirement is a master’s thesis or an integrated learning experience.

The master’s thesis requires a written thesis, a public defense of the thesis and approval by the student’s supervisory committee. Students must form a master's thesis committee, which will include the advisor and at least two other graduate faculty members. The thesis topic and committee must be approved by the advisor and graduate coordinator. The final version of the thesis must be approved by the advisor, thesis committee and graduate coordinator.

The integrated learning experience may include a substantial capstone project or a capstone project and internship. Students must prepare a written document explaining and/or demonstrating their capstone project or internship activity and its significance. In addition, students must give a public presentation of their capstone project or internship, and the written document and presentation must be approved by their supervisory committee.

Roadmap

This roadmap is a recommended semester-by-semester plan of study for this program. Students will work with their advisor to develop a sequence based on their academic goals and history. Courses designated as critical (!) must be completed in the semester listed to ensure a timely graduation.

Program Learning Outcomes

Graduates of this program will be able to:

1. Ask the questions so that problems in a particular business or industrial situation become clear.
2. Determine if the problem may be addressed with data science methods and tools and, if yes, propose potential methods for solving the problems.
3. Make suggestions for how data science may be used to enhance the quality and value of currently existing products (whether the products are physical or methods) and how data science may be used in the development of new products.

Full Description

The Master of Science degree in Data Science provides a focus on developing scientists who will understand the theories, methods and tools of data science and apply data science to solving research and workplace questions in the natural, health and social sciences for businesses and industries.

Data science is a STEM discipline founded on the principles of mathematics and the sciences and developed through a synthesis of mathematics and computer science. One may think of data science as a blending together of methods and ideas from analysis, statistics, databases, big data, artificial intelligence, numerical analysis, graph theory and visualization for the purposes of finding information in data and applying that information to solving real-world problems.