PhD in Data Science - Anointed University Worldwide

About Course

Course Introduction

The PhD in Data Science program at Anointed University Worldwide is designed to equip students with advanced knowledge and skills in data analysis, machine learning, big data technologies, and data-driven decision-making. The program emphasises both theoretical foundations and practical applications, preparing graduates for leadership roles in academia, industry, and research.

Course Objectives

Upon completion of the PhD in Data Science program, students will be able to:

Demonstrate a comprehensive understanding of data science principles and methodologies.
Apply advanced statistical and machine learning techniques to solve complex data problems.
Conduct original research and contribute to the field of data science.
Communicate data-driven insights effectively to diverse audiences.
Address ethical considerations and data governance in data science practices.

Course Content

Foundations of Data Science

Overview of Data Science
Data Types and Structures
Data Collection and Cleaning Techniques

Statistical Methods for Data Analysis

Programming for Data Science

Machine Learning Fundamentals

Research Methodologies in Data Science

Advanced Machine Learning

Big Data Technologies

About Course

Course Content

Foundations of Data Science

Overview of Data Science

Data Types and Structures

Data Collection and Cleaning Techniques

Statistical Methods for Data Analysis

Descriptive and Inferential Statistics

Hypothesis Testing

Regression Analysis

Programming for Data Science

Introduction to Python/R for Data Science

Data Manipulation with Pandas

Data Visualisation Techniques

Machine Learning Fundamentals

Supervised vs. Unsupervised Learning

Key Algorithms (e.g., Decision Trees, SVM, K-Means)

Model Evaluation and Validation

Research Methodologies in Data Science

Research Design and Frameworks

Qualitative vs. Quantitative Research

Ethical Considerations in Data Research

Advanced Machine Learning

Ensemble Methods (e.g., Random Forests, Boosting)

Neural Networks and Deep Learning

Natural Language Processing (NLP)

Big Data Technologies

Introduction to Big Data Concepts

Tools and Frameworks (e.g., Hadoop, Spark)

Data Storage Solutions (e.g., NoSQL Databases)

.Data Mining Techniques

Clustering and Association Rule Learning

Anomaly Detection

Text Mining and Sentiment Analysis

Data Ethics and Governance

Data Privacy and Security

Ethical Use of Data

Regulatory Compliance (e.g., GDPR)

Seminar in Data Science

Current Trends and Research in Data Science

Guest Lectures from Industry Experts

Discussion and Networking Opportunities

Advanced Statistical Modelling

Bayesian Statistics

Time Series Analysis

Multivariate Analysis

Capstone Project I

Proposal Development

Literature Review

Initial Data Collection and Analysis

Data Visualisation and Communication

Advanced Visualisation Techniques (e.g., Tableau, D3.js)

Storytelling with Data

Presentation Skills for Data Scientists

Domain-Specific Applications of Data Science

Applications in Healthcare, Finance, Marketing, etc.

Case Studies and Real-World Applications

Collaborations with Industry Partners

Elective Course (Choose One)

Artificial Intelligence in Data Science

Geographic Information Systems (GIS)

Advanced Topics in Data Ethics

Capstone Project II

Completion of Capstone Project

Data Analysis and Interpretation

Final Report and Presentation

Dissertation Proposal Development

Developing a Research Proposal

Methodology and Framework

Proposal Defence

Dissertation Research

Conducting Original Research

Data Collection and Analysis

Writing and Preparing the Dissertation

Dissertation Research Continuation

Continued research and data analysis

Regular meetings with the dissertation advisor

Advanced Topics in Data Science

Current Research Trends

Emerging Technologies in Data Science