Doctor of Philosophy
in Computer Science
(PhD-CS)

In this course, students will study how computer science concepts and theories can be applied and used to solve real-world problems. Students will also be introduced to research in computer science and become acquainted with venues for sharing knowledge in the field. 

In this course, students will explore emerging issues in computer network design and management. Of particular attention will be topics on how mobile computing has changed the way networks are designed and managed.

Organizations depend on distributed systems and parallel computing to achieve the performance and reliability required by their systems. These systems provide organizations with the ability to leverage hardware and software platforms to improve efficiency. During this course, you will research the latest developments in computing concepts, such as instruction level parallelism, task level parallelism, multicore computing, and distributed algorithms. You will complete the course by predicting evolving trends in distributed algorithms and parallel computing.

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 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 will consolidate a foundation of concepts relating to security and cybersecurity, enabling the successful examination of greater detail and complexity in later courses. You will examine the full range from governance through compliance with laws, through people risks, to technology. Various mitigation options are considered to reduce vulnerabilities, counter threats, and to protect an organization’s data and system assets. You will study the need to protect organizations from future exposures and apply critical thinking and synthesis to motivate the resources needed to defend an organization.

With more application systems and data processed, together with mobile, cloud, and virtual environments all technology system risks increase for security, privacy, compliance issues. While expectations for safety rise, increased complexity and threats make the provision far more difficult and expensive. Record tampering and accidental or intentional exposure are also among the internal risks every enterprise face. During this course, you will learn how to assess, mitigate, and manage risks, threats, and vulnerabilities. You will also study theories, models, standards, and frameworks that aid the effective design, protection mechanisms, practices, and procedures to implement and manage operations within information systems.

This course serves as an extensive exploration of statistics for the technology leader. Included in the course, is an advanced examination of statistical analyses commonly used for information systems and technology research. During this course, you will use different statistical tools to enhance your advanced analytical skills. These statistical analysis skills are required to plan, conduct, and interpret quantitative data to inform enterprise decisions. You will also learn to illustrate and produce technical output reports.

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.

Business processes change continually and require new technology solutions to improve production. Improved technology solutions can speed up processes, increase service delivery speed, and reduce the number of defects. This project-driven technical research course explores the principles and processes needed for the efficient development of software solutions with a focus on software quality design and development. During this course, you will research and suggest models and frameworks to improve software development processes based on different business problems and needs.

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 course is an advanced examination of the theories, concepts, and strategies for managing technology policies while developing and implementing computer science projects. During this final course in the PhD-CS program, before starting the dissertation sequence, you will research and design an information technology policy strategy for software development and implementation. You will also evaluate ways to improve the competitive position of an organization, recommend coding control structures for data integrity and security, and plan ways to address business concerns regarding the adoption of new technologies.

The Pre-Candidacy Prospectus is intended to ensure students have mastered knowledge of their discipline prior to candidacy status and demonstrated the ability to design empirical research as an investigator before moving on to the dissertation research coursework. Students will demonstrate the ability to synthesize empirical, peer reviewed research to support all assignments in this course. The Pre-Candidacy Prospectus is completed only after all foundation, specialization, and research courses have been completed.

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.