Doctor of Philosophy in Data Science

3.0 Doctoral Credit Hours

Data and databases are the foundation of all business systems. Organizations that do not understand the importance of data management are less likely to survive in the modern economy. During this course, you will study advanced concepts of database management systems and data warehouses. You will also research processes and techniques used to improve data repositories, manipulate data, and prevent data corruption. By the end of the course, you will be able to construct, assess, and transform data to improve business intelligence to support informed business decisions.

3.0 Doctoral Credit Hours

This course focuses on modern tools and methods to develop and work with large datasets. Some course concepts include the exploration of relational databases, distributed storage software, distributed computing methods, analytics and algorithms. You will explore current topics in the area of big data and potential future problems. You will investigate appropriate architectural techniques associated with big data. You will also evaluate the constructs of ethics in data science, propose techniques for application, and design a system to produce insights.

3.0 Doctoral Credit Hours

This course addresses needs in industry, business, and academia to improve performance and advance scientific knowledge. You will learn data mining techniques that help discover patterns, trends, anomalies, and associations that are otherwise hidden or unknown. In addition, this course introduces the fundamentals, principles, implementation techniques, and applications of data mining. Learning also includes data curation techniques, focuses on exploratory data analysis, prediction, classification, association analysis, similarity assessment and clustering, outlier, and anomaly detection. Interpreting and evaluating data analysis/data mining results is explored. Additionally, data mining experience for applications in computer vision, big data, and social networks will be provided.

3.0 Doctoral Credit Hours

This course examines the use of multivariate analysis to provide statistical and applied insight to data science problems. You will apply a variety of multivariate methods by selecting the appropriate models for the research questions posed and the data type. You will engage in hypothesis testing using parameters of multivariate data. Specifically, you will develop problem solutions by analyzing multidimensional data to derive meaningful insights into problem statements. Finally, you will present your results and actionable insights in an appropriate format for your audience.

3.0 Doctoral Credit Hours

Exploring univariate data analysis, beginning with the fundamentals of clustering univariate data, students learn to group similar data points an essential skill for identifying patterns in various fields. Moving on to advanced analytical methods, students extract deeper insights and discern trends. The next focus is on predictive analytics, where students acquire the skills to forecast outcomes using univariate data implementing predictive techniques, processes, and diagnostics. The natural language processing, underlining the criticality of effectively communicating analytical results is also a subject explored. Each section is carefully crafted to provide a detailed and practical learning experience, making this course ideal for anyone seeking to master the spectrum of univariate data analysis, from basic clustering techniques to advanced predictive and communication strategies.

3.0 Doctoral Credit Hours

Exploring univariate data analysis, beginning with the fundamentals of clustering univariate data, students learn to group similar data points an essential skill for identifying patterns in various fields. Moving on to advanced analytical methods, students extract deeper insights and discern trends. The next focus is on predictive analytics, where students acquire the skills to forecast outcomes using univariate data implementing predictive techniques, processes, and diagnostics. The natural language processing, underlining the criticality of effectively communicating analytical results is also a subject explored. Each section is carefully crafted to provide a detailed and practical learning experience, making this course ideal for anyone seeking to master the spectrum of univariate data analysis, from basic clustering techniques to advanced predictive and communication strategies.

3.0 Doctoral Credit Hours

Evaluating the accuracy and effectiveness of graphical representations of data is a critical skill required of experienced data scientists. This advanced course in data visualization will help you identify the appropriate questions required to evaluate the validity of the insights provided by others and develop the skills needed to influence other decision makers. During this course, you will synthesize research on the best practices associated with communicating through data visualization. You will also study techniques and processes you can use to dynamically communicate your interpretations of effective graphic interactive representations of data.

3.0 Doctoral Credit Hours

This course provides a survey of the different methods used to conduct technology-based research. During this course, you will learn about the research principles and methodologies that guide scientific inquiry in order to develop an understanding of the effects of research on individuals and organizations. Specifically, you will study the scientific research lifecycle, data collection methods, and research design methodology. You will finish the course by selecting a research design methodology to support your research interests through the remainder of your program.

3.0 Doctoral Credit Hours

This advanced Data Science research design course immerses you in diverse methodologies, equipping you with a multifaceted approach to data-driven investigations. From the foundations of quantitative research, which harnesses statistical analyses to draw generalizable conclusions from large datasets to the cutting-edge realm of Constructive Research focuses on models, frameworks, tools, and software used by industry to improve value creation. Throughout the course, you will delve into DSR (Design Science Research) and examine how it integrates theoretical and empirical constructs with industry practices to develop applied and testable models, enhancing the Data Science landscape. Common approaches include experimental design, where controlled experiments are conducted to test hypotheses, observational studies that involve data collection without intervention, and exploratory research to uncover patterns and relationships in data. Furthermore, cross-sectional and longitudinal designs allow for the examination of data at specific time points or over time.

3.0 Doctoral Credit Hours

Technical, quantitative research involves statistical analysis of data collected from a larger number of participants to determine an outcome that can be applied to a general population. Technical constructive research focuses on models, frameworks, tools, and software used by the industry to improve value creation. A constructive approach to research of a technical nature integrates theoretical and empirical constructs with standard practices and experience to develop an applied and testable model to improve the field of Data Science. During this course, you will work through the scientific research process and apply your knowledge of both quantitative and constructive research design to develop a technical research proposal that you can use to support your research interests through the remainder of your program.

3.0 Doctoral Credit Hours

New data science technologies and programs should be aligned to the organizational mission, vision, and values; thus, it is important for technology leaders to develop data, information, and knowledge management policies. During this advanced course in data and knowledge management, you will develop an enterprise data governance strategy that integrates industry standards and best business practices in data science. You will also design metrics to measure and analyze data integrity to ensure data validity, evaluate various influences on enterprise data and knowledge management, and recommend data management solutions.

3.0 Doctoral Credit Hours

The Pre-Candidacy Prospectus is intended to ensure students have mastered knowledge of their discipline prior to doctoral candidacy status and are able to demonstrate the ability to design empirical research as an investigator before moving on to the dissertation research coursework. During this course, you will demonstrate the ability to synthesize empirical, peer reviewed research to prepare for the dissertation sequence of courses. This course should be completed only after the completion of all foundation, specialization, and research courses.

3.0 Doctoral Credit Hours

In this course, students will complete Chapters 1 and 2 of their Dissertation Proposal. Chapter 1 includes a brief review of the literature with substantiating evidence of the problem, the research purpose and questions, the intended methodological design and approach, and the significance of the study, while Chapter 2 consists of the literature review, including the theoretical/conceptual framework. Completed and committee-approved (against the minimum rubric standards) Chapter 1 and Chapter 2 are required to pass this course successfully.

3.0 Doctoral Credit Hours

In this course, students will complete Chapter 3 and their Dissertation Proposal (DP). Chapter 3 includes the research methodology and design, population, sample, measurement instruments, data collection and analysis, limitations, and ethical considerations. Students will then assemble their Dissertation Proposal including all appendices necessary. A completed, committee-approved Chapter 3 and a final approved Dissertation Proposal (against the minimum rubric standards) are required by the end of the course.

3.0 Doctoral Credit Hours

In this course, students will prepare, submit, and obtain approval of their IRB application. After obtaining IRB approval, students will collect data and submit a data collection verification form when data collection is complete. Students will begin synthesizing collected data and analyzing results. IRB study approval and a completed data collection verification form are required by the end of the course.

3.0 Doctoral Credit Hours

In this course, students will complete Chapters 4 and 5, followed by their Dissertation Manuscript. Chapter 4 includes presenting study results aligned to the research design and research questions and/or hypotheses. Chapter 5 includes study implications, recommendations, and conclusions. Students will then compile and present findings in the completed Dissertation Manuscript and in an Oral Defense. A completed, committee-approved Chapter 5, Dissertation Manuscript, and successful Oral Defense (against the minimum rubric standards) are required to complete the course and graduate.