About This Program

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.Read more...

Contact Information

• Program Coordinator:Hassan Peyravi|gradinfo@cs.kent.edu| 330-672-9047
• Connect with an Admissions Counselor:U.S. Student|International Student

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 thegraduate admission website. For more information on international admissions, visit theinternational 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)
• Effective for fall 2024 admission term, GRE scores will be required
• Two letters of recommendation
• English language proficiency - all international students must provide proof of English language proficiency (unless they meet specific exceptions) by earning one of the following:
  • Minimum 525 TOEFL PBT score
  • Minimum 71 TOEFL IBT score
  • Minimum 74 MELAB score
  • Minimum 6.0 IELTS score
  • Minimum 50 PTE score
  • Minimum 100 Duolingo English score

¹

Students entering the program are expected to have previously completed courses in linear algebra (equivalent toMATH 21001orMATH 21002), statistics (equivalent toMATH 20011), advanced calculus (equivalent toMATH 22005), discrete mathematics/structures (equivalent toMATH 31011orCS 23022), programming and data structures (equivalent toCS 23001) and database systems (equivalent toCS 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.

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.

Program Requirements

Major Requirements

Course List

Code	Title	Credit Hours
Major Requirements
CS 63005	ADVANCED DATABASE SYSTEMS DESIGN	3
CS 63015	DATA MINING TECHNIQUES	3
CS 63016	BIG DATA ANALYTICS	3
MATH 50015	APPLIED STATISTICS	3
MATH 50024	COMPUTATIONAL STATISTICS	3
MATH 50028	STATISTICAL 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
orMATH 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

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.

集成学习的经验可能包括一个年代ubstantial 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 major. However, courses designated as critical (!) must be completed in the semester listed to ensure a timely graduation.

Plan of Study Grid

Semester One		Credits
CS 63005	ADVANCED DATABASE SYSTEMS DESIGN	3
MATH 50015	APPLIED STATISTICS	3
Major Elective		3
	Credit Hours	9
Semester Two
CS 63015	DATA MINING TECHNIQUES	3
MATH 50024	COMPUTATIONAL STATISTICS	3
MATH 50028	STATISTICAL LEARNING	3
	Credit Hours	9
Semester Three
CS 63016	BIG DATA ANALYTICS	3
Major Elective		3
	Credit Hours	6
Semester Four
Culminating Requirement		6
	Credit Hours	6
	Minimum Total Credit Hours:	30

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. 可以使用数据如何科学提出建议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.