Data Science

Data Science Graduate Programs

Data scientist is ranked the number one best job in America, according to Glassdoor’s 2019 jobs report. With a median base salary of $108,000, job satisfaction of 4.3 out of 5 and more than 6,500 job openings, individuals with data science expertise are well-positioned for success in the job market.

BGSU’s graduate programs in data science are preparing individuals to be the next generation of data scientists. Our graduate programs in data science include Master of Science (M.S.) and Doctorate of Philosophy (Ph.D.) degrees.

Graduates of the M.S. program will be well-prepared to enter industry or begin Ph.D.-level work in data science. Graduates of the Ph.D. program are ready for careers in academia, industry and university research labs, government departments and big-name tech companies.


Application for fall/spring

Applications are due no later than four weeks prior to the start of the term. Applications are reviewed on a rolling basis as soon as all supporting materials have been received.

To be considered for highly competitive and limited funding, students will need to apply by March 15 for Fall or November 1 for Spring admittance.

Program Strengths

The M.S. in Data Science is a multidisciplinary program with expert faculty from three departments: Computer Science and Mathematics and Statistics, both in the College of Arts and Sciences, and Applied Statistics & Operations Research in the Schmidthorst College of Business. Graduates will be prepared to enter industry or begin Ph.D.-level work in Data Science, making the program a strong starting point for those interested in pursuing a Ph.D. in the field or furthering their career.

Why a Master of Science in Data Science at BGSU?

BGSU’s program is an interdisciplinary program. It requires a background in computer science, mathematics and statistic, and also provides an education that continues to blend those disciplines in a robust fashion, resulting in graduates that have a breadth and depth in all three disciplines.

According to “The Burtch Works Study: Salaries of Data Scientists & Predictive Analytics Professionals”:

  • 47% of data scientists sampled hold at least an M.S. degree, while 86% hold a graduate-level degree
  • The median salary for an entry level data scientist with an M.S. degree was $80,000
  • Demand for data scientists has been increasing as more organizations jump on board the “data bandwagon” and while the supply has been improving, it still lags behind.

Learning outcomes

Graduates of the program demonstrate:

  • Competency in the core concepts and techniques of computer science, operations research, and statistics as needed for data science.
  • The ability to acquire, clean and organize structured and unstructured datasets, and to prepare them for appropriate analysis. 
  • The ability to write original computer code in an appropriate computer language to implement solutions to data science problems.
  • The ability to model sources of data, to apply appropriate statistical procedures, and to interpret the results. 
  • The ability to communicate the results of a project to technical and nontechnical audiences.

Professional opportunities

Those who choose professional careers after graduation can expect to find employment with titles like data scientist, data analyst or data engineer performing tasks such as data acquisition, data cleaning/transformation, analytics, prescribing actions and programming automation.

M.S. in Data Science Curriculum (30 credit hours)

  • CS 5200: Artificial Intelligence Methods
  • CS 5620: Database Management Systems
  • CS 6010: Data Science Programming
  • MATH 6410: Probability Theory I
  • MATH 6420: Mathematical Statistics II
  • STAT 5020: Regression Analysis
  • STAT 5160: Time Series Analysis
  • STAT 6440/CS6440: Data Mining
  • OR 6610: Linear and Integer Programming
  • DATA 6910: Data Science Project

Admissions requirements

Prerequisite coursework includes differential, integral, and multivariate calculus, linear algebra, senior0level introduction to probability and statistics, programming skills in high-level languages as C, C++, Java, Python and understanding of data structures and computer algorithms.

Applicants must have a minimum GPA of 3.0 on a 4.0 scale (or equivalent). Applicants are required to submit scanned copies of official or unofficial transcripts from all institutions attended. Upon admission, final official or notarized copies of transcripts from all institutions where degrees were earned and diplomas from international institutions must be submitted. They are also required to submit official scores from the Graduate Record Examination (GRE) or the Graduate Management Admissions Test (GMAT).

Applicants must also submit three letters of recommendation from faculty or professionals in the field, a statement of purpose and a current resume.

International applicants are also require to submit scores from the International English Language Testing System (IELTS), the Pearson Test of English Academic (PTEA), or the Test of English as a Foreign Language (TOEFL). Successful completion of ELS level 112 will also be accepted for this requirement.

Cost of Tuition

The current cost of tuition is available through the Office of the Bursar.

Financial Assistance

Scholarships and stipends are limited and available on a competitive basis. For more information, contact the coordinator.

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 at


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.


Successful candidates for the M.S. or Ph.D. in Data Science must demonstrate proficiency through coursework or clear professional experience in the following areas: 

  • Differential, integral, and multivariate calculus (BGSU equivalent of Math 1310, 2320, and 2330/2350)
  • Linear Algebra (BGSU equivalent of Math 3320)
  • Senior-level Introduction to Probability (BGSU equivalent of Math 4410)
  • Senior-level Statistics (BGSU equivalent of Math 4420)
  • Programming skills in high level languages such as C, C++, Java, Python (BGSU equivalent of CS 2010 and 2020)
  • Data structures (BGSU equivalent of CS 3350)
  • Algorithms (BGSU equivalent of CS 4120)

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.

Yan Wu, Ph.D.
Associate Professor and Graduate Coordinator of Computer Science

Updated: 09/17/2021 10:33AM