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.

Introducing statistical techniques is essential for extracting meaningful insights from data focusing on projects and research of Data Science. Through a comprehensive eight-week journey, students will explore topics such as normal distribution, hypothesis testing, power of test, type I and type II errors, sampling distributions, bootstrapping methods, diagnostic tools, validation techniques, and more. The course emphasizes practical applications, equipping learners with the skills to make data-driven decisions and extract hidden patterns from datasets. By mastering these inferential statistics techniques, students will be well-prepared to tackle complex real-world problems and enhance their expertise in the field of Data Science.

A comprehensive exploration of advanced predictive modeling and machine learning techniques. The course equips students with the skills needed to harness the power of data for making informed decisions. The course dives into regression models, decision trees, support vector machines, and ensemble methods like random forests and gradient boosting. Students will also learn about clustering methods, time series analysis, and the application of these techniques in real-world scenarios. Through hands-on projects and assessments, participants will become proficient in building predictive models, evaluating models, and effectively leveraging machine learning algorithms. It also equips students to interpret and communicate their findings effectively.

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 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.

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.

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.

Artificial intelligence is becoming more and more useful in helping solve everyday problems. Intelligent agents and natural language processing have become common in the marketplace. During this course, you will evaluate the impact of artificial intelligence on performance and enterprise resources. You will also expand your ability to improve an artificial intelligence application to address varied user specifications. Finally, you will be able to produce a complete artificial intelligence project plan that will integrate with current and proposed IT solutions for process improvement.

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.

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.

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.

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.