
Data Science - M.S.
Kent, USA
DURATION
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PACE
Full time
APPLICATION DEADLINE
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TUITION FEES
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STUDY FORMAT
On-Campus
Scholarships
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Introduction
Data Science - M.S.
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.
Admissions
Scholarships and Funding
Curriculum
Major requirements
- Advanced Database Systems Design
- Data Mining Techniques
- Big Data Analytics
- Applied Statistics
- Computational Statistics
- Statistical Learning
Culminating Experience Requirement, choose from the following:
- Capstone Project
- Capstone Project and Graduate Internship
- Thesis I
- Thesis I
Major Electives, choose from the following:
- Biological Statistics
- Artificial Intelligence
- Data Security and Privacy
- Big Data Management
- Probabilistic Data Management
- Computational Health Informatics
- Advanced Artificial Intelligence
- Multimedia Systems and Biometrics
- Information Visualization
- Research or
- Research
- Econometrics I
- Econometrics Ii
- 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
- Clinical Analytics
- Human Factors and Usability in Health Informatics
- Clinical Analytics II
- Foundational Principles of Knowledge Management
- The Information Landscape
- Information Organization
- Probability Theory and Applications
- Topics in Probability Theory and Stochastic Processes
- Stochastic Actuarial Models
- Quantitative Statistical Analysis I
- Quantitative Statistical Analysis II
Program Outcome
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.
Program Tuition Fee
English Language Requirements
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