Image
Data Science M.S.

Data Science - M.S.

The Data Science M.S. program provides you with the theoretical knowledge and practical experience needed to succeed in today's data-driven world. With hands-on learning opportunities, experienced faculty and cutting-edge technology, you'll be prepared to solve complex data challenges and make an impact in your field.

Contact Us

Apply Now
Request Information
Schedule a visit

Master’s Degree in Data Science

The Master's of Science degree in Data Science program is a DHS STEM Designated Degree Program. It situates computing-specific competencies in computer science and statistical-related fields including database, data mining, machine learning, and big data within the broader interdisciplinary space.

Program Information for Data Science - M.S.

Program Description

Program Description

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.

Admissions for Data Science - M.S.

Admissions

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 wholistic 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

Applications submitted after these deadlines will be considered on a space-available basis.

Learning Outcomes

Learning Outcomes

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.

Coursework

On This Page

Program Requirements

Major Requirements

Major Requirements
CS 63005ADVANCED DATABASE SYSTEMS DESIGN 3
CS 63015DATA MINING TECHNIQUES 3
CS 63016BIG DATA ANALYTICS 3
MATH 50015APPLIED STATISTICS 3
MATH 50024COMPUTATIONAL STATISTICS 3
MATH 50028STATISTICAL LEARNING 3
Major Electives, choose from the following:6
BSCI 60104
BIOLOGICAL STATISTICS
CS 54201
ARTIFICIAL INTELLIGENCE
CS 57206
DATA SECURITY AND PRIVACY
CS 63017
BIG DATA MANAGEMENT
CS 63018
PROBABILISTIC DATA MANAGEMENT
CS 63100
COMPUTATIONAL HEALTH INFORMATICS
CS 64201
ADVANCED ARTIFICIAL INTELLIGENCE
CS 64402
MULTIMEDIA SYSTEMS AND BIOMETRICS
CS 67302
INFORMATION VISUALIZATION
CS 69098
RESEARCH
or MATH 67098
 RESEARCH
ECON 62054
ECONOMETRICS I
ECON 62055
ECONOMETRICS II
ECON 62056
TIME SERIES ANALYSIS
EHS 52018
ENVIRONMENTAL HEALTH CONCEPTS IN PUBLIC HEALTH
EPI 52017
FUNDAMENTALS OF PUBLIC HEALTH EPIDEMIOLOGY
EPI 63016
PRINCIPLES OF EPIDEMIOLOGIC RESEARCH
EPI 63018
OBSERVATIONAL DESIGNS FOR CLINICAL RESEARCH
EPI 63019
EXPERIMENTAL DESIGNS FOR CLINICAL RESEARCH
GEOG 59070
GEOGRAPHIC INFORMATION SCIENCE
GEOG 59080
ADVANCED GEOGRAPHIC INFORMATION SCIENCE
HI 60401
HEALTH INFORMATICS MANAGEMENT
HI 60411
CLINICAL ANALYTICS
HI 60414
HUMAN FACTORS AND USABILITY IN HEALTH INFORMATICS
HI 60418
CLINICAL ANALYTICS II
KM 60301
FOUNDATIONAL PRINCIPLES OF KNOWLEDGE MANAGEMENT
LIS 60020
INFORMATION ORGANIZATION
MATH 50011
PROBABILITY THEORY AND APPLICATIONS
MATH 50051
TOPICS IN PROBABILITY THEORY AND STOCHASTIC PROCESSES
MATH 50059
STOCHASTIC ACTUARIAL MODELS
PSYC 61651
QUANTITATIVE STATISTICAL ANALYSIS I
PSYC 61654
QUANTITATIVE STATISTICAL ANALYSIS II
Culminating Requirement
Choose from the following:6
DATA 69099
CAPSTONE PROJECT
DATA 69099
& DATA 69192
CAPSTONE PROJECT
and GRADUATE INTERNSHIP
DATA 69199
THESIS I
Minimum Total Credit Hours:30

Graduation Requirements
Minimum Major GPA Minimum Overall GPA
- 3.000
  • 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

Roadmap

Roadmap

This roadmap is a recommended semester-by-semester plan of study for this major. However, courses designated as critical (!) must be completed in the semester listed to ensure a timely graduation.

Plan of Study Grid
Semester OneCredits
CS 63005 ADVANCED DATABASE SYSTEMS DESIGN 3
MATH 50015 APPLIED STATISTICS 3
Major Elective 3
 Credit Hours9
Semester Two
CS 63015 DATA MINING TECHNIQUES 3
MATH 50024 COMPUTATIONAL STATISTICS 3
MATH 50028 STATISTICAL LEARNING 3
 Credit Hours9
Semester Three
CS 63016 BIG DATA ANALYTICS 3
Major Elective 3
 Credit Hours6
Semester Four
Culminating Requirement 6
 Credit Hours6
 Minimum Total Credit Hours:30

Program Delivery

Program Delivery

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

Examples of Possible Careers and Salaries for Data Science - M.S.

Graduates of Kent State University's Master of Science in Data Science can pursue careers as data scientists, computer programmers, and business analysts. They are prepared to work in diverse industries, including technology, healthcare, finance, and marketing, where they can analyze complex data sets, build predictive models, and drive data-driven decision-making to solve real-world problems.

Data scientists and mathematical science occupations, all other

30.9%

much faster than the average

33,200

number of jobs

$98,230

potential earnings

Computer and information research scientists

15.4%

much faster than the average

32,700

number of jobs

$126,830

potential earnings

Statisticians

34.6%

much faster than the average

42,700

number of jobs

$92,270

potential earnings

Computer and information systems managers

10.4%

much faster than the average

461,000

number of jobs

$151,150

potential earnings

Management analysts

10.7%

much faster than the average

876,300

number of jobs

$87,660

potential earnings

Database administrators and architects

9.7%

much faster than the average

132,500

number of jobs

$98,860

potential earnings

Computer programmers

-9.4%

decline

213,900

number of jobs

$89,190

potential earnings

Software developers and software quality assurance analysts and testers

21.5%

much faster than the average

1,469,200

number of jobs

$110,140

potential earnings

Notice: Career Information Source
* 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.
© 2025 Kent State University All rights reserved.