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The mission of the program is to create scientifically 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.

There are two phases of the doctoral program at HU: a learning phase that includes coursework, seminars, research, and fieldwork that contributes to the student’s knowledge in the program of study; and a research phase that focuses on the student’s original research culminating in their final examination. Upon a student’s successful completion of all required course work, defense of the dissertation, and completion of all milestones, the student is awarded the doctoral degree in the program of study.

The Ph.D. in Data Science at HU is a four to five-year program. The Harrisburg Master’s in Analytics program is technically considered to be the first two years of the Ph.D. program. The proper time, however, for applying to the Ph.D. program (the last two years of the program ) is in the last semester of HU’s Master’s program.

Prior education at the Master’s level and above in related fields, such as computer science, can lead to some transfer credit. But in no way can a Master’s in another field at Harrisburg University, or a master’s degree in Data Analytics from another institution serve as a substitute for the first two years of the Ph.D. Program. The educational track that is provided here in the first two years is specific to HU’s Ph.D. program as a whole.

Program Goals

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

Doctorate Program Admissions Process

Doctorate program applicants are encouraged to apply at least six months prior to the start of any semester. This application process allows ample time for an admissions decision and development of an academic schedule. The Admission Committee reviews all documents and will request an interview with the applicant prior to making an admission decision for a limited number of applicants to become resident or non-resident candidates for the degree.

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Program Courses

The following courses comprise the 36 semester hours required for the Ph.D. in Data Sciences. Complete 18 semester hours in upper level courses, 6 semester hours of Doctoral Research Seminars and defend dissertation proposal, and complete 12 hours to complete the dissertation process and defend the dissertation.

ANLY 705 – Modeling for Data Science (3 credits)

This course provides a more in depth presentation of the theory behind linear statistical models, segmentation models, and production level modeling. Further emphasis is placed on practical application of these methods when applied to massive data sources and appropriate and accurate reporting of results.

Prerequisites: Completion of the requirements within the first two years of the doctoral program
Corequisites: None

ANLY 710 – Appld Expmntal & Quasi-Expmnt Des (3 credits)

Methods and approaches used for the construction and analysis of experiments and quasi-experiments are presented, including the concepts of the design and analysis of completely randomized, randomized complete block, incomplete block, Latin square, split-plot, repeated measures, factorial and fractional factorial designs will be covered along with methods for proper analysis and interpretation in quasi-experiments.

Prerequisites: Completion of the requirements within the first two years of the doctoral program
Corequisites: None

ANLY 715 – Applied Multivariate Data Analysis (3 credits)

This course provides hands-on experience in understanding when and how to utilize the primary multivariate methods Data Reduction techniques, including Principal Components Analysis and Exploratory and Confirmatory Factor Analyses, ANOVA/MANOVA/MANCOVA, Cluster Analysis, Survival Analysis and Decision Trees.

Prerequisites: Completion of the requirements within the first two years of the doctoral program
Corequisites: None

ANLY 720 – Data Science from an Ethical Perspe (3 credits)

This course introduces the power and pitfalls of handling user information in an ethical manner. The student is offered a historical and current perspective and will gain an understanding of their role in assuring the ethical use of data.

Prerequisites: Completion of the requirements within the first two years of the doctoral program
Corequisites: None

ANLY 725 – Research Seminar in Unstructured (3 credits)

This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Unstructured Data. Topics of special interest in Unstructured Data analysis are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester.

Prerequisites: Completion of the requirements within the first two years of the doctoral program
Corequisites: None

ANLY 730 – Research Seminar in Forecasting (3 credits)

This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Forecasting. Topics of special interest in Forecasting Data analysis are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester.

Prerequisites: Completion of the requirements within the first two years of the doctoral program
Corequisites: None

ANLY 735 – Research Seminar in Machine (3 credits)

This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Machine Learning. In addition, topics of special interest in Machine Learning are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester.

Prerequisites: Completion of the requirements within the first two years of the doctoral program
Corequisites: None

ANLY 740 – Graph Theory (3 credits)

This course introduces standard graph theory, algorithms, and theoretical terminology. Including graphs, trees, paths, cycles, isomorphisms, routing problems, independence, domination, centrality, and data structures for representing large graphs and corresponding algorithms for searching and optimization.

Prerequisites: Completion of the requirements within the first two years of the doctoral program
Corequisites: None

ANLY 745 – Functional Prog Mthds for Data Sci (3 credits)

This course is designed to build on the Functional Programming Methods for Analytics course. The student works to extend programming skills to write the student’s own versions of popular statistical functions using a current programming language.

Prerequisites: Completion of the requirements within the first two years of the doctoral program
Corequisites: None

ANLY 755 – Advanced Topics in Big Data (3 credits)

Topics include the design of advanced algorithms that are scalable to Big Data, high performance computing technologies, supercomputing, grid computing, cloud computing, and Parallel and Distributed Computing, and issues in data warehousing.

Prerequisites: Completion of the requirements within the first two years of the doctoral program
Corequisites: None

ANLY 760 – Doctoral Research Seminar (3 credits)

This seminar provides support to doctoral students within their specific domains of research. Led by the faculty advisor for that domain, the course is designed to provide a forum where faculty and students can come together to discuss, support, and share the experiences of working in research.

Prerequisites: Completion of doctoral coursework requirements; pass qualification examination
Corequisites: None

ANLY 761 – Research Seminar in Unstructured (3 credits)

This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Unstructured Data. Topics of special interest in Unstructured Data analysis are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester.

Prerequisites: None
Corequisites: None

ANLY 762 – Research Seminar in Forecasting (3 credits)

This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Forecasting. Topics of special interest in Forecasting are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester.

Prerequisites: None
Corequisites: None

ANLY 763 – Research Seminar in Machine (3 credits)

This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Machine Learning. Topics of special interest in Machine Learning are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester.

Prerequisites: None
Corequisites: None

ANLY 799 – Doctorial Studies (6 credits)

Advancement to candidacy is a prerequisite of this course. This is an individual study course for doctoral students. Content to be determined by the student and the student’s Doctoral Committee. May be repeated for credit.

Prerequisites: None
Corequisites: None

International Admissions

Information for International Students

All of the University’s graduate programs are STEM approved, and curricular practical training (CPT) is offered for this program.

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