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.
Data Science - M.S.
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- Hassan Peyravi | gradinfo@cs.kent.edu | 330-672-9047
- Connect with an Admissions Counselor: U.S. Student | International Student
Master鈥檚 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.
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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.
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Admissions
for Data Science - M.S.
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.
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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.
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Learning Outcomes
Program Learning Outcomes
Graduates of this program will be able to:
- Ask the questions so that problems in a particular business or industrial situation become clear.
- Determine if the problem may be addressed with data science methods and tools, and if yes, propose potential methods for solving the problems.
- 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.
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Coursework
On This Page
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 60104BIOLOGICAL STATISTICS CS 54201ARTIFICIAL INTELLIGENCE CS 57206DATA SECURITY AND PRIVACY CS 63017BIG DATA MANAGEMENT CS 63018PROBABILISTIC DATA MANAGEMENT CS 63100COMPUTATIONAL HEALTH INFORMATICS CS 64201ADVANCED ARTIFICIAL INTELLIGENCE CS 64402MULTIMEDIA SYSTEMS AND BIOMETRICS CS 67302INFORMATION VISUALIZATION CS 69098RESEARCH or MATH 67098RESEARCH ECON 62054ECONOMETRICS I ECON 62055ECONOMETRICS II ECON 62056TIME SERIES ANALYSIS EHS 52018ENVIRONMENTAL HEALTH CONCEPTS IN PUBLIC HEALTH EPI 52017FUNDAMENTALS OF PUBLIC HEALTH EPIDEMIOLOGY EPI 63016PRINCIPLES OF EPIDEMIOLOGIC RESEARCH EPI 63018OBSERVATIONAL DESIGNS FOR CLINICAL RESEARCH EPI 63019EXPERIMENTAL DESIGNS FOR CLINICAL RESEARCH GEOG 59070GEOGRAPHIC INFORMATION SCIENCE GEOG 59080ADVANCED GEOGRAPHIC INFORMATION SCIENCE HI 60401HEALTH INFORMATICS MANAGEMENT HI 60411CLINICAL ANALYTICS HI 60414HUMAN FACTORS AND USABILITY IN HEALTH INFORMATICS HI 60418CLINICAL ANALYTICS II KM 60301FOUNDATIONAL PRINCIPLES OF KNOWLEDGE MANAGEMENT LIS 60020INFORMATION ORGANIZATION MATH 50011PROBABILITY THEORY AND APPLICATIONS MATH 50051TOPICS IN PROBABILITY THEORY AND STOCHASTIC PROCESSES MATH 50059STOCHASTIC ACTUARIAL MODELS PSYC 61651QUANTITATIVE STATISTICAL ANALYSIS I PSYC 61654QUANTITATIVE STATISTICAL ANALYSIS II Culminating Requirement Choose from the following: 6 DATA 69099CAPSTONE PROJECT DATA 69099
& DATA 69192CAPSTONE PROJECT
and GRADUATE INTERNSHIPDATA 69199THESIS I Minimum Total Credit Hours: 30 Graduation Requirements
Graduation Requirements Summary 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.
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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 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 Delivery
- Delivery:
- In person
- Location:
- 天天吃瓜 Campus
- Delivery:
Examples of Possible Careers and Salaries for Data Science - M.S.
Graduates of 天天吃瓜'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.
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Data scientists and mathematical science occupations, all other
30.9%
much faster than the average
33,200
number of jobs
$98,230
potential earnings
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Computer and information research scientists
15.4%
much faster than the average
32,700
number of jobs
$126,830
potential earnings
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Statisticians
34.6%
much faster than the average
42,700
number of jobs
$92,270
potential earnings
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Computer and information systems managers
10.4%
much faster than the average
461,000
number of jobs
$151,150
potential earnings
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Management analysts
10.7%
much faster than the average
876,300
number of jobs
$87,660
potential earnings
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Database administrators and architects
9.7%
much faster than the average
132,500
number of jobs
$98,860
potential earnings
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Computer programmers
-9.4%
decline
213,900
number of jobs
$89,190
potential earnings
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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
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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.