Earn a PhD from University of Fairfax
PhD in Computer Science and Engineering
A Program for Professionals, Taught by Professionals
The PhD in Computer Science and Engineering is a research-oriented degree designed to add and contribute knowledge in computer science and engineering. The courses and correlative course outcomes in the core of this program are designed with a high degree of measurement. Several core courses require organization, quantitative and qualitative analyses, data syntheses, and results in dissemination. The University of Fairfax has a long-established track record in offering distance-oriented Doctoral programs, and most of the faculty in this program possess a PhD from distance programs. They can thus provide the first-hand experience to this program.
Program Format
The sequential part of the PhD program (accommodating the qualifying / comprehensive exam, development of the dissertation concept paper, feasibility paper, and more) is divided into five phases, most of which have steps within each phase. These steps continue to measure attainment of research skills as the student slowly and methodically progresses through a lengthy series of “gates” and ultimately to the final defense.
- Phase 1: Identifying a Dissertation Topic
- Phase 2: Achieving Candidacy
- Phase 3: Conducting the Research
- Phase 4: Obtaining Dissertation Approval
- Phase 5: Publishing the Dissertation
In implementing a phased approach (sequential) to satisfying the dissertation itself, this program is much more supportive of getting students through the program. All activities in this part of the program are measurable and attainable via distance learning.
Program Outcomes
Upon completion of this degree program, graduates will be able to:
- Recommend the appropriate algorithms, programming languages, and architecture for a given problem.
- Formulate solutions with fundamental knowledge in several specialized areas of research and expertise.
- Develop independent and innovative solutions through research by applying reasoning, problem solving, and technical skills with minimal guidance.
- Prepare concepts and results for a technical audience in the form of conference papers, journal papers, and/or oral presentations.
- Develop qualitative and/or quantitative research to analyze critical technical issues.
Credit Requirements
The PhD in Computer Science degree program consists of 62 semester credits beyond a master’s degree.
Course Descriptions
Core Courses:
DS7000 Database Management & Implementation
(Prerequisite: RM8500) In this course, students will explore Database Concepts, Advanced Design and Implementation, Data modeling and the importance of Data models. This course also includes Best Practices in database design and management as well as usage of data specifications. (3 credits)
DS7100 Advanced Operating Systems
(Prerequisite: RM8500) In this course, students will examine the use of operating systems, their impact of improving areas such as memory management, process scheduling, file systems, and device drivers. This course will also examine the techniques and technologies of non-distributed operating systems that benefit researcher, academicians, and practitioners. (3 credits)
DS7200 Software Engineering & Development
(Prerequisite: RM8500) In this course, students will explore Software Engineering techniques and deepen their understanding of high-level languages and system programming. Some of the principles discussed include abstraction, algorithms, data structures, and web development. Students will also apply the methods and techniques for creating software systems using the best practices in modeling, architecture, process analysis, design, and object-orientated design patterns. This course will provide students with the principles and concepts involved in the analysis and design of large software systems. (3 credits)
DS7600 Big Data and Analytical Research Methods for Software Developers
(Prerequisite: RM8500) In this course students will learn Advanced Data Analysis techniques which are oftentimes associated with Data Mining. Students will evaluate various optimization and simulation models in an effort to determine which models are best suited for various markets. Students will conduct extensive analysis to determine relationships among variables within various environmental settings. (3 credits)
IA8021 Cloud Cybersecurity
In this course students will research and analyze virtualization technology needed in today’s rapidly changing IT workplace. The course will focus on virtualization in software-defined data centers. Students learn to build virtual networks, implement high-availability clusters, enhance performance and security, and manage the virtual data center. (3 credits)
IA9150 Strategic and Technological Trends in Information Security
In this course, students will focus on the managerial aspects of information security assurance. Topics covered include access control models, information security governance, and information security program assessment and metrics. Coverage on the foundational and technical components of information security is included to reinforce key concepts. The course includes up-to-date information on changes in the field, such as national and international laws and international standards like the ISO 2700 series. (3 credits)
DC7350 Advanced Research Methods in Computer Science and Engineering
During this course students will learn research skills necessary for conducting research in the field of Computer Science and Engineering. Some of the principles will include formulating research questions, data analysis, theory, and identification of various research methods. This course is designed for Computer Science students planning to conduct research that involves human interaction with computer technology, controlled experiments, action research, archival analysis, case studies, and surveys.
DC7450 Advanced Research Methods in Communications Networks
(Prerequisites: RM8500, CS6500) This course is designed for students interested in conducting research on advanced topics in Communications Networks. This course will also examine current and emerging research topics in communication networks. Topics covered include network measurements, internet routing peer to peer networks, network protocols, network security, wireless and sensor networks. Due to the rise in Cybersecurity, A significant portion of this course will focus on Security and Networking related issues.
DC7550 Advanced Research Methods in Parallel and Distributed Database Systems
(Prerequisites: RM8500, CS6600) This course covers algorithms and architectures necessary for parallel and distributed database management systems. While the main focus of this course is on relational systems, issues related to all large-scale database systems will also be addressed. Some of the areas examined will include MapReduce-based distributed data management, Parallel data management, distribution architectures, distribution design, distributed query processing and optimization.
DC7650 Advanced Research Methods in Very Large-Scale Integration Design
(Prerequisites: RM8500, DS7200) – This engineering related course is designed to help students understand the fabrication and design techniques associated in the design of Large-Scale Systems. Various topics will be introduced to include CMOS logic, MOSFET theory, design techniques, capacitance requirements, power consumption, performance estimation, effective circuit design, and clocking. This course will also cover the design of elementary data paths for microprocessors, including moderate-speed adders, and multipliers.
Pre-Dissertation:
RM8500 Research Foundations
In this course, doctoral students are introduced to the purpose and nature of primary research. Students explore the foundations and concepts of applied field research. (3 credits)
RM9100 Qualitative and Quantitative Analysis
(Prerequisite: Program Core Courses completed) In this course, students compare, contrast, and evaluate qualitative and quantitative methods of data analysis for solving business problems and conducting business-related field research. In week 4, the Comprehensive and qualifying exam is released as a separate course shell, IA9130. The Exam is expected to be completed concurrently while completing RM9100 and is due in week 8.
DC9130-CX Comprehensive Exam
Students complete the Comprehensive & Qualifying Examination in weeks 4-8 of the RM9100 courses. The DC9130-CX course is administered concurrently with RM9100.
Research Methodologies:
DC7700 Advanced Qualitative Methods in Computer Science Engineering
(Prerequisites: all program core/RM9100, DC9130-CX) This course is part of a two-course advanced research methodology sequence that is designed to assess the student’s ability to conduct independent research under the guidance of an instructor. These courses will assess the student’s ability to listen to the instructor and incorporate the instructor’s feedback. These courses will also assess the student’s ability to work productively with the instructor to accomplish the following goals including, but not limited to: choosing an appropriate a topic that aligns with the parameters set forth in the class syllabus; refining the topic; conducting the literature review; designing the study that that aligns with the parameters set forth in the class syllabus; collecting appropriate evidence; interpreting the findings; critically assessing/analyzing the evidence in relation to the problem under investigation and the research questions; critically assessing/analyzing the evidence in relation to the problem under investigation and the hypotheses (quantitative research); and writing scholarly doctoral-level research that adheres to APA guidelines. The assessment of the aforementioned personal attributes and skill-sets, in addition to the formal research knowledge and skill-sets under investigation in these two classes, are paramount to improving the student’s success later in the program when h/she is researching and writing his/her own, original dissertation project with his/her Chair.
DC7800 Advanced Quantitative Methods in Computer Science Engineering
This course is part of a two-course advanced research methodology sequence that is designed to assess the student’s ability to conduct independent research under the guidance of an instructor. These courses will assess the student’s ability to listen to the instructor and incorporate the instructor’s feedback. These courses will also assess the student’s ability to work productively with the instructor to accomplish the following goals including, but not limited to: choosing an appropriate a topic that aligns with the parameters set forth in the class syllabus; refining the topic; conducting the literature review; designing the study that that aligns with the parameters set forth in the class syllabus; collecting appropriate evidence; interpreting the findings; critically assessing/analyzing the evidence in relation to the problem under investigation and the research questions; critically assessing/analyzing the evidence in relation to the problem under investigation and the hypotheses (quantitative research); and writing scholarly doctoral-level research that adheres to APA guidelines. The assessment of the aforementioned personal attributes and skill-sets, in addition to the formal research knowledge and skill-sets under investigation in these two classes, are paramount to improving the student’s success later in the program when he/she is researching and writing his/her own, original dissertation project with his/her Chair.
Phase I
RM9150: Feasibility Problem Driven Research
(Prerequisite: Program Core, Pre-Dissertation and Research Methodologies completed) In this course, students identify a research site, describe a plan for access to the research site, identify a problem affecting the research site that can be developed into a feasible topic area for field research, and develop a working bibliography of recent and relevant peer-reviewed research that supports the theoretical framework of the proposed topic. Students apply the concept of problem-driven research as the basis for selecting a feasible and non-trivial research topic or problem.
DC9200 Designing Solutions to Computer Science Engineering Problems
Prerequisites: all program core, all pre-dissertation, RM9150
In this course, students continue to evaluate the feasibility of their proposed research site and the potential solutions to be studied. Students present their proposed project at the Dissertation Bootcamp at the end of this course.
Phase II
DC8110 Dissertation Proposal (Chapter 1)
This is the first course in Phase II of the doctoral plan. Phase II consists of the Research Preparation courses (DC8110, 8120, 8121, and 8130) in which doctoral students follow a structured approach to designing their dissertation study, refining their research question/s, and developing the operational details for their study. The focus is on clearly specifying the assessment criteria and organizational requirements needed to justify a proposed improvement in professional practice, and on designing and implementing such an assessment. The goal of the entire course sequence is to complete the dissertation proposal (Chapters 1, 2, 3 and 4.1). In this seminar, students will revisit the foundations of research methods and apply them to the modifications required for creating the required dissertation deliverables. DC8110 is the first course in which students start developing the dissertation proposal (Chapters 1-4.1). Concepts covered include: research problems, questions and hypotheses, data types, quantitative and qualitative approaches, research designs, variables and scales, data collection instruments, and sampling.
DC8120 Dissertation Proposal (Chapter 2)
This is the second course in Phase II of the doctoral plan. Phase II consists of the Research Preparation courses (DC8110, 8120, 8121, and 8130) in which doctoral students follow a structured approach to designing their dissertation study, refining their research question/s, and developing the operational details for their study. The focus is on clearly specifying the assessment criteria and organizational requirements needed to justify a proposed improvement in professional practice, and on designing and implementing such an assessment. The goal of the entire course sequence is to complete the dissertation proposal (Chapters 1, 2, 3 and 4.1). In this seminar, students will revisit the foundations of research methods and apply them to the modifications required for creating the required dissertation deliverables. DC8120 is the second course in which students continue developing the dissertation proposal (Chapters 1-4.1). Concepts covered include: research problems, questions and hypotheses, data types, quantitative and qualitative approaches, research designs, variables and scales, data collection instruments, and sampling.
DC8121 Dissertation Proposal (Chapter 2 Continued)
This is the second course in Phase II of the doctoral plan. Phase II consists of the Research Preparation courses (DC8110, 8120, 8121, and 8130) in which doctoral students follow a structured approach to designing their dissertation study, refining their research question/s, and developing the operational details for their study. The focus is on clearly specifying the assessment criteria and organizational requirements needed to justify a proposed improvement in professional practice, and on designing and implementing such an assessment. The goal of the entire course sequence is to complete the dissertation proposal (Chapters 1, 2, 3 and 4.1). In this seminar, students will revisit the foundations of research methods and apply them to the modifications required for creating the required dissertation deliverables. DC8120 is the second course in which students continue developing the dissertation proposal (Chapters 1-4.1). Concepts covered include: research problems, questions and hypotheses, data types, quantitative and qualitative approaches, research designs, variables and scales, data collection instruments, and sampling.
DC8130 The Dissertation Proposal (Chapters 3 and 4.1 & the IRB)
Prerequisites: all program core, all pre-dissertation, phase I, DC8110, DC8120, DC8121
Phase III
DC8700 Final Draft Dissertation
Dissertation writing
Phase IV
DC8800X Dissertation Defense (Dissertation Committee & DDR)
(Prerequisites: all program core, all pre-dissertation, phase I, phase II, phase III) In this course, candidates present their findings to the Dissertation Committee at the defense. (1 credit)
Phase V
Dissertation Printing and Binding

University of Fairfax incorporates
into the PhD in Computer Science & Engineering curriculum.
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Potential Careers for PhD in Computer Science and Engineering Graduates:
When you graduate with a PhD in Computer Science and Engineering at the University of Fairfax, you will be prepared for many exciting careers in this growing industry.
Whether you want to work as a Computer and Information Research Scientist at a major enterprise or teach at a university level, we will help you develop the skills to succeed in your chosen career.
Transfer Credits
A maximum of nine semester credits equivalent to our courses in content, credit and level and taken for graduate credit, as part of a degree or graduate certificate program, from an accredited institution may be transferred.
Accreditation Note
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The PhD in Computer Science and Engineering at the University of Fairfax is accredited by the Distance Education Accrediting Commission (DEAC). The University of Fairfax is one of a small group of accredited institutions requested by DEAC to participate in a pilot demonstration to the U.S. Department of Education (USDOE) that DEAC’s scope of accreditation should include recognition by USDOE of the PhD program level.