Doctorate in Big Data Analytics Online

This course provides an overview and introduction to the breadth of research in the disciplines of computer science and information systems.  As such, its content will evolve over time and is expected to cover research developments at the leading edge of these disciplines.

Computer Science is dynamic; Moore’s Law tells us that today’s standard could very well be obsolete in 18 months.  This course addresses the top three issues of current database theory and practice, identifying current trends and near future changes in the field.  As such, the course content will vary according to the evolution of the discipline.  Students will research major literature sources that address issues and trends, compare and contrast centralized database systems with distributed databases and identify principles behind database warehousing and data mining.

This course covers the fundamentals of concurrent and distributed systems including threading, synchronization and deadlock prevention as well as logical clocks, and group communication. It also covers current topics such as software for multiprocessors and multicore processors.

This course presents decision making frameworks, their advantages and limitations. Topics include constructing a data warehouse and its use for data mining in order to do trend analysis; the development and protection of business intelligence; and knowledge management within an enterprise. These topics will lead a student to appreciate the value of the knowledge contained in the data gathered by an organization and its impact on the business.

This course builds on the foundation of quantitative analysis by delving into advanced techniques associated with operations to be performed on massive amounts of data rather than on samples. Topics include correlation, prediction, confidence intervals, and regression analysis. Methods used in operations research are addressed.

Develops the skills in futuring through a variety of techniques. Introduces formal methods of innovation and diffusion of innovation.

This course introduces the subject of big data analytics, emphasizing how scale affects the very nature of the problem. Conventional computing technologies, methods and models are recast in light of that fact. The nature of common instances of unstructured data is explored including; the roles of various tools used for capturing, storing, searching, processing, sharing, managing, displaying and analyzing such a massive amount of data. Remote sensor data and transactional data are examined. The tools of big data analytics are used. The opportunity for students innovatively to improve or propose a solution to any subject related to big data analytics is also provided.

This course addresses advanced topics in big data analytics, particularly those that emphasize performance. The architecture of the World Wide Web is examined in light of performance bottlenecks. Techniques to mitigate those, such as in-memory data and massively parallel computation, are covered. The use of big data analytics to help defend the computing infrastructure of an organization from security threats is explored.

This course covers analytical techniques for big data. A review of applied statistics is included. Then methods of prediction are incorporated. The particulars of health informatics are addressed to include diagnostics and detection of fraud. Case studies are used to illustrate the wide range of big data analytics in use today and in the past.

This course addresses and uses tools for big data analytics. The use of XML for transactional and sensor data is examined and exercised. Hadoop and related tools are used to determine the nature of large amounts of unstructured data. The role of artificial intelligence is explored. Techniques for visualization of big data and its analyses are reviewed and exercised.

This course provides a general understanding of both quantitative and qualitative methods within the context of research designs. Research design is the plan for the selection and application of accepted research practices. Research methods provide models for the appropriate collection, organization and analysis of data for decision-making, replication, and contribution to a knowledge base. Additionally, this course supports doctoral students’ abilities to demonstrate an understanding of the research purpose, nature and forms of research design and their relationship to research questions, methods for data collection and data analyses.

This course examines the fundamental principles of qualitative inquiry differentiating among various qualitative research designs.  Includes active engagement and practice with capturing qualitative data including being a participant observer and an interviewer. Students will learn how to minimize threats to the internal validity of qualitative studies, focusing on specific techniques for interpretation of data that contributes to the authenticity of qualitative studies.

Students will learn fundamental concepts of designing, collecting and assessing quantitative data. The course covers descriptive measures as well as various forms of probability and inferential analysis. Exploration of multivariate statistics will be practiced via large datasets using statistical analysis software.

RES863 is the third course of eight research and writing courses that result in a dissertation. Each term, the student progresses toward the completion of the dissertation by completing required elements of the dissertation process. This course: clarifies the research focus, defines the research question(s)/objective/hypotheses, produces a review of the literature.

RES864 is the fourth course of eight research and writing courses that result in a dissertation. Each term, the student progresses toward the completion of the dissertation by completing required elements of the dissertation process. This course requires: fine tuning the research question(s)/objective/hypotheses, strengthening the review of the literature, drafting a methods chapter (min), and drafting a chapter one. Students may surpass this description as they are able.

RES865 is the fifth course of eight research and writing courses that result in a dissertation. Each term, the student progresses toward the completion of the dissertation by completing required elements of the dissertation process. This course requires the student to focus on: producing a defense-ready draft of Chapters 1, 2 & 3 (the research proposal), undertaking the Proposal Defense, undertaking modifications required by the dissertation committee, achieving an approved IRB application.

RES866 is the sixth course of eight research and writing courses that result in a dissertation. Each term, the student progresses toward the completion of the dissertation by completing required elements of the dissertation process. This course requires the student to focus on: undertaking the Proposal Defense, undertaking modifications required by the dissertation committee, achieving an approved IRB application, proceeding with Data Collection and Analysis.

RES867 is the seventh course of eight research and writing courses that result in a dissertation. Each term, the student progresses toward the completion of the dissertation by completing required elements of the dissertation process. This course requires the student to focus on: proceeding with Data Collection and Analysis, working on initial drafting of chapters 4 & 5, and preparing for Final Defense.  Course is pass/fail.

RES868 is the eighth course of eight research and writing courses that result in a dissertation. Each term, the student progresses toward the completion of the dissertation by completing required elements of the dissertation process. This course requires the student to focus on: completing Data Collection and Analysis as needed, completing work on chapters 4 &5, undertaking the Final Defense, modifying document as required by the committee, editing of final document for publishing, and University sign off.  Course is pass/fail.