Doctor of Philosophy
In Data Science
(PhD-DS)

This course provides an introduction and overview of data science in order to make informed decisions about business needs. The objective of this course is to introduce you to the nature and methods of data science at the doctoral level. While data science is a varied and nuanced field that generally combines computer science with advanced mathematics, it’s application in research and industry ranges from understanding problem statements to producing insights using validated methods. You will explore data science life cycle and determine appropriate design methods and management of data to fit the context of research and/or industry issues. 

This course includes analytics methods to understand how data is shaped in relation to how it can be analyzed. This is a foundational skill for data scientists and important to apply prior to creating confirmatory (final) models that predict and deliver end-user insights for decision making. The focal points in this course are descriptive statistics and exploratory data analysis. Specific attention is given to measures of central tendency, clustering, variability, and frequency. You will learn identification of the appropriate univariate analysis for use in applied research in a business context. You will also learn to apply clustering analysis in relation confirmatory models.

Establishing insights concerning population estimates, while understanding and communicating knowledge about variance in likely outcomes, is a fundamental skill of a data scientist. At the doctoral level, you will apply this understanding to the delivery of documentation for an audience of stakeholders who hinge key business decision-making on understanding the likelihood of an event’s occurrence. Within the academic setting, this understanding drives the development of foundational knowledge for research in the resolution of problem settings. In this course you will learn how to understand probability functions to apply your knowledge as a decision-maker or educator.

Data preparation is the process in which data from one or more sources is cleaned and transformed to improve its quality before its use in data analysis. This process requires the majority of the time required to complete the data science lifecycle. During this course, you will learn the tools and techniques used during data preparation and the role they play in delivering quality data for making informed decisions. You will end the course by gathering and preparing data sets for future analysis.

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.

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.

This course includes methods, means, and processes involved in transforming raw data into useable form for a multitude of analytics. Data curation is a set of processes that transforms, manages, stores, and democratizes data for use for analysts and data scientists through the lifecycle of data. The curation of data enables an organization or researcher to maximize the value of the data and effectively use the transformed data to produce and deliver insights. This course considers data that have been already acquired and integrated into useable repository and focuses on teaching techniques to make those data usable for next steps in developing analytics models.

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.

This course examines current techniques and methods utilized in manipulating data in quantitative analysis. You will analyze processes within data science that help organize large data sets. You will explore the differences in statistical reasoning based on Frequentists and Bayesian philosophy and will analyze output based on Artificial Neuron Network analysis.

The ability to generate insights from data is a critical data science competency. As part of this course, you will expand upon your understanding of ethics regarding data reporting. As such, you will be required to develop standards needed to improve integrity and validity of data. This course also covers textual and tabular reporting concepts and formats used in data science. During this course, you will evaluate methods for communicating data outputs and outcomes. You will end the course by creating a data presentation report and executive memo that adheres to industry standards.

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.

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.

The results of technical research are frequently used to support informed management decisions. This course provides technology leaders and professionals with the skills needed to design and conduct quantitative research studies to support specific types of data. During this advanced course in research, you will explore and apply different types of quantitative research methods and statistical techniques. You will also explore instrumentation, data collection, and data analysis tools and techniques to create aligned, ethical, and substantive research designs.

A quantitative research design includes objective analysis using experimental, quasi-experimental, and related techniques. 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. During this course, you will work through the scientific research process and apply your knowledge of quantitative research design to develop a technical research proposal in which you can use to support your research interests through the remainder of your program.

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.

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.

Students in this course will be required to complete Chapter 1 of their dissertation proposal including a review of literature with substantiating evidence of the problem, the research purpose and questions, the intended methodological design and approach,  and the significance of the study. A completed, committee approved (against the minimum rubric standards) Chapter 1 is required to pass this course successfully. Students who do not receive approval of Chapter 1 to minimum standards will be able to take up to three supplementary 8-week courses to finalize and gain approval of Chapter 1.

Students in this course will be required to work on completing Chapters 1-3 of their dissertation proposal and receive committee approval for the Dissertation Proposal (DP) in order to pass the class. Chapter 2 consists of the literature review. Chapter 3 covers the research methodology method and design and to includes population, sample, measurement instruments, data collection and analysis, limitations, and ethical considerations. In this course, a completed, committee-approved Chapters 2 and 3 are required and, by the end of the course, a final approved dissertation proposal (against the minimum rubric standards). Students who do not receive approval of the dissertation proposal will be able to take up to three supplementary 8-week courses to finalize and gain approval of these requirements.

Students in this course will be required to prepare, submit, and obtain approval of their IRB application, collect data, and submit a final study closure form to the IRB. Students still in data collection at the end of the 12-week course will be able to take up to three supplementary 8-week courses to complete data collection and file an IRB study closure form.

In this dissertation course students work on completing Chapters 4 and 5 and the final Dissertation Manuscript. Specifically, students will complete their data analysis, prepare their study results, and present their findings in an Oral Defense and a completed manuscript. A completed, Committee approved (against the minimum rubric standards) Dissertation Manuscript and successful Oral Defense are required to complete the course and graduate. Students who do not receive approval for either or both their Dissertation Manuscript or defense can take up to three supplementary 8-week courses to finalize and gain approval of either or both items as needed.