Since August 2026
Instructor since August 2026
My lessons are designed to take you from simply following code to genuinely understanding how data science works.
We can cover the complete data science process, including data cleaning, exploratory data analysis, feature engineering, visualisation, statistics, machine learning, model evaluation and communicating results.
Depending on your goals, lessons can include:
Python, pandas, NumPy and scikit-learn
Data cleaning and exploratory analysis
Regression and classification
Decision trees, random forests and boosting
Clustering and dimensionality reduction
Cross-validation and model evaluation
Feature engineering and model interpretation
Neural networks and deep learning foundations
Bayesian modelling and PyMC
Portfolio and interview preparation
Support understanding university modules and projects
I use diagrams, analogies and practical demonstrations to make difficult ideas easier to understand. We will normally begin with an intuitive explanation, look at the underlying logic or mathematics, and then implement the concept in Python.
Lessons are personalised around your level. Complete beginners receive a structured learning path, while experienced students can focus on advanced topics, project guidance, debugging or interview preparation.
You will be encouraged to explain ideas back to me, interpret results and make your own modelling decisions. My goal is not only to help you produce working code, but to help you become an independent and confident data scientist.
We can cover the complete data science process, including data cleaning, exploratory data analysis, feature engineering, visualisation, statistics, machine learning, model evaluation and communicating results.
Depending on your goals, lessons can include:
Python, pandas, NumPy and scikit-learn
Data cleaning and exploratory analysis
Regression and classification
Decision trees, random forests and boosting
Clustering and dimensionality reduction
Cross-validation and model evaluation
Feature engineering and model interpretation
Neural networks and deep learning foundations
Bayesian modelling and PyMC
Portfolio and interview preparation
Support understanding university modules and projects
I use diagrams, analogies and practical demonstrations to make difficult ideas easier to understand. We will normally begin with an intuitive explanation, look at the underlying logic or mathematics, and then implement the concept in Python.
Lessons are personalised around your level. Complete beginners receive a structured learning path, while experienced students can focus on advanced topics, project guidance, debugging or interview preparation.
You will be encouraged to explain ideas back to me, interpret results and make your own modelling decisions. My goal is not only to help you produce working code, but to help you become an independent and confident data scientist.
1st lesson is backed
by our Good-fit Instructor Guarantee
by our Good-fit Instructor Guarantee