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Dissertation

A Dissertation describes original research performed by the student. The Dissertation topic must be approved by the advisor and Graduate Coordinator. A Dissertation committee, made up of graduate faculty, must be formed to assess the quality and value of the work. A public Dissertation defense is made by the student. The final Dissertation and defense must be approved by the advisor and Dissertation committee.

Candidacy Examination

Students who have passed the Preliminary Examination at the Ph.D. level  are expected to continue to broaden their general computer science background and to take courses in their areas of special interest. Before starting substantial work on a dissertation, the student is required to take the Candidacy Examination.

Preliminary Examination

The Preliminary Examination is intended to assess a student’s understanding of the basic prerequisite concepts for entrance into the Doctoral program in Computer Science. It also insures that all incoming students have the ability to effectively reason with and integrate the underlying knowledge and concepts in the broad field of Computer Science. This ability is necessary to continue the student’s studies in the Doctoral program.

Computer Science - Ph.D.

The Ph.D. degree in Computer Science provides students with an educational and research environment that fosters personal and intellectual growth, flourishes academic goals and develops career paths through necessary training with emerging technologies. The program promotes research, discovery and integration, and is designed for students interested in becoming professional scholars, college and university professors or researchers in private, industrial or government research institutions.

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Program Information for Computer Science - Ph.D.

Learning Outcomes

Learning Outcomes

Program Learning Outcomes

Graduates of this program will be able to:

  1. Have all around breadth-of-knowledge and understanding of essential facts, concepts, principles and theories relating to advanced topics in computer science to be regarded as a scholar of computer science.
  2. Demonstrate depth of knowledge at least in one specialized topic.
  3. Conduct independent research by advancing the body of knowledge in the area through the doctoral dissertation research.
  4. Clearly articulate advanced research problems and their solutions.
  5. Present general computer science topics in a learning environment.
  6. Develop and write publishable papers that clearly articulate advanced research problems and their solutions.
  7. Demonstrate integrative and deep knowledge of essential literature, facts, concepts, principles and theories relating to a chosen area of research.
  8. Perform complete and thorough literature searches.
  9. Evaluate, comprehensively and critically, the extent to which a particular work relates to and/or contributes to a given field.
  10. Publish and participate in a chosen research community.

Admissions for Computer Science - Ph.D.

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 or higher in computer science (or closely related field) from an accredited college or university1
  • Minimum 3.000 GPA on a 4.000-point scale
  • Official transcript(s)
  • GRE scores
  • Résumé
  • Goal statement
  • Three 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 whose records clearly indicate a potential to do doctoral-level work in computer science may be directly admitted and must fulfill the requirements of both the master's and doctorate degrees.

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.

Coursework

On This Page

Program Requirements

Major Requirements

Major Requirements
Computer Science Electives (CS 70000 level) 127-57
CS 89191DOCTORAL SEMINAR 23
Culminating Requirement
CS 89199DISSERTATION I 330
Minimum Total Credit Hours for Post-Baccalaureate Students90
Minimum Total Credit Hours for Post-Master's Students60
1

Maximum 9 credit hours of CS 89098 or CS 89991 may be applied towards the degree.

2

Students must make a public presentation of project and/or research work (excluding dissertation defense and candidacy examination) at least two times before graduation. The presentation must take place in the doctoral seminar at least one full term before graduation and not more than two years after entering the program. The doctoral seminar is offered for 1 or 2 credit hours; therefore, the student must enroll in this course at least two times. This course can be taken multiple times but only 3 credit hours count toward the degree.

3

Each doctoral candidate, upon admission to candidacy, must register for CS 89199 for a total of 30 credit hours. It is expected that a doctoral candidate will continuously register for Dissertation I, and thereafter CS 89299, each semester, until all requirements for the degree have been met. A dissertation describes original research performed by the student. The dissertation topic must be approved by the advisor and graduate coordinator. A dissertation committee, made up of graduate faculty, must be formed to assess the quality and value of the work. A public dissertation defense is made by the student. The final dissertation and defense must be approved by the advisor and dissertation committee.

Graduation Requirements
Minimum Major GPA Minimum Overall GPA
- 3.000

Proficiency Requirements and Candidacy

Students must clear core area proficiency requirements. Students admitted after completing a master's of computer science degree must take the Prelims examination within the first two semesters. Other students, including directly-admitted Ph.D. students, must take the Prelims examination within the first three semesters

The candidacy examination is a comprehensive examination in the field of the major subject. The format of the candidacy examination will be determined by the student’s Candidacy Examination Committee, which is composed of the student’s advisor and two other graduate faculty members. The Candidacy Examination Committee must be approved by the graduate coordinator. The student must complete the candidacy examination at least one year before the dissertation defense.

Program Delivery

Program Delivery

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

°µÍø½ûÇø Board of Trustees Approves Significant Tuition Reduction for In-State Podiatric Medicine Students

Students studying at °µÍø½ûÇø's College of Podiatric Medicine review foot X-rays.

The °µÍø½ûÇø Board of Trustees approved a revised tuition rate for students enrolled at the university’s College of Podiatric Medicine at the Board’s regular quarterly meeting held Wednesday, Sept. 20, in Rockwell Hall on the Kent Campus. The Board approved reducing tuition for the college by nearly $14,000 for Ohio resident students – a decrease of more than 30% – from the current tuition rate. The new tuition rate is effective for the 2024 Spring Semester. Yearly tuition for in-state podiatric medicine students will drop to $32,095 from $45,961. This tuition reduction makes Kent...

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 will be prepared to solve complex data challenges and make an impact in your field.

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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 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.

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: 16
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 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:6
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)
Minimum Total Credit Hours:30
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).

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 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.

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
Culminating Requirement 3
 Credit Hours9
Semester Four
Culminating Requirement 3
 Credit Hours3
 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.

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' . 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.

Artificial Intelligence - M.S.

The Master of Science in Artificial Intelligence program provides rigorous training in the theory and application of AI, equipping you with the skills to develop intelligent systems that can solve complex problems in a variety of fields. With access to state-of-the-art technology and experienced faculty, you will gain the knowledge and practical experience needed to make an impact in this rapidly growing field.

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Program Information for Artificial Intelligence - M.S.

Program Description

Program Description

Full Description

The Master of Science degree in Artificial Intelligence prepares students with a focused educational and research environment to develop career paths through necessary learning and training with emerging artificial intelligence technologies and applications to intelligent analytics, smart homes and communities and robotics and automation. Graduates have technical knowledge and research and development skills necessary for applying artificial intelligence to industry, community and military. These areas include sectors requiring intelligent pattern-analysis of big data such as retail, healthcare, biology, psychology and intelligent human-machine interactions and interfaces.

Admissions for Artificial Intelligence - 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
  • Course Proficiency: Completion of high-level algebra, geometry and calculus courses (equivalent to MATH 12002, MATH 12003, MATH 21001)1(starting with spring 2026 admissions, a minimum B grade in these courses will be required)
  • Official transcript(s)
  • GRE scores
  • Résumé
  • Goal statement
  • 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

Admission to this interdisciplinary program is holistic. Highly qualified students from related disciplines who are lacking preparation in some standard areas may be considered for admission on a case-by-case basis.

1

It is strongly recommended that applicants to the program have completed computer sciences courses with a minimum B grade in such areas as computer programming, discrete structures, data structures and abstraction, operating systems, database and computer algorithms (equivalent to CS 13011, CS 13012, CS 23001, CS 23022, CS 33007, CS 33211, CS 46101).

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.

Learning Outcomes

Learning Outcomes

Program Learning Outcomes

Graduates of this program will be able to:

  1. Combine intelligent analytics and automation, human-computer interaction and robotics techniques to optimize and automate transportation, industrial processes and/or healthcare processes.
  2. Apply machine learning techniques on big data to predict, classify, data mine and explore patterns.
  3. Apply intelligent visualization and Internet-based techniques for smart homes and communities.
  4. Perform research, discovery and integration by applying knowledge of artificial intelligence theory and techniques.

Coursework

On This Page

Program Requirements

Major Requirements

Major Requirements
CS 53302ALGORITHMIC ROBOTICS 3
or CS 63018 PROBABILISTIC DATA MANAGEMENT
or CS 67302 INFORMATION VISUALIZATION
CS 54201ARTIFICIAL INTELLIGENCE 3
CS 54202MACHINE LEARNING AND DEEP LEARNING 3
CS 63005ADVANCED DATABASE SYSTEMS DESIGN 3
CS 64201ADVANCED ARTIFICIAL INTELLIGENCE 3
Major Electives, choose from the following:9
CS 53301
SOFTWARE DEVELOPMENT FOR ROBOTICS
CS 53302
ALGORITHMIC ROBOTICS
CS 53303
INTERNET OF THINGS
CS 53305
ADVANCED DIGITAL DESIGN
CS 53334
HUMAN-ROBOT INTERACTION
CS 63015
DATA MINING TECHNIQUES
CS 63016
BIG DATA ANALYTICS
CS 63017
BIG DATA MANAGEMENT
CS 63018
PROBABILISTIC DATA MANAGEMENT
CS 63100
COMPUTATIONAL HEALTH INFORMATICS
CS 63306
EMBEDDED COMPUTING
CS 64401
IMAGE PROCESSING
CS 64402
MULTIMEDIA SYSTEMS AND BIOMETRICS
CS 65203
WIRELESS AND MOBILE COMMUNICATION NETWORKS
CS 67301
SCIENTIFIC VISUALIZATION
CS 67302
INFORMATION VISUALIZATION
CS 69995
SPECIAL TOPICS IN COMPUTER SCIENCE (requires advisor approval to apply)
Culminating Requirement
Choose from the following:6
Thesis Option
CS 69199
THESIS I 1
Project Option
CS 69099
CAPSTONE PROJECT
Project Option Electives, choose from the following:
CS 69192
GRADUATE INTERNSHIP
CS 69995
SPECIAL TOPICS IN COMPUTER SCIENCE
Minimum Total Credit Hours:30
1

Students selecting the thesis option 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.

Progression Requirements
  • Students should complete a minimum of two required courses and either CS 53302, CS 63018 or CS 67302 before taking elective courses.
  • Students must maintain a minimum 3.000 GPA. Students earning less than a 3.000 GPA or earning a C grade or lower in two courses will be placed on academic probation.

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.
Program Delivery

Program Delivery

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

Examples of Possible Careers and Salaries for Artificial Intelligence - M.S.

Computer and information research scientists

15.4%

much faster than the average

32,700

number of jobs

$126,830

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

Data scientists and mathematical science occupations, all other

30.9%

much faster than the average

33,200

number of jobs

$98,230

potential earnings

Notice: Career Information Source
* Source of occupation titles and labor data comes from the U.S. Bureau of Labor Statistics' . 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.
Tuition to Drop by 30% for °µÍø½ûÇø Podiatric Medicine Students From Ohio

°µÍø½ûÇø College of Podiatric Medicine students in the Class of 2027 read the physician’s oath during the traditional white coat ceremony that signifies the start of podiatric medical school.

Thanks to a nearly eight-year effort of various °µÍø½ûÇø administrators, Ohio students enrolled in the university’s College of Podiatric Medicine will see a significant reduction in tuition on their next semester’s bill and will graduate with less debt. At its Sept. 20 meeting, the °µÍø½ûÇø Board of Trustees approved a revised tuition rate that reduces tuition by nearly $14,000 for Ohio resident students – a decrease of more than 30% – from the current tuition rate effective for the 2024 Spring Semester. Yearly tuition for in-state podiatric medicine students will drop to $32...

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