Python Programming
From first principles to idiomatic Python: data types, control flow, functions, OOP, and working with files and libraries.
BeginnerTeaching & learning
Complex ideas. Clearer beginnings.
Programming, data, statistics, and machine learning — with a path from first principles to practical understanding.
Data Analytics
Practical analysis with pandas and visualisation — cleaning, exploring, and communicating insight from real datasets.
Beginner → IntermediateMachine Learning
Core supervised and unsupervised methods, model evaluation, and the intuition behind the algorithms.
IntermediateApplied Statistical Analysis
Statistical reasoning for data science: distributions, inference, hypothesis testing, and regression.
IntermediateThe approach
Understanding comes first.
Intuition before notation.
Start with a clear mental model. Give the formalism something to attach to.
Learn by making.
Work with code and data you can run, change, and question.
Show the reasoning.
Make assumptions, trade-offs, and the messy middle visible.