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Doctorate in Computer Science

Doctorate in Computer Science

Program Overview

A Doctorate in Computer Science (D.CS) focuses on deepening knowledge in various areas of computer science through original research and innovation. It covers a wide range of topics, including artificial intelligence, machine learning, data science, cybersecurity, software engineering, and networking. Students engage in cutting-edge research, contributing to advancements in both theory and practice. With the rapid pace of technological innovation, a Doctorate in Computer Science offers opportunities to shape the future of digital transformation across industries. Graduates are equipped with the skills necessary for careers in academia, research labs, and the tech industry, where they lead developments in emerging technologies, design complex systems, and solve critical problems.

Why Pursue a Doctorate in Computer Science (D.CS) from FLISM?

The Doctorate in Computer Science at Florida Institute of Science and Management is designed for professionals, researchers, and innovators looking to push the boundaries of technology, AI, cybersecurity, and data science. This advanced research program provides a platform to explore cutting-edge developments, create industry-disrupting solutions, and contribute to global technological advancements. With a focus on practical application and interdisciplinary research, this doctorate equips scholars with the expertise needed to lead in academia, industry, and policy-making. The program at FLISM is designed to enable professionals in leadership roles to think analytically and build problem-solving abilities alongside developing applied research skills.

Key Highlights

100% Online Program

100% Online Program

Get the flexibility to complete your education along with your work commitments.
24*7 Access

24*7 Access

State-of-the-art and advanced Learning Management System for enhanced self-learning
Comprehensive Curriculum

Comprehensive Curriculum

Curriculum developed by Industry Experts and world-class faculty members
Networking Opportunities

Networking Opportunities

Gain global networking opportunity

Program Objectives

  • Conduct high-impact research in artificial intelligence, blockchain, cybersecurity, cloud computing, and big data.
  • Develop new computational models, algorithms, and AI-driven solutions for real-world applications.
  • Publish research in top-tier journals, international conferences, and industry forums.
  • Apply computer science principles to finance, healthcare, business, smart cities, and sustainability.
  • Utilize data-driven insights, machine learning, and deep learning to optimize decision-making.
  • Address global cybersecurity threats through advanced encryption, digital forensics, and risk mitigation strategies.
  • Contribute to data privacy regulations, ethical AI frameworks, and responsible computing.

Curriculum

Research

  1. Scope and Significance
  2. Types of Research
  3. Research Process
  4. Characteristics of Good Research
  5. Identifying Research Problem
  6. Meaning of Sampling Design
  7. Steps in sampling
  8. Criteria for good sample design
  9. Types of Sample Design
  10. Probability and non-probability sampling methods
  11. Meaning of Measurement
  12. Types of scales

 

Review of Literature

  1. Data Collection
  2. Types of Data
  3. Sources of Data Collection
  4. Methods of Data Collection
  5. Constructing questionnaire
  6. Establishing, reliability and validity
  7. Data processing
  8. Coding, Editing and tabulation of data
  9. Meaning of Report writing
  10. Types of Report
  11. Steps of report writing
  12. Precautions for writing report
  13. Norms for using Tables
  14. Charts and diagram
  15. Appendix: – Index, Bibliography
  • Meaning and importance of Research
  • Types of Research
  • Selection and formulation of Research Problem
  • Meaning of Research Design
  • Need of Research Design
  • Features of Research Design
  • Inductive, Deductive and Development of models
  • Developing a Research Plan
  • Exploration, Description, Diagnosis, Experimentation
  • Determining Experimental and Sample Designs
  • Analysis of Literature Review
  • Primary and Secondary Sources
  • Web sources
  • Critical Literature Review
  • Hypothesis
  • Different Types of Hypothesis
  • Significance
  • Development of Working Hypothesis
  • Null hypothesis
  • Research Methods: Scientific method vs Arbitrary Method
  • Logical Scientific Methods: Deductive, Inductive, Deductive-Inductive
  • Pattern of Deductive
  • Inductive logical process
  • Different types of inductive logical methods.

Introduction to Quantitative Research

 Part 1:

a. Session Overview
b. RQ Hypothesis Course Context Video
c. What is Quantitative Research?
d. Ethics of Quantitative Research
e. Session Summary

Part 2:

f. Session Overview
g. Introduction to the Scientific Method of Research
h. Comparing Descriptive, Predictive and Prescriptive Research
i. Inductive and Deductive Approaches to Quantitative Research
j. Constructing Models
K. Session Summary

Exploring Quantitative Research Design

 Part 1:

a. Session Overview
b. Fundamentals of Research Design
c. Components of a Research Design
d. Characteristics of a Research Design
e. Session Summary

Part 2:

f. Session Overview
g. Research Design for Experimental Research Studies
h. Research Design for Quasi Experimental Studies
i. Research Design for Non-Experimental Research Studies
j. Evaluating Quantitative Research Design
k. Session Summary

Data Collection for Quantitative Research

 Part 1:

a. Session Overview
b. Defining Surveys
c. Exploring Survey Methods
d. Session Summary
Part 2:

e. Session Overview
f. The Process of Questionnaire Development
g. Designing a Questionnaire
h. Designing Rating Scales
i. The Art of Asking Questions
j. Session Summary

Part 3:

k. Session Overview
l. Tips to Conduct Effective Surveys
m. Ethics of Using Technology in Surveys
n. Session Summary

Measurement and Sampling

Part 1:

a. Session Overview
b. What is measurement?
c. True Score Theory, Estimating Measurement Errors
d. Evaluating Validity of Measures
e. Evaluating Reliability of Measures
f. Session Summary

Part 2:

g. Session Overview
i. Basic Concepts of Sampling
j. Problems and Blases in Sampling
k. Probability Sampling
l. Non-Probability Sampling
m. Session Summary

Part 3:

n. Session Overview
o. Determining the Sample Size
p. Sampling Distribution and Statistical Inference
q. Demonstrations on Sampling
r. Session Summary

Constructing Statistical Models   
Part 1:

a. Session Overview
b. Significance of Comparing Means for Analysis
c. What is ANOVA?
d. Types of ANOVA
e. Calculating and Interpreting One-Way ANOVA
f. Session Summary

Part 2:

g. Session Overview
h. Building a Statistical Model
i. Effect of Moderating and Mediating Variables
j. Demonstration on Mediation and Moderation
k. Session Summary

Enhancing Statistical Models
Part 1:

a. Session Overview
b. What is Factor Analysis?
c. Conducting Factor Analysis
d. Demonstration on R: Factor Analysis
e. Interpreting Factor Scores
f. Session Summary

Part 2:

g. Session Overview
h. What is Factorial ANOVA?
i. Dealing with Interaction Effects in Factorial ANOVA
j. Calculating and Interpreting Factorial ANOVA
k. Session Summary

Multivariate Analyses
Part 1:

a. Session Overview
b. Multivariate regression
c. MANOVA
d. Logistic Regression
e. Structural Equation Modeling
f. Tree Structured Methods
g. Conjoint Analysis
h. Session Summary

Part 2:

i. Session Overview
j. Time Series
k. Cluster Analysis
l. Session Summary

Writing a Quantitative Research Paper
Part 1:

a. Session Overview
b. Introduction to Formatting the Research Project for Quantitative Research
c. Components of a Quantitative Research Paper
d. Writing the Summary, Background and Purpose of Quantitative Research
e. Writing the Literature Review
f. Detailing your Research Design/Methodology
g. Curating your Results, Analysis and Supplementary Findings
h. Outlining your Conclusions and Recommendations
i. Making Appendices
j. Session Summary

Part 2:

k. Session Overview
l. Writing Different Types of Quant Papers
m. Guidelines for Fine-Tuning your Research Presentation
n. Session Summary

Introduction to Qualitative Research

a .Key Elements of Qualitative Research
b. Writing Qualitative Research Question
c. Qualitative Research: Framework
d. Steps to Write a Qualitative Research Paper
e. Ethics for Qualitative Research and IRB
f. Introduction to Design Strategies
g. Data Collection and Analysis Strategies
h. Introduction to research design
i. Major aspects of research design

Data Collection in Qualitative Research

  1. Sources of Evidence: A Comparative
    b. Assessment (Forms-Strengths-Weaknesses)
    c. Principles of Data Collection
    d. Sampling
    e. Reliability and Validity
  • Interviews and Focus Groups
  • Introduction to Data Analysis
  •   An Introduction to Data Analysis
    b. First Cycle Coding (Description Demo)
    c. Second Cycle Coding (Description Demo)
    d. Jottings and Analytic Memoing (Description Demo)
    e. Assertions and Propositions (Description Demo)
    f. Within Case and Cross-Case Analysis (Description Demo)

Data Display and Exploration

  1. Matrix and Networks
    b. Timing, formatting
    c. Extracting Inferences and Conclusions
    d. Exploring Fieldwork in Progress
    e. Exploring Variables
    f. Exploring Reports in Progress

Data Analysis Process – Next Steps

  1. Describing Participants
    b. Describing Variability
    c. Describing Action
    d. Ordering by time
    e. Ordering by process
    f. Explaining Interrelationship-Change
    g. Explaining Causation
    h. Making Predictions

Verifying Conclusions

  1. Tactics to achieve integration among diverse pieces of data
    b. Tactics to sharpen understanding by differentiation
    c. Tactics of seeing relationships in data abstractly
    d. Tactics to assemble a coherent understanding of data
    e. Tactics for testing or confirming findings
    f. Standards for quality of conclusions

Writing a Report and New Technologies

  1. Other methods in Qualitative Research
    b. Audiences and Effects
    c. Different aspects / apa
    d. An Introduction to Mixed Methods Research

Frequently Asked Questions

Graduates can pursue careers as research scientists, professors, or senior positions in technology companies. Opportunities exist in both academia and industry, including roles in AI development, cybersecurity, software engineering, and data science.

Students benefit from industry collaborations and a global network of research professionals. A doctorate in Computer Science offers cutting-edge research opportunities, with access to advanced facilities, renowned faculty members, and a strong emphasis on practical applications of computer science theories. 

The Doctorate in Computer Science at Florida Institute of Science and Management focuses on pioneering research that leads to technological breakthroughs. You’ll work on cutting-edge topics like AI, cybersecurity, and cloud computing, enabling you to contribute new knowledge and solutions to the field.

Yes, Florida Institute of Science and Management offers flexible learning options, including an online format to cater to working professionals and students with other commitments.