Data Science and Engineering, PhD

Contact Information

Dr. Randy Hoover
Department of Electrical Engineering and Computer Science
E-mail: Randy.Hoover@sdsmt.edu

Students are responsible for checking with their advisors for any program modifications that may occur after the publication of this catalog.

PhD in Data Science and Engineering

Offered jointly with University of South Dakota (USD). Courses are offered at both South Dakota Mines and USD campuses.

Degree requirements

Distribution of credits

Core requirements: 12 credits
Research requirements: 36 credits
Elective requirements: 24 credits
Total credits: 72

At least 36 of the required 72 credits must be taken at the 600-level or above.

Students may apply 24 coursework credits and 6 research credits from a previous MS degree toward the PhD requirements, subject to approval by the student’s committee.

Core requirements

DSE 559 Introduction to Data Science Credits: 3
MATH 543 Data Analysis Credits: 3
ENGM 535 Optimization Techniques Credits: 3
CSC 790 Seminar Credits: 1 to 3 *
*A minimum of three credits of CSC 790 are required.
All core classes must be taken at the 500-level or above.

Elective requirements

Each PhD program of study is individually designed to meet the goals of the student. Courses from a variety of areas, for example biology, chemical engineering, chemistry, computer engineering, computer science, electrical engineering, industrial engineering, materials and metallurgical engineering, mathematics, mechanical engineering, or other disciplines may be used to fulfill the elective requirements in a manner intended to complement the student’s research. Elective courses in the area of the student’s intended research are to be selected in consultation with, and approved by, the student’s major advisor.

Research requirements

The completion of a doctoral dissertation, approved by the student’s graduate committee and the Dean of Graduate Education, is required for this degree. PhD students are expected to participate in the creation of new knowledge and applications in data science and engineering.

CSC /MATH /IENG 898D Dissertation Credits to be arranged **

**At least 36 credits of 898 are required. No more than 36 credits of 898 may be counted toward the degree.

Examinations

Detailed information on examination policies, admission to candidacy, and defense of dissertation may be found in the Graduate Education section of this catalog and the DSE Graduate Handbook.

Qualifying examination

The qualifying examination has two components to demonstrate the student’s aptitude for doctoral work: 1) proficiency in the foundational material of the discipline and 2) necessary skills and drive for advanced research. The format and timing of the examination are set by each program, but it must be completed within the first two years of study.

Comprehensive examination and admission to candidacy

PhD students must prepare a dissertation prospectus, which is a plan describing the proposed content and format of the dissertation in sufficient detail for the student’s graduate committee to evaluate whether the scope and value of the work warrants a PhD degree. The student is admitted to candidacy upon approval of the prospectus by the committee. The format and timing of the prospectus is set by each program, but it must be completed no later than two years after the qualifying exam.

Dissertation defense

A dissertation defense and a final oral examination are required for this degree.

Additional requirements

In addition to these degree-specific requirements, the student must also meet the requirements and policies applied to all graduate degrees by the Council of Graduate Education.

Objectives and Outcomes

Student Outcomes:

Have the ability to analyze current research and identify knowledge boundaries.
Understand data as an abstract concept and how data encodes and captures information and insights.
Derive insights from large, complex data sets.
Be fluent in programming, data processing, model development, statistical analysis, model evaluation, and data visualization.
Effectively communicate complex ideas to a variety of audiences and stakeholders both verbally and written.
Recognize the ethical and legal issues relevant to data science and their impact on society.
Develop both novel and applied data-driven solutions from a wide array of scientific fields.
Understand the theoretical underpinnings of data science methods as well as how these underpinnings apply in practical applications.