
Hands-On Learning
Apply generative AI to real research tasks with guided prompts, use cases, and interactive activities.

Research-Focused Instruction
Learn from in-depth, interdisciplinary examples rooted in academic and applied research practice.

Flexible, Self-Paced Study
Work through structured lessons at your own pace, with tools to support your current research projects.
About the Course
Our course on Generative AI for Data Analysis & Research Methodology is designed to help researchers use AI tools to support study design, interpret data, and improve methodological transparency. Whether you're developing hypotheses, analysing qualitative themes, or clarifying statistical concepts, this course offers practical guidance and research-focused prompts to help you use AI responsibly and effectively. Join us to build confidence, save time, and strengthen your research using generative AI—without compromising academic standards or ethical practice.

Meet Your Instructor
I'm Jonathan Wong, a Lead AI Scientist and researcher specialising in medical diagnostics, data science, and advanced research technologies. My work focuses on applying generative AI and machine learning to improve healthcare outcomes and support complex research workflows. In this course, I share practical tools, ethical strategies, and real-world insights developed through applied experience in research and development. Whether you're just beginning or looking to deepen your skills, you'll find structured guidance to help you use AI effectively, creatively, and responsibly in your research practice.
Curriculum
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1
Introduction
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5.1 Using AI for Research Design and Hypothesis Development
- 5.1.1 Generating Research Hypotheses Using AI-Driven Analysis of Existing Data Free preview
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5.1.2 Designing Robust Experimental Methodologies with the Help of AI Tools
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5.1.3 How AI Can Suggest Research Designs Based on Historical and Current Trends
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5.1.4 Refining and Adapting Research Questions Through AI Insights and Data Analysis
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5.1.5 Using AI for Statistical Modelling to Optimise Research Designs
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5.1.6 Key Takeaways
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5.1.A Practical Use Cases
- 5.1.B Learning Activities Free preview
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5.1.C Prompt Templates
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5.1.D Innovative Use Cases
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5.1.E Reflection & Discussion
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5.1.F Productivity Tips
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5.2 AI for Statistical Concept Explanation (Not Calculation)
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5.2.1 Using AI to Explain Complex Statistical Concepts for Researchers
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5.2.2 How Conversational AI Simplifies Advanced Statistical Theories for Better Comprehension
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5.2.3 Using AI to Provide Real-Time Feedback and Clarification on Statistical Methods
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5.2.4 AI for Teaching Statistical Concepts Interactively
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5.2.5 Addressing Gaps in Statistical Understanding with AI-Powered Explanations
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5.2.6 Key Takeaways
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5.2.7 Knowledge Check
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5.2.A Practical Use Cases
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5.2.B Learning Activities
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5.2.C Prompt Templates
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5.2.D Innovative Use Cases
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5.2.E Reflection & Discussion
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5.2.F Productivity Tips
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5.3 AI for Data Interpretation and Insight Generation
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5.3.1 Leveraging AI to Identify Patterns, Trends, and Correlations Within Data
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5.3.2 Using AI Tools to Generate Insights and Interpretations from Complex Research Data
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5.3.3 AI-Powered Tools for Improving Data-Driven Decision-Making in Research
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5.3.4 Enhancing Qualitative Analysis with AI’s Natural Language Processing Capabilities
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5.3.5 Using AI for Data Visualisation to Complement Insight Generation
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5.3.6 Key Takeaways
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5.3.7 Knowledge Check
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5.3.A Practical Use Cases
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5.3.B Learning Activities
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5.3.C Prompt Templates
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5.3.D Innovative Use Cases
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5.3.E Reflection & Discussion
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5.3.F Productivity Tips
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5.4 AI for Qualitative Data Thematic Analysis
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5.4.1 How Generative AI Automates Qualitative Data Analysis, Including Interviews and Surveys
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5.4.2 Identifying Key Themes and Insights in Qualitative Research Data Using AI
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5.4.3 Enhancing the Richness of Qualitative Data Analysis Through AI Assistance
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5.4.4 Using AI to Produce Deeper Insights from Qualitative Data That Might Be Overlooked Manually
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5.4.5 AI-Powered Thematic Analysis for Large-Scale Qualitative Research Projects
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5.4.6 Key Takeaways
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5.4.7 Knowledge Check
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5.4.A Practical Use Cases
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5.4.B Learning Activities
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5.4.C Prompt Templates
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5.4.D Innovative Use Cases
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5.4.E Reflection & Discussion
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5.4.F Productivity Tips
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5.5 AI’s Role in Research Transparency and Reproducibility
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5.5.1 Using AI Tools to Ensure Transparency in Research Methodologies and Data Analysis
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5.5.2 Automating the Documentation of Research Processes and Findings for Reproducibility
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5.5.3 Maintaining Consistency and Objectivity in AI-Assisted Research Workflows
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5.5.4 Enhancing the Transparency of Data Collection and Analysis through AI Tools
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5.5.5 How AI Can Aid in Tracking and Validating Research Processes for Replicability
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5.5.6 Key Takeaways
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5.5.7 Knowledge Check
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5.5.A Practical Use Cases
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5.5.B Learning Activities
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5.5.C Prompt Templates
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5.5.D Innovative Use Cases
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5.5.E Reflection & Discussion
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5.5.F Productivity Tips
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7
Final Lesson Message
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Course Complete: Share Your Feedback
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8
Feedback
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End of Course Survey
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Unlock Your Research Potential
Join our course today and unlock the full potential of Generative AI in your research projects. Empower yourself with the knowledge and skills to drive innovation and make a significant impact in your field.
£97.00