Online Doctorate Degree in Comp Sci - Big Data Analytics

Doctor of Computer Science - Big Data Analytics

As the importance of data continues to rise, the need for professionals who can structure and interpret it will be important. You can utilize 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.

100

Program Credits

10 Weeks

Course Length

Online Colorado Springs

Location

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Program Overview

Effective February 2019, this program is no longer available for future enrollments at the Denver South campus.

The Doctor of Computer Science with a concentration in Big Data Analytics (DCS-BDA) program includes a combination of core and elective courses, as well as doctoral symposium and research-related courses. The research element will culminate in a dissertation.

The DCS-BDA program is designed to aid leaders, data analysts, and data scientists in the development and use of tools and techniques to analyze large amounts of distributed, unstructured data in order to produce meaningful insight and automation for their respective organizations.

This program does not lead to additional licensure or certification. As such, CTU has made no determination regarding prerequisites for licensure or certification in any state or jurisdiction.

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Courses

Courses: Core		Credits
AI870 This 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
CS818 This course is designed to enable students to develop fluency in the concepts, principles, technologies, and applications of computer science and information technology disciplines . As such, its content will evolve over time and is expected to cover industry-current research developments of these disciplines.	Current Topics in Computer Science and Information Technology	4
CS834 This course is designed to enable students to build a more comprehensive understanding of advanced topics in database systems. Students may compare centralized database systems with distributed databases, examine the principles behind data warehousing and data mining, and evaluate the major literature sources that address the issues and trends in data management. Additionally, the course will assess current trends and contemporary changes in the field of database systems. Students may also evaluate database theory and practice.	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
RSCH860 The 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
RSCH861 This 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
RSCH862 This 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
SYMP801 Doctoral 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
SYMP802 Doctoral 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

Electives: DCS students must complete two 4-credit courses for these electives. These courses may be selected from any of those offered under DCS. One of those electives may be chosen from the Doctor of Management program instead.

Total Credit Hours: 100

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Learning Outcomes

Doctor of Computer Science Outcomes

Assess the evolution of knowledge within the chosen computer science discipline in addressing a technical real-world technical problem.
Contribute to the body of knowledge within the computer science industry through ethical research, scholarly writing, dissemination of research and real-world innovations in evolving, diverse environments.
Develop analytical and critical thinking proficiencies that clearly articulate applying computer science principles and models in solving real-world technical problems.

Big Data Analytics Concentration Outcomes

Assess advanced big data analytical tools to address industrial problems.
Apply tested, advanced big data analytic tools for organizational decision-making.
Contribute to the body of knowledge by enhancing big data tools and models in support of the mission of an organization.

Faculty

Dr. Yanzhen Qu

University Dean

Dr. Yanzhen Qu is the University Dean and Professor at the College of Computer Science, Engineering and Technology at Colorado Technical University (CTU). During his tenure at CTU, Dr. Qu has taken a leadership role in modernizing the curricula of CTU’s CS, IT and Cybersecurity degree programs, ranging from Associate to Doctoral levels.

Dr. Richard Cai

Executive Program Director

Dr. Richard Cai is CTU’s Executive Program Director for the College of Computer Science, Engineering & Technology. In this role, Dr. Cai supervises curriculum design, development and revision for the degree programs offered in the college. He also plays a key role in the program assessment and accreditation.

For more faculty profiles please visit our Leadership and Faculty page.
For more information visit Security Studies.

Admission Requirements

Provisional Requirements

Prior to starting the DCS program, all applicants must submit an unofficial or official copy of their college transcripts of their bachelor’s and master’s degrees for review. Applicants who hold a master’s degree in a field other than computer science may be granted provisional enrollment into their program, based on a review by the College of Computer Science,Engineering and Technology. These students must first complete up to four (4) college-level preparatory courses in the following subject areas: discrete mathematics, high-level programming languages, data structures, and algorithms.

Courses: Preparatory

CS219	Python Programming	4
CS233	Fundamentals of Data Structures	4
CS267	Computer Algorithms	4
MAT212	Fundamentals of Discrete Mathematics	4

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.

Foundations

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.

Symposium

Doctor of Computer Science (DCS) students are required to attend a symposium even two times during their enrollment in the program. Additional information about CTU's doctoral symposium can be viewed in the Doctoral Symposium section of this catalog.

Graduation Requirements

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.

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FAQs

What is a Doctor of Computer Science (DCS) with a concentration in Big Data Analytics degree?

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.

What will I study in this DCS degree program?

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>

When and where can I begin my big data analytics classes?

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.

How many credits are required to graduate from the doctoral degree program?

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.

Why should I pursue a DCS degree at CTU?

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.

Are there any requirements before graduating with a Doctor of Computer Science with a concentration in Big Data Analytics at CTU?

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.