Doctorate in Big Data Analytics Online
Doctor of Computer Science - Big Data Analytics
As the importance of data continues to rise, the need for true experts who can structure and interpret it will be important. You can leverage your experience and develop expertise with a Doctor of Computer Science degree in Big Data Analytics from Colorado Technical University. In this program, you can study tools, like XML and Hadoop, and techniques, like AI and data visualization, to analyze huge amounts of distributed, unstructured data in order to produce meaningful insights.
Our doctoral program includes:
- An online curriculum with a residency component
- Multiple start dates throughout the year
- An opportunity to complete the program in three years
- Dissertation development integrated into the program
- In-person symposium experiences
At CTU, students come first. Our flexible online course schedule helps you to build a class schedule around your schedule. And with grants and scholarships available for those who qualify, a degree from CTU can be both achievable and affordable. Learn more below or fill out the form to speak with an admissions advisor.
Relevant Institutional/Programmatic Accreditation
CTU is institutionally accredited by the Higher Learning Commission www.hlcommission.org
- January 02, 2024
- February 06, 2024
- March 19, 2024
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Total tuition for this degree program will vary depending on your educational needs, existing experience, and other factors.Estimate your costs, potential savings and graduation date
Course Title Course Description Credit Hours AI870This course is designed for students to develop fluency in the history, current status, and future of Artificial Intelligence (AI), as well as the concepts, technologies, and applications of AI. The course also focuses on assessing the impact of AI in solving problems in business and society. The topics include analyzing the principles of applying AI in real world problem-solving; evaluating the suitability of a business application for a specific AI technique such as machine learning, neural networks and deep learning, gaming, or robotics; and developing a suitable AI strategy and AI ethics for an organization or business to gain competitive advantage. Artificial Intelligence in Real World Problem Solving 4 CS814
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.
Current Topics in Computer Science and Information Systems 4 CS828
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.
Advanced Topics in Database Systems 4 CS857
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.
Business Intelligence 4 CS871
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.
Advanced Quantitative Analysis 4 CS875
Develops the skills in futuring through a variety of techniques. Introduces formal methods of innovation and diffusion of innovation.
Futuring and Innovation 4 CS877
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 to improve or propose a solution to any subject related to big data analytics is also provided.
Introduction to Big Data Analytics 4 CS879
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.
Advanced Topics in Big Data Analytics 4 CS881
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.
Analytics for Big Data 4 CS882
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.
Tools for Big Data Analytics 4 RES804
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.
Principles of Research Methods and Design 4 RES812
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.
Qualitative Research Methods 4 RES814
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.
Quantitative Research Methods 4 RES863
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.
Doctoral Research III: Dissertation Literature Review 4 RES864
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.
Doctoral Research IV: Dissertation Methods 4 RES865
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.
Doctoral Research V: Dissertation Introduction 4 RES866
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.
Doctoral Research VI: Dissertation Findings 4 RES867
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.
Doctoral Research VII: Dissertation Discussion and Conclusion 4 RES868
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.
Doctoral Research VIII: Dissertation Conclusion 4 RSCH860The course is designed to help students develop as scholar‐practitioners through research and writing activities required for the dissertation. Students will be introduced to doctoral level evidence‐based research and writing skills, critical thinking, ethics in research, and the development of an annotated bibliography. Doctoral Research I: Principles of Research and Writing 4 RSCH861This is the first of the research and writing courses that together comprise the dissertation process. Each term, the student advances on the dissertation pathway by completing required components of the dissertation. The focus of this course is on the development of the research prospectus, which is the first official milestone in the dissertation process. Satisfactory completion of the research prospectus is required to move forward to the next step on the dissertation pathway. Students are also encouraged to begin work on next steps in the dissertation process as appropriate. Dissertation Process I 4 RSCH862This course focuses on development of a literature review in the context of a dissertation topic aligned with the student's degree field, concentration, and personal interests; is consistent with the study trio; and is both scholarship‐based and practice‐oriented. As part of developing the literature review, the course also addresses creation of the conceptual framework and consideration of the research tradition appropriate for the proposed topic. To pass this course the student must produce a literature review draft satisfactory for movement forward to the next course in the dissertation process sequence. Students are also encouraged to begin work on next steps in the dissertation process as appropriate. Dissertation Process II 4 Select two 4- credit courses from 800-level CS or EM or EIS courses 8 SYMP801Doctoral Symposium I provides first-year doctoral students with activities designed to develop foundational skills for doctoral study and an orientation to the doctoral dissertation process. Students participate in both the Doctoral Symposium and online classroom activities in the second term of their first year of study.
Course work is designed to help students prepare to progress through the next year of the doctoral program. Successful completion of all specified activities and requirements of the Doctoral Symposium and assigned deliverables are required to pass this course.
Doctoral Symposium I 2 SYMP802Doctoral Symposium II provides second or third year doctoral students with activities that help them prepare for completion of the dissertation. Students participate in both the Doctoral Symposium and online course work. The course is designed to prepare students to attend Symposium and to progress to completion of the dissertation. Successful completion of all specified activities and requirements of the Doctoral Symposium and assigned deliverables are required to pass this course. Doctoral Symposium II 2
Total Credit Hours: 100
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Program Areas of Focus
The DCS program is designed to provide candidates with theoretical, research, and application capabilities in the field. The areas of focus are described below.
The program provides a focus on computer science and information systems topics and an orientation to research and writing at the doctoral level. Coursework covers current topics in the disciplines as well as research methods and qualitative techniques. The research component results in a broad overview of the student’s area of concentration in order to put the research into context and inform the student’s selection of a research topic.
Acquisition of Knowledge
Once the foundations are in place, the focus is on student development of an in-depth understanding of the knowledge and research methods in his or her chosen area of study. While most of the focus is on developing a richer understanding of the discipline, the research courses include quantitative methods and the dissertation process.
Leadership and Professional Advancement
The program includes the two remaining concentration courses plus the final six doctoral research courses that are designed to help students to complete the research and dissertation.
Doctoral programs at Colorado Technical University require a residential symposium. Additional information about CTU's doctoral symposium can be viewed in the Doctoral Symposium section of this catalog.
In addition to the successful completion of the above 100 credits with an acceptable GPA, students must also satisfactorily complete their research proposal and final dissertation. The research proposal must be approved by the student’s Research Supervisor and University Reviewer. The dissertation, which must be approved by the student’s dissertation committee, is an extensive document that includes the research study. In addition, graduation requires presentation of the final dissertation.
Cost of this degree may be reduced based on one or more of the following:
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Program details are provided lower on the page.
Classes start January 2, 2024 *
*Start dates may vary by program and location.
CTU’s Doctor of Computer Science (DCS) with a concentration in Big Data Analytics Degree Program is designed to provide candidates with theoretical, research, and application capabilities in the field. The program’s coursework provides a focus on computer science and information systems topics including those related to big data analytics, as well as an orientation to research and writing at the doctoral level. Coursework may cover current topics in the disciplines as well as various types of research methods.
As you work to complete your DCS with a concentration in Big Data Analytics, you will be immersed in courses where you will study these topics and much more: analytical techniques for big data; quantitative and qualitative methods within the context of research designs; and uses tools including artificial intelligence and machine learning for big data analytics.p>
Courses for the DCS with a concentration in Big Data Analytics degree program start online approximately every five weeks. Completion of the CTU admissions process will depend on how quickly you complete the steps in the CTU online application process. You may complete the application process over the phone with an advisor or you may go online. Once you’ve completed the online application, you may hear from an advisor within the following 24 hours to discuss the next steps toward starting your degree program. Master’s programs may have additional entrance requirements that take additional processing time.
The DCS with a concentration in Big Data Analytics degree program consists of 100 credits. You may be eligible for transfer credit, which is evaluated on an individual basis.
As you study topics in computer science and data analytics that are always being evaluated and updated to reflect industry-relevant trends, you will experience a curriculum through classroom learning and hands-on experience that aligns to industry standards and helps you work to develop skills that are applicable to the needs of the digital economy.
In addition to the successful completion of the program’s 100 credits with an acceptable GPA, students must also satisfactorily complete their research proposal and final dissertation. The research proposal must be approved by the student’s research supervisor and university reviewer. The dissertation, which must be approved by the student’s dissertation committee, is an extensive document that includes the research study. Graduation also requires students’ presentation of the final dissertation.