DOCTOR OF PHILOSOPHY

Data Sciences

ANLY 705 Modeling for Data Science (3 credit hours)
Prerequisites: Completion of the requirements within the first two years of the doctoral program

Description: 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.

 

ANLY 710 Applied Experimental and Quasi-Experimental Design (3 credit hours)
Prerequisites: Completion of the requirements within the first two years of the doctoral program

Description: 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.

 

ANLY 715 Applied Multivariate Data Analysis (3 credit hours)
Prerequisites: Completion of the requirements within the first two years of the doctoral program

Description: 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.

 

ANLY 720 Data Science from an Ethical Perspective (3 credit hours)
Prerequisites: Completion of the requirements within the first two years of the doctoral program

Description: 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.

 

ANLY 725 Current Topics in Unstructured Data Analysis (3 credit hours)
Prerequisites: Completion of the requirements within the first two years of the doctoral program

Description: This course follows a research seminar format.  Students and faculty develop research proposals, analyses, and reporting in the domain of Unstructured Data.  In addition, 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.

 

ANLY 730 Current Topics in Forecasting (3 credit hours)
Prerequisites: Completion of the requirements within the first two years of the doctoral program

Description: This course follows a research seminar format.  Students and faculty develop research proposals, analyses, and reporting in the domain of Forecasting.  In addition, 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.

 

ANLY 735 Current Topics in Machine Learning (3 credit hours)
Prerequisites: Completion of the requirements within the first two years of the doctoral program

Description: 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.

 

ANLY 740 Graph Theory (3 credit hours)
Prerequisites: Completion of the requirements within the first two years of the doctoral program

Description: 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.

 

ANLY 745 Functional Programming Methods for Data Science (3 credit hours)
Prerequisites: Completion of the requirements within the first two years of the doctoral program

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

 

ANLY 755 Advanced Topics in Big Data (3 credit hours)
Prerequisites: Completion of the requirements within the first two years of the doctoral program

Description: 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.

 

ANLY 760 Doctoral Research Seminar (6 credit hours)
Prerequisites: Completion of doctoral coursework requirements; pass qualification examination

Description: 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.

 

ANLY 799 Doctoral Studies (12 credit hours)
Prerequisites: Completion of doctoral coursework requirements; pass qualification examination

Description: 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.


To receive and application & catalog, email Steven Infanti, AVP Admissions, at Sinfanti@HarrisburgU.edu.