Data Science

Program Strengths

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.

Why a Doctorate in Data Science?

  • Shortage of skilled staff will persist. In the U.S. alone, there will be 181,000 deep analytics roles in 2018 and five times that many positions requiring related skills in data management and interpretation
  • Over the next five years, spending on cloud-based Big Data and analytics solutions will grow three times faster than spending for on-premise solutions
  • Adoption of technology to continuously analyze streams of events will accelerate as it is applied to Internet of Things (IoT) analytics

Admission Criteria

  • Three letters of recommendation
  • GRE/GMAT test scores
  • Minimum 3.0 GPA on 4.0 scale
  • TOEFL/IELTS scores for international students

All applicants will apply directly to the Ph.D. program whether or not they have earned a master's degree. Upon review by the admissions committee, students who do not have an earned master's degree in data science, statistics, applied statistics, computer science, mathematics, or a closely related field, will be enrolled in the 90 credit pathway and will first complete the 30 credit MS in Data Science program before entering the 60 credit post-master Ph.D. program. Others needing additional prerequisite coursework may be admitted to the 60 credit pathway with additional credit requirements.

Program Curriculum (60 credit hours)

  • Choose one of the following sequences in Computer Science (6 credit hours)
    • CS 6260 Visualization (3) and CS 7200 Machine Learning (3)
    • CS 6500 Big Data Analytics (3) and CS 7300 Unsupervised Feature Learning (3)
  • Choose one of the following sequences in Statistics (6 credit hours)
    • MATH 7550 Statistical Learning I (3) and MATH 7560 Statistical Learning II (3)
    • MATH 7570 Linear Stat Inference (3) and MATH 7590 Gen Linear Models and Ext (3)
  • Take the following courses (8 credit hours)
    • DATA 7770 Data Science Exploration (1)
    • DATA 7780 Data Science Communication (1)
    • PHIL 6XXX Ethical Issues in Data Science (3)
    • STAT 7440 Advanced Data Mining (3)
  • Applied Data Science Experience (3 credit hours)
    • DATA 7890 Internship/DATA 7930 Directed Reading (3)
  • Elective Courses (21 credit hours)
  • Qualifying Examination
  • Preliminary Examination
  • Dissertation (16 credit hours)

Financial Aid

Scholarships and stipends are available for this program. For more information, please contact the department. Domestic students enrolled in four (4) or more credit hours are eligible to apply for financial aid using the Free Application for Federal Student Aid (FAFSA) to calculate student contribution and financial need. You may apply online.

Graduate assistantships are available on a competitive basis.  Assistantships include a scholarship and a stipend. Undergraduate GPA, GRE/GMAT scores, letters of recommendation, the student's statement, and other materials are all used in the aid decision.  The department reserves the right to adjust the level of funding conditional on the availability of funds or the student's academic progress. For any general information about graduate assistantships, click here.

Green Robert CNBG0746
Graduate Program Director

Dr. Robert Green
Assistant Professor and Graduate Coordinator
College of Arts and Sciences