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Nov 21, 2024
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2023-2024 Graduate Catalog [ARCHIVED CATALOG]
Data Science, PhD
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Return to: Graduate Programs
Students completing the Ph.D. in Data Science will demonstrate competency in the core concepts and techniques of data science, which come from both computer science and statistics. At BGSU, students will develop appropriate techniques to analyze structured, unstructured, or dynamic datasets, understand the principles of analytical methods, and articulate the strengths and limitations of analytical methods. Students will learn the skills needed to communicate effectively with technical and non-technical audiences. The program is designed to identify and respond to ethical concerns with the provenance and use of data while developing new techniques for the analysis of complex datasets.
Learning Outcomes
Upon completion of the doctoral degree, students in the Data Science program are expected to be able to:
- Demonstrate competency in the core concepts and techniques of data science, which come from both computer science and statistics.
- Demonstrate the ability to use or develop appropriate techniques to analyze structured, unstructured, or dynamic datasets.
- Demonstrate an understanding of the principles that underlie analytical methods, to articulate the strengths and limitations of analytical methods, and to defend choices to use some methods over others.
- Demonstrate the ability communicate effectively to technical and non-technical audiences orally, in writing, and with effective visualization.
- Demonstrate the ability to identify and respond to ethical concerns with the provenance and use of data.
- Demonstrate the ability to develop new techniques for the analysis of complex datasets or real-time modeling and decision-making, or extend existing techniques to novel and challenging datasets.
- Demonstrate the ability to organize data using tools appropriate to the problem, code new techniques in the appropriate computer language, optimize for performance and scalability, and distribute new tools to the data science community in a usable form.
Admission Requirements
Master’s degree in Science in Mathematics, Statistics, Computer Science, Data Science, or a closely related field is preferred. Those admitted without sufficient background may be asked to complete the MS in Data Science program first.
Additional Documents required:
- Submit scores from the Graduate Record Examination (GRE) General Test or the Graduate Management Admission Test (GMAT)
- Three Letters of Recommendation from faculty or professionals who know you well
- Resume
- Statement of Purpose
Application Requirements
Admissions Categories and Grade Point Average Requirements
International Application Information
Degree Requirements
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Required Courses (20 credits)
1. Computer Science core. Select one sequence (6 credits) from:
or
2. Statistic score courses: Select one sequence (6 credits)
or
3. Other courses. Select 4 courses from:
Other Requirements (3 credits)
Satisfy this requirement by getting graduate coordinator pre-approval for an internship, professional position, or research group. Document the experience by registering for 3 credit hours of internship, directed readings, or a similar course.
Culminating Experience (16 credits)
Minimum Total Credits (60 credits)
Additional Requirements
- Minimum 3.0 graduate cumulative grade point average
- Maximum of 10 credits of 5000-level coursework may be counted toward degree requirements
- Preliminary Examination
- Minimum of 16 credits of dissertation research (maximum of 30 credits of dissertation research are applicable to degree requirements)
- Dissertation Defense and Publication of Manuscript on OhioLINK
- All requirements must be completed within eight years from the end of the earliest course used to fulfill degree requirements.
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Return to: Graduate Programs
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