This course provides a comprehensive foundation in data science, blending classical statistical and programming skills with modern AI-assisted techniques. Learners will progress from core data science concepts through to machine learning, generative AI, and data storytelling — developing the practical ability to find, interpret, and communicate insights from data in real-world contexts.
Aspiring data analysts, business professionals working with data, and those looking to transition into data science roles. No prior programming or statistics experience is required, though a comfort with numbers and logical thinking is helpful.
By the end of the course, participants should be able to:
- Explain the data science lifecycle and its business applications
- Apply core statistical concepts, probability, and inference to data problems
- Identify appropriate data sources, types, and storage technologies
- Write and debug Python and R scripts with AI-assisted coding support
- Clean, transform, and structure data using prompting techniques
- Conduct exploratory data analysis with AI-generated summaries and pattern detection
- Use generative AI tools for insight extraction, explanation, and hypothesis generation
- Apply supervised and unsupervised machine learning to generate and interpret insights
- Support data-driven decision making in business contexts
- Communicate findings through structured data stories and effective visualisations
- Complete a final project demonstrating end-to-end data science capability
Module 01: Foundations of Data Science
An introduction to what data science is, how it is applied, and the lifecycle that underpins it.
Topics
- What is data science
- Data science life cycle and applications
Module 02: Foundations of Statistics
Core statistical knowledge needed to understand, interpret, and draw conclusions from data.
Topics
- Statistical concepts
- Probability theory
- Statistical inference
Module 03: Data Sources and Types
Understanding the variety of data available and how it is stored and used in visualisation contexts.
Topics
- Types of data and sources
- Data storage technologies and application to data visualisation
Module 04: Programming for Data Exploration with AI Assistance
An introduction to Python and R for data science, with AI-assisted coding to support script generation and debugging.
Topics
- Introduction to Python and R for data science
- AI-assisted coding via prompts including generating scripts and debugging
Module 05: Data Wrangling with AI Support
Using AI prompts to efficiently clean, transform, and structure raw data for analysis.
Topics
- Using prompts to clean, transform, and structure data
Module 06: Exploratory Data Analysis with AI
Applying AI tools to surface patterns, generate summaries, and ask sharper analytical questions.
Topics
- AI-generated summaries and pattern detection
- How to ask better analytical questions via prompts
Module 07: Generative AI for Insight Generation
A core module exploring the generative AI tools landscape and how to prompt for meaningful analytical outputs.
Topics
- GenAI tools landscape
- Insight extraction prompts
- Explanation prompts
- Hypothesis generation
Module 08: Machine Learning for Insight, Not Complexity
A practical introduction to machine learning algorithms focused on generating and interpreting business-relevant insights.
Topics
- Supervised and unsupervised learning algorithms
- Other common algorithms
- Interpreting outputs with AI
Module 09: Data-Driven Decision Making
How to apply data findings to real business decisions using the right tools and frameworks.
Topics
- Tools for data-driven decision making
- Making business decisions and measuring impact
Module 10: Data Storytelling and Visual Communication
A core module on translating data into compelling narratives and visuals tailored to different audiences.
Topics
- Visual design principles
- Narrative structuring
- Audience adaptation
- Using AI to generate narratives and visuals
Module 11: Summary Project
A capstone project bringing together the full data science workflow from preparation through to storytelling.
Topics
- Data preparation
- AI-assisted insight generation
- Visualisation design
- Storytelling for decisions
Module 12 (Optional): AI Agents for Data Analysis
An introductory look at how AI agents can automate insight generation and reporting workflows.
Topics
- Automating insight generation and reporting with AI agents
