Data Science - M.S.
Department of Mathematics
233 Mathematics and Computer Science Building
Department of Computer Science
241 Mathematics and Computer Science Building
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 an emerging 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.
Fully Offered At:
- Kent Campus
- Bachelor’s degree from an accredited college or university for unconditional admission
- Minimum 3.000 undergraduate GPA (on a 4.000 point scale) for unconditional admission
- Prerequisite mathematics and computer science courses1
- Official transcript(s)
- 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 (paper-based version)
- Minimum 71 TOEFL IBT score (Internet-based version)
- Minimum 74 MELAB score
- Minimum 6.0 IELTS score
- Minimum 50 PTE score
- Minimum 100 Duolingo English Test score
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.
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.
|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|
|Culminating Requirement, choose from the following:||6|
|CAPSTONE PROJECT |
and GRADUATE INTERNSHIP
|CAPSTONE PROJECT |
and GRADUATE INTERNSHIP
|Major Electives, choose from the following:||6|
|DATA SECURITY AND PRIVACY|
|BIG DATA MANAGEMENT|
|PROBABILISTIC DATA MANAGEMENT|
|COMPUTATIONAL HEALTH INFORMATICS|
|ADVANCED ARTIFICIAL INTELLIGENCE|
|MULTIMEDIA SYSTEMS AND BIOMETRICS|
or MATH 67098
|TIME SERIES ANALYSIS|
|ENVIRONMENTAL HEALTH CONCEPTS IN PUBLIC HEALTH|
|FUNDAMENTALS OF PUBLIC HEALTH EPIDEMIOLOGY|
|PRINCIPLES OF EPIDEMIOLOGIC RESEARCH|
|OBSERVATIONAL DESIGNS FOR CLINICAL RESEARCH|
|EXPERIMENTAL DESIGNS FOR CLINICAL RESEARCH|
|GEOGRAPHIC INFORMATION SCIENCE|
|ADVANCED GEOGRAPHIC INFORMATION SCIENCE|
|HEALTH INFORMATICS MANAGEMENT|
|HUMAN FACTORS AND USABILITY IN HEALTH INFORMATICS|
|CLINICAL ANALYTICS II|
|FOUNDATIONAL PRINCIPLES OF KNOWLEDGE MANAGEMENT|
|THE INFORMATION LANDSCAPE|
|PROBABILITY THEORY AND APPLICATIONS|
|TOPICS IN PROBABILITY THEORY AND STOCHASTIC PROCESSES|
|STOCHASTIC ACTUARIAL MODELS|
|QUANTITATIVE STATISTICAL ANALYSIS I|
|QUANTITATIVE STATISTICAL ANALYSIS II|
|Minimum Total Credit Hours:||30|
The culminating experiences may be 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.
The integrated learning experience may include a substantial capstone project or a capstone project and internship. For either non-thesis option, 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.