Data Science - Archived 2019-20 Graduate Catalog

Program Coordinator: Robert C. Green II, Ph.D.
Address: Hayes Hall 243
Phone: 419-372-8782
Program Web Page:

Degrees Offered
Ph.D. in Data Science
M.S. in Data Science

Programs Offered
Data Science

Program 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.

Prerequisite to Graduate Work
Prerequisite coursework includes differential, integral, and multivariate calculus, linear algebra, senior-level introduction to probability and statistics, programming skills in high level languages such as C, C++, Java, Python, and understanding of data structures and computer algorithms

Admission Procedure
Applicants must complete the admission process required by the Graduate College that includes submission of the complete online application, all previous college and university official transcripts (a minimum GPA of 3.0 required), GRE General Test or GMAT scores, statement of purpose, resume, three letters of recommendation from faculty or professionals in the field, and, for certain international students, TOEFL or IELTS scores. Applicants should refer to the Graduate College web site for a complete list of admission procedures. 

All students will apply to the PhD program whether or not they have earned a master’s degree.  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 Data Science Master’s program before entering the 60 credit post-master PhD program. Students with an earned master’s degree in data science, statistics, applied statistics, computer science, mathematics, or a related field should have the course background that makes it possible to immediately take PhD-level courses in computer science or statistics.  Others who have been determined by the Committee to need additional prerequisite coursework may be admitted to the 60 credit pathway with additional credit requirements.

Degree Requirements

Curriculum for the Master of Science in Data Science:

Required courses (30 credit hours)

  1. CS 5200 Artificial Intelligence Methods (3 credit hours) 
  2. CS 5620 Database Management Systems (3 credit hours) 
  3. CS 6010 Data Science Programming (3 credit hours) 
  4. MATH 6410 Probability Theory I (3 credit hours) 
  5. MATH 6420 Mathematical Statistics II (3 credit hours) 
  6. STAT 5020 Regression Analysis (3 credit hours) 
  7. STAT 5160 Time Series Analysis (3 credit hours) 
  8. STAT 6440/CS 6440 Data Mining (3 credit hours) 
  9. OR 6610 Linear and Integer Programming (3 credit hours) 
  10. DATA 6910 Data Science Project (3 credit hours) 

Curriculum for the 60 Credit Hour PhD in Data Science Program:
Note that the core required courses cover statistical and machine learning in complementary ways, scaffolding a student's learning of these concepts.

Required Courses (23 credit hours):
Choose one of the following sequences in Computer Science (6 hours) 

  1. CS 6260 Visualization (3)  and CS 7200 Machine Learning (3)
  2. CS 6500 Big Data Analytics (3) and CS 7300 Unsupervised Feature Learning (3)

Choose one of the following sequences in Statistics (6 hours) 

  1. MATH 7550 Statistical Learning I (3) and MATH 7560 Statistical Learning II (3)
  2. MATH 7570 Linear Statistical Inference (3) and MATH 7590 Generalized Linear Models and Extensions (3)  

Take the following four courses (8 hours) 

  1.  DATA 7770 Data Science Exploration (1)  
  2. DATA 7780 Data Science Communication (1)  
  3. PHIL 6XXX Ethical Issues in Data Science (3)  
  4. STAT 7440 Advanced Data Mining (3)   

Applied Data Science Experience (3 hours) 
DATA 7890 Internship/DATA 7930 Directed Reading: Satisfy this requirement by getting Graduate Coordinator's pre-approval for a data science related internship or professional position, or joining a research group on or off campus, preferably after successfully completing at least 9 credit hours in Data Science. The experience is documented by registering for 3 credit hours of internship, DATA 7890, directed readings credit such as DATA 7930, or similar course.  The final report for these classes must identify and analyze ethical considerations encountered during the experience.  

Elective Courses (21 credit hours):  
Choose 7 additional courses with at least 2 from CS, at least 1 from MATH,  and at least 1 from OR/STAT. If not counted toward a required course sequence, CS 6260, 7200, 6500, 7300 and/or MATH 7550, 7560, 7570, 7590 may be used as electives.  At most three starred (*) courses may be counted as electives. At most 10 credit hours of 5000-level courses may be counted toward the degree. Courses counted toward a master's degree at BGSU cannot be counted as electives. 

  • CS 5170 Introduction to Parallel Computing 
  • CS 5200 Artificial Intelligence Methods* 
  • CS 5620 Database Management Systems* 
  • CS 6010 Data Science Programming*  
  • CS 6200 Advanced Topics in Artificial Intelligence 
  • CS 6270 Advanced High Performance Computing  
  • CS 6630 Spatial and Multidimensional Database  
  • CS 6800 Special Topics Course, with advisor approval (e.g., Applied Machine Learning, Applied Big Data Analytics, Big Data Processing. etc.) 
  • DATA 7820 Topics in Data Science  
  • MATH 6410 Probability Theory I* 
  • MATH 6420 Mathematical Statistics II* 
  • MATH 6440 Stochastic Processes 
  • MATH 6460 Nonparametric Statistical Inference 
  • MATH 6470 Sequential Statistical Inference 
  • MATH 6480 Bayesian Statistical Inference 
  • MATH 6490 Statistical Graphics  
  • MATH 6570 Statistical Computing
  • MATH 6710 Survival Analysis 
  • MATH 6720 Biostatistical Methods 
  • MATH 6820 Topics in Mathematics or Statistics, with advisor approval (e.g. Sequential Analysis, Causal Inference, Multilevel Models) 
  • MATH 7400 Multivariate Statistics 
  • MATH 7450 Advanced Mathematical Statistics 
  • MATH 7460 Advanced Mathematical Statistics 
  • OR 6610 Linear and Integer Programming* 
  • OR 6620 Probability Models for Decision Making 
  • STAT 5020 Regression Analysis* 
  • STAT 5160 Time Series Analysis* 
  • STAT 6300 Applied Multivariate Analysis 
  • STAT 6340 Discrete Data Analysis 
  • STAT 6440 Data Mining* 
  • STAT 6750 Research Methods in Statistics 

Qualifying Examination: 
Students will take a qualifying examination preferably after the first  academic year of the PhD program (approximately 18 credit hours) in order for the program faculty to evaluate the student’s progress in the different disciplines necessary for the degree. In order to continue in the PhD program, a student must pass the qualifying examination. Students will take and pass one exam in Statistics and one exam in Computer Science. These may consist of two PhD-level exams or one PhD-level exam and one master's-level exam. The choices of the exams are:

  1. Choose one from the following Statistics sequences
    1. (PhD-level) MATH 7550-7560
    2. (PhD-level) MATH 7570-7590
    3. (MS-level) MATH 6410-6420
  2. Choose one from the following Computer Science sequences
    1. (PhD-level) CS 6260-7200
    2. (PhD-level) CS 6500-7300
    3. (MS-level) CS 5200-5620

Preliminary Examination: 
A student must pass the Qualifying Examination to qualify to take the Preliminary Examination. The Preliminary Examination includes a written and an oral component. This is a research-oriented examination intended to help prepare students to begin their dissertation research. The Preliminary Examination Committee must have representation from at least two of the three departments participating in the program.

A doctoral candidate must enroll in at least 16 credit hours of dissertation work (DATA 7990), maximum of 30 credit hours. 

Summary of credit hour requirements:   
Total minimum credit hours (60), consisting of Required Core (23), Elective courses (21), and Dissertation (16). 

Graduate Courses
Please access graduate courses online at Graduate courses offered by the MSW program use the prefixes CS, DATA, MATH, OR, PHIL, and STAT.

Updated: 08/14/2020 10:41AM