Data Sciences, Ph.D.

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Doctorate education focuses on enabling the student to make original contributions to their respective fields of study. There are two phases of the doctoral program at HU: (1) a learning phase that includes coursework, seminars, research, and fieldwork that contributes to the student’s knowledge in the program of study; and, (2) a research phase that focuses on student’s original research culminating in his/her final examination. Upon a student’s defense of the dissertation and completion of all other requirements, the student is awarded the doctoral degree in the program of study.

Mission Statement

The mission of the Harrisburg University Data Science Program is to create scientically minded and technically proficient professionals with a comprehensive background in the methodological diversity of the data sciences and the intellectual depth to offer influential perspectives to analytical teams across disciplines.

Program Goals

The Data Sciences Program will produce Ph.D. graduates who:

Will have applied diverse data science methodologies using a scientific process individually or in teams to provide impactful insights from large sets of data;
Will have used effective communications to explain insights from analytical processes on data to diverse audiences; and,
Will have grown professionally through self-study, continuing education, and professional development.

Student Outcomes

HU Competencies	Analytics Program Student Outcomes
Critical Thinking	Identify and assess the opportunities, needs and constraints for data usage Conduct independent research at a level appropriate for expanding the body of knowledge in the data science domain
Information Literacy	Make clear and insighful analyses changing direction quicky as required by these analyses Measure, evaluate, and explain the level of quality of a dataset and determine/develop a plan to improve the quality
Teamwork and Collaboration	Work effectively in a team to develop data analytic solutions
Ethical Decision Making	Recognize and analyze ethical issues related to intellectual property, data security, integrity, and privacy
Communication	Communicate clearly and persuasively to a variety of audiences

Specific Course Requirements for the Ph.D. in Data Sciences

The following courses assume the completion of the 36 semester hours required for the Master of Science in Analytics. Each course listed represents a three-semester hour value. Details about each of the Milestones listed can be found in the PhD Guidebook on MyHU.

Complete the 6 courses from the following upper-level courses - 18-semester hours:

ANLY 705 - Modeling for Data Science (3 semester hours)
ANLY 710 - Applied Experimental and Quasi-Experimental Design (3 semester hours)
ANLY 715 - Applied Multivariate Data Analysis (3 semester hours)
ANLY 720 - Data Science from an Ethical Perspective (3 semester hours)
ANLY 725 - Research Seminar in Unstructured Data Analysis (3 semester hours)
ANLY 730 - Research Seminar in Forecasting (3 semester hours)
ANLY 735 - Research Seminar in Machine Learning (3 semester hours)
ANLY 740 - Graph Theory (3 semester hours)
ANLY 745 - Functional Programming Methods for Data Science (3 semester hours)
ANLY 755 - Advanced Topics in Big Data (3 semester hours)

Complete the Doctoral Research Seminars (6 semester hours) and defend dissertation proposal

ANLY 760 - Doctoral Research Seminar (6 semester hours)
ANLY 761 - Doctoral Research in Unstructured Data (3 semester hours)
ANLY 762 - Doctoral Research in Forecasting (3 semester hours)
ANLY 763 - Doctoral Research in Machine Learning (3 semester hours)

Comprehensive examination to qualify for doctoral thesis research work

Complete the dissertation process (12 semester hours of ANLY 799: Doctoral Studies) and defend the dissertation

ANLY 799 - Doctoral Studies (6 semester hours)