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Masters

Data Science - M.S.

Kent State University
Kent , United States
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Program Details
Degree
Masters
Major
Data Analysis | Data Science | Statistics
Area of study
Information and Communication Technologies | Mathematics and Statistics
Course Language
English
About Program

Program Overview

Data Science - M.S.

Overview

The Data Science M.S. program provides students 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, students will be prepared to solve complex data challenges and make an impact in their field.


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 courses
  • Official transcript(s)
  • GRE scores
  • Two letters of recommendation
  • English language proficiency (for international students)

Program Requirements

  • Major Requirements:
    • CS 63005: Advanced Database Systems Design
    • CS 63015: Data Mining Techniques
    • CS 63016: Big Data Analytics
    • MATH 50015: Applied Statistics
    • MATH 50024: Computational Statistics
    • MATH 50028: Statistical Learning
  • Major Electives (choose from the following):
    • 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
    • MATH 67098: Research
    • ECON 62054: Econometrics I
    • ECON 62055: Econometrics II
    • ECON 62056: Time Series Analysis
    • EHS 62018: Environmental Health Concepts in Public Health
    • EPI 62017: Fundamentals of Public Health Epidemiology
    • EPI 63016: Principles of Epidemiologic 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:
      • Thesis Option: DATA 69199: Thesis I
      • Project and Internship Option: DATA 69099: Capstone Project, DATA 69192: Graduate Internship
      • Project and Course Option: DATA 69099: Capstone Project, Approved Graduate course (50000 level or higher)

Graduation Requirements

  • Minimum Major GPA: 3.000
  • 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.

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.


  • Semester One:
    • CS 63005: Advanced Database Systems Design
    • MATH 50015: Applied Statistics
    • Major Elective
  • Semester Two:
    • CS 63015: Data Mining Techniques
    • MATH 50024: Computational Statistics
    • MATH 50028: Statistical Learning
  • Semester Three:
    • CS 63016: Big Data Analytics
    • Major Elective
    • Culminating Requirement
  • Semester Four:
    • Culminating Requirement

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.


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