Data Science Ph.D. Curriculum

The Data Science Ph.D. program is designed for students with undergraduate or graduate backgrounds in data science, computer science, mathematics, statistics, engineering, information technology, or related areas who wish to contribute to data and computational sciences. The curriculum combines mathematics and statistics, AI and machine learning, advanced data analytics and database design, as well as related topics in research and discovery.

Highlights include:

  • Mathematical and statistical theory
  • Advanced scientific computing
  • Advanced database design
  • Advanced Statistics
  • Big data management and analytics
  • Artificial Intelligence and machine learning
  • Computational software engineering
  • Predictive modeling and analytics
  • Visualization and unstructured data analysis
  • Big data privacy and security
  • Ethical, Legal and Societal Issues in big data analytics

Graduation Requirements for the Data Science Ph.D. Program

Completion of the program requires 75 graduate credits.  This includes:

  • 15 hours of foundational coursework drawn from computer science and mathematics.
  • 36 hours of “core” courses drawn from data science, computer science and advanced statistics.
  • 1 hour of Candidacy Exam (or qualifying) study and preparation in the following 6 courses: 
    • MSDS 550 Computational Machine Learning;
    • MSDS 565 Predictive Modeling and Analytics;
    • MSDS 700 Fundamentals of Database Management Systems;
    • MSDS 710 Mathematical and Statistical Theory;
    • MSDS 715 Data Modeling for Big Data;
    • MSBD 720 Advanced Statistics. The candidacy exam is normally taken at the end of the second year.
      The PhD student must pass all 6 courses and will be allowed to retake the exam(s) from any of the courses no more than once.
  • 5 hours of research seminar.
  • 6 hours of special topics and electives.
  • 12 hours devoted to dissertation and defense.

Course Schedule Overview

Data Science Ph.D. students will enroll in three concurrent courses during the Fall, Spring, and Summer Semesters.

The Pathway to a Data Science Ph.D. degree provide an outline of all degree requirements and a curriculum map, organized by semester, for completing them.

Data Science Ph.D. Program goals

Graduates will have a deep understanding of data science methods and will be engaged, ethical and collaborative scientists. Specific programmatic goals for Data Science Ph.D. program graduates are to possess:

  • Critical Thinking Skills such as creative thinking, innovation, inquiry and analysis, evaluation and synthesis of information at an advanced scientific level; critically evaluate quantitative approaches in the scientific literature;
  • Empirical and Quantitative Skills that include the advanced manipulation and analysis of data or observable facts resulting in informed conclusions;
  • Personal Responsibility Skills such as the ability to connect choices, actions and consequences to ethical decision-making in the data science context;
  • Communication Skills which include effective development, interpretation and expression of ideas through written peer reviewed scientific publications, oral and visual communication;
  • Teamwork Skills includes the ability to consider different points of view and to lead and work effectively with others to support a shared or goal; 
  • Social Responsibility Skills to include intercultural competence, knowledge of civic responsibility, and the ability to serve and engage effectively in regional, national, and global communities of importance to data science.

Learning Outcomes

A Meharry SACS Ph.D. in Data Science will prepare graduates for rewarding careers in academia, any industry or government. Our graduates will:

  • Function effectively on a data science team and as the team leader in the area of rigorous computational and statistical investigation.
  • Identify and analyze social, legal, ethical and regulatory issues surrounding a data science research project.
  • Apply advanced knowledge of data science and mathematical statistics appropriate to the discipline.
  • Conduct an advanced data science research and design phase of a real-world project, employing data science methods, techniques, and practices.
  • Provide new solutions to complex big data issues in data science through the design and implementation of advanced modeling techniques, such as predictive analytics and data mining or stochastic methods from artificial intelligence.
  • Implement and present a comprehensive real-life, industry-type or academic style data science research problem and solution.