Doctor of Computer Science - Big Data Analytics

In today's data-driven industries, companies that develop the capacity to turn large volumes of data into useful knowledge can develop a competitive advantage. This is creating a new area of expertise and specialization for data scientists with the advanced skills to propose solutions to issues related to big data analytics.

The CTU Doctor of Computer Science with a concentration in Big Data Analytics is designed to develop thought leaders who have mastered the tools and techniques to analyze huge amounts of distributed, unstructured data in order to produce meaningful insight and automation for their respective organizations.

Relevant Institutional/Programmatic Accreditation
CTU is institutionally accredited by the Higher Learning Commission www.hlcomission.org

  • Classes start

  • Total Credits 

    96
  • Program Availbility

    Online

Program Details

Overview
Courses
Related Degrees
Tuition
Career Paths
  • Overview

    The Doctor of Computer Science—Big Data Analytics (DCS-BDA) at Colorado Technical University is designed to develop leaders, data analysts, and data scientists in the development and use of tools and techniques to analyze huge amounts of distributed, unstructured data in order to produce meaningful insight and automation for their respective organizations.

    Outcomes:

    • The program prepares the graduates to be knowledgeable consultants, academics, or professionals in their areas of expertise.
    • The program prepares the graduates to be thought leaders in their field in academia or industry.
    • The program prepares the graduates to be scholars who are able to contribute to the body of knowledge.

    Each of the three years of the DCS program is designed to provide candidates with theoretical, research, and application capabilities in the field. The organization of each year is described below.

    Year 1: Foundations
    Year one focuses 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.

    Year 2: Acquisition of Knowledge
    Once the foundations are in place, year two is where each student develops an in-depth understanding of the knowledge and research methods in his or her chosen area of study. While most of the effort in year two is on developing a richer understanding of the discipline, the research courses include quantitative methods and the dissertation process.

    Year 3: Leadership and Professional Advancement
    Coursework in the final year of the program includes the two remaining concentration courses plus the final six doctoral research courses that enable one to complete the research and dissertation.

    The program thus includes fifteen instructional courses, plus nine doctoral research courses. Each class is conducted online.

    Symposium
    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 the catalog.

    Graduation Requirements
    In addition to the successful completion of the above 96 credits with an acceptable GPA, students must also satisfactorily complete and defend their research proposal and final dissertation. The research proposal must be approved by the student's committee, consisting of a mentor and two readers. The dissertation is an extensive document that incorporates the literature review, a major study, and a proposal for further investigation. The dissertation must be approved by the student's committee.

    Degree Completion and Post Doctoral Study
    The student must be continuously enrolled until all graduation requirements are fulfilled. A student who has not completed the research requirements by the end of the formal coursework continues by registering for RES893 Research Continuation according to CTU’s re-take policy.

    The Doctoral Advantage
    While a relevant master’s degree is ordinarily required for admission to CTU doctoral programs, there is also the option of completing a CTU MSCS, MSIT, MSM-ISS, MSM-IT/PM, or MSSE degree while starting work on the Doctor of Computer Science degree. The program outcomes remain the same for the DCS and the master’s degrees under this option, but the normal completion time for the degrees in the combined program is reduced. Through this program, doctoral work is started after ten of the twelve required master’s courses have been successfully completed. Program plans must be approved by the Dean of Doctoral Computer Science.

  • Courses

    Degree Requirements

    Courses: Core
    CS814 Current Topics in Computer Science and Information Systems

    4

    CS828 Advanced Topics in Database Systems

    4

    CS844 Concurrent and Distributed Systems

    4

    CS857 Business Intelligence

    4

    CS870 Advanced Quantitative Analysis

    4

    CS872 Introduction to Big Data Analytics

    4

    CS874 Advanced Topics in Big Data Analytics

    4

    CS875 Futuring and Innovations

    4

    CS876 Analytics for Big Data

    4

    CS878 Tools for Big Data Analytics

    4

    RES804 Principles of Research Methods and Design

    4

    RES812 Qualitative Research Methods

    4

    RES814 Quantitative Research Methods

    4

    RES860 Doctoral Research I: Principles of Research and Writing

    4

    RES861 Doctoral Research II: Annotated Bibliography

    4

    RES862 Dissertation Research Process

    4

    RES863 Doctoral Research III: Dissertation Literature Review

    4

    RES864 Doctoral Research IV: Dissertation Methods

    4

    RES865 Doctoral Research V: Dissertation Introduction

    4

    RES866 Doctoral Research VI: Dissertation Findings

    4

    RES867 Doctoral Research VII: Dissertation Discussion and Conclusion

    4

    RES868 Doctoral Research VIII: Dissertation Conclusion

    4

    Electives Select two 4- credit courses from 800-level CS or EM or EIS courses

    8

    TOTAL CREDIT HOURS: 96
    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.

    CS814 Current Topics in Computer Science and Information Systems

    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.

    Prerequisites

    None

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science
    ×

    CS828 Advanced Topics in Database Systems

    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.

    Prerequisites

    None

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science
    ×

    CS844 Concurrent and Distributed Systems

    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.

    Prerequisites

    None

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science
    ×

    CS857 Business Intelligence

    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.

    Prerequisites

    None

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science
    ×

    CS870 Advanced Quantitative Analysis

    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.

    Prerequisites

    CS812

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science/Engineering/Information Technology
    ×

    CS872 Introduction to Big Data Analytics

    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.

    Prerequisites

    None

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science/Engineering/Information Technology
    ×

    CS874 Advanced Topics in Big Data Analytics

    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.

    Prerequisites

    CS872

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science/Engineering/Information Technology
    ×

    CS875 Futuring and Innovation

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

    Prerequisites

    None

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science
    ×

    CS876 Analytics for Big Data

    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.

    Prerequisites

    CS872

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science/Engineering/Information Technology
    ×

    CS878 Tools for Big Data Analytics

    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.

    Prerequisites

    CS872

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science/Engineering/Information Technology
    ×

    RES804 Principles of Research Methods and Design

    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.

    Prerequisites

    None

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science
    ×

    RES812 Qualitative Research Methods

    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.

    Prerequisites

    None

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science
    ×

    RES814 Quantitative Research Methods

    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.

    Prerequisites

    None

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science
    ×

    RES860 Doctoral Research I: Principles of Research and Writing

    RES860 is the first 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, and produces a written document that shows adequate progress toward completion of dissertation research.

    Prerequisites

    None

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science
    ×

    RES861 Doctoral Research II: Annotated Bibliography

    RES861 is the second 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, and produces a written document that shows adequate progress toward completion of dissertation research.

    Prerequisites

    RES860

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science
    ×

    RES862 Dissertation Research Process

    This course presents doctoral students to the dissertation research process and applies relevant integrative understanding of complementary disciplines. It examines in depth the research process and introduces doctoral candidates to the various aspects of conducting valid research. Topics in this course include: hypothesis formulization, designing a literature review, conniving data collection techniques, ethical issues in research, and dissertation research design.

    Prerequisites

    None

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science
    ×

    RES863 Doctoral Research III: Dissertation Literature Review

    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.

    Prerequisites

    None

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science
    ×

    RES864 Doctoral Research IV: Dissertation Methods

    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.

    Prerequisites

    None

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science
    ×

    RES865 Doctoral Research V: Dissertation Introduction

    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.

    Prerequisites

    RES864

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science
    ×

    RES866 Doctoral Research VI: Dissertation Findings

    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.

    Prerequisites

    RES865

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science
    ×

    RES867 Doctoral Research VII: Dissertation Discussion and Conclusion

    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.

    Prerequisites

    RES866

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science
    ×

    RES868 Doctoral Research VIII: Dissertation Conclusion

    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.

    Prerequisites

    RES867

    Corequisites

    None

    Credits

    4

    Distribution

    Computer Science
    ×
  • Related Degrees

    Big Data Analytics is just one of the industry-relevant concentrations CTU offers in the Doctor of Computer Science degree program. Choose the option below that best supports your educational goals.

  • Tuition

    $57,408 Tuition

    $4,000 Symposium Fee*
    $200 Graduation Fee

    We understand that paying for your education is an investment in your future. Visit our tuition resources page for links to full tuition, books and fees.

    Cost of this degree may be reduced based on one or more of the following:

    *A $1,000 non-refundable fee is charged to a student’s account each quarter in which a student is registered for symposium (attendance at four symposia are required as part of the degree). This fee covers administrative costs such as conference rooms, AV equipment, academic event materials and supplies that are associated with the symposium event. Please see the Doctoral Symposium section of the catalog for more information.
    **Financial aid available for those who qualify

    Headset icon Contact an Admissions Advisor for additional information
  • Career Paths

    CTU's Doctor of Computer Science degree builds on the foundation of a master’s degree to prepare students for senior level leadership, consulting, and teaching positions within business, government, nonprofit organizations, and higher education. CTU doctoral students are educated to discover new solutions to unsolved problems in a range of fields. Students develop analytic and research skills to define problems, study advanced content knowledge to discover innovative solutions, and practice consulting and leadership techniques to facilitate innovative change in organizations, communities, and society. Using these skills, graduates may find opportunities as leaders within nonprofit organizations and businesses, as consultants, or as faculty within higher education.

    Headset icon Request more information now

Gainful Employment and Other Student Disclosures

Below you can find placement rates and other information tailored to your chosen campus and program. Certain disclosures are published on this website to assist students in understanding the facts about their programs.

Program Disclosure Information for:
Online Programs - Big Data Analytics

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Online Programs - Graduation Rate

The percentage of first-time, full-time undergraduate students who started between
7/1/2008 and 10/15/2008 who completed within 150% of the normal time period: 24%

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Graduation Rate

A first-time student has no prior postsecondary experience before enrolling at this campus. This means that a student who attended another college, university or other postsecondary school before enrolling at this school is not included in the calculation. The rate also does not include students initially enrolled part-time, taking individual classes (as compared to enrolling in a full program), or only auditing classes. These rates are calculated using the Student Right-to-Know formula in order to comply with U.S. Department of Education requirements. The statistics track all first-time, full-time and certificate or degree-seeking undergraduate students who began school during the date range and have completed within 150% of the normal program length. For example, for a two-year program, the graduation rate would include students who had completed within three years of beginning the program. This statistic is not specific to one program alone; rather, all applicable undergraduate programs are included in this overall rate. Information pertaining to the Graduation Rates of all postsecondary institutions recognized by the U.S. Department of Education may be found on the College Navigator website. http://nces.ed.gov/collegenavigator/

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