Statistics, A/B testing, and data storytelling — from SQL to dashboards to insights.
Never written code? You can still start here.
Module 0 opens with 18 lessons of programming from nothing — your first program, variables, loops, functions, reading errors, the terminal, Git and arrays — before any of the Data Science material. No prior coding assumed.
AI Foundations — optional basics (free)
Week 0 · 20 lessons
Beyond the chatbox: what AI assistants really are
How the model "thinks" — and why it changes how you ask
The capability map: what AI does brilliantly
The limits: hallucination, freshness, and what never to trust it with
Your first real win: turn a 30-minute task into 3
6 senior modules · 29lessons · capstone & certificate. Upgrade your plan to unlock the advanced tier.
Causal Inference & Advanced Experimentation
5 lessons
The Causal Question: Potential Outcomes
Confounding & Causal Graphs (DAGs)
Get started
Take the free assessment to see your skill level and get a personalised learning plan.
Take AssessmentFeatured Projects
Automated EDA Profiler
A/B Test Analyser
Sales Dashboard
About
Data Science
20 questions · ~30 minutes
Context is everything: give it what it needs to know
Give it a role and a goal
Constraints and format: define what "good" looks like
Show, don't tell: examples and templates
Iterate like a pro: steer, don't restart
Documents and reports: summarise, extract, and draft better
Spreadsheets and data: Excel formulas, analysis, and charts
Presentations: from blank page to a solid deck
Long, messy input: transcripts, threads, notes, and screenshots
ChatGPT, Claude, Copilot, Gemini: which to reach for, and when
Make smarter decisions: AI as your thinking partner
Chaining steps: multi-part tasks done reliably
Your prompt library: reusable templates that save hours
Staying safe: privacy, accuracy, and company data
Capstone: do a real task end-to-end (graded) → your certificate
Foundations: Programming & Tooling
Week 0 · 23 lessons
What a program is and how to run your first one
Storing values in variables, and why text and numbers behave differently
Making decisions with if, else and comparisons
Storing many values in a list
Looking things up by name with dictionaries
Doing something to every item with loops
Writing your own functions
Reading errors without panic
Finding the mistake in your own code
Saving your code in a file and running it
Importing code other people have written
Giving each project its own environment
Talking to your computer with the terminal
Saving your work with Git
Putting your work on GitHub
What an array is, and why a list is not enough
Picking out pieces: indexing and slicing, including two dimensions
Maths on a whole array at once, and where you go next
Set Up Your Toolkit: Python, the Terminal & an Editor
Python Essentials: Variables, Types, Functions, Loops & Conditionals
Jupyter Notebooks & NumPy for Data
Pandas in 30 Minutes: Load, Inspect & Slice a DataFrame
Git & GitHub: Version Control & Shipping Your Project
Statistics Foundations
Week 1 · 5 lessons
Descriptive Statistics
Probability Basics
Distributions
Hypothesis Testing
Statistical Significance
SQL & Databases
Week 2 · 5 lessons
SQL SELECT & Filtering
JOINs & Aggregations
Window Functions
Subqueries & CTEs
Database Performance
Python for Data
Week 3 · 5 lessons
Pandas DataFrames
Data Cleaning
GroupBy & Pivot Tables
Merging & Joining Data
Time Series Basics
Data Visualisation
Week 4 · 7 lessons
Matplotlib Fundamentals
Seaborn for Stats Plots
Plotly Interactive Charts
Dashboard Design
Storytelling with Data
Geospatial Visualisation with Folium & GeoPandas
Building Interactive Data Apps with Streamlit
Exploratory Analysis
Week 5 · 6 lessons
EDA Process
Correlation Analysis
Outlier Detection
Feature Importance
Automated EDA Tools
Text Data: Cleaning, TF-IDF & Sentiment Basics
Predictive Modelling
Week 6 · 6 lessons
Regression Analysis
Classification Models
Model Selection
Cross-Validation
Ensemble Methods
Time-Series Forecasting with ARIMA & Prophet
A/B Testing
Week 7 · 5 lessons
Experiment Design
Sample Size Calculation
Statistical Tests
Multi-Variant Testing
Results Interpretation
Production Analytics
Week 8 · 5 lessons
Data Pipelines (ETL)
dbt for Modelling
Streamlit Dashboards
Reporting Automation
Data Team Best Practices
Time Series & Forecasting
Week 9 · 6 lessons
Thinking in Time: Components, Stationarity and the Resample Toolkit
Baselines That Beat Fancy Models
Classical Forecasting: Exponential Smoothing and ARIMA
Backtesting Without Lying to Yourself
ML for Forecasting: Features, Trees and Global Models
Forecasting in Production
Matching & Propensity Scores
Difference-in-Differences, RDD & IV
Experimentation at Scale
Advanced & Bayesian Statistics
5 lessons
Bayesian Thinking
MCMC & Probabilistic Programming
Bayesian A/B Testing & Decision-Making
Hierarchical / Multilevel Models
GLMs & Regularised Inference
MLOps & Productionising Models
5 lessons
From Notebook to Service: Serialization and a Prediction API
Reproducibility: Experiment Tracking, Versioning, and Pipelines
Monitoring in Production: Drift and Degradation
Retraining, CI/CD & Feature Stores
Reliability & Cost
Big-Data & Scale
5 lessons
Why Single-Machine Breaks & the Distributed Model
Spark DataFrames
Performance & Tuning
Scalable Feature Engineering
Beyond Spark: Modern Lakehouse (Polars/DuckDB)
Interpretability & Responsible AI
5 lessons
Why Interpretability
SHAP & Permutation Importance
Local Explanations & Counterfactuals
Fairness in Machine Learning
Responsible Deployment
Deep Learning Primer for Data Scientists
4 lessons
Neural Nets, Conceptually
Practical DL with Keras/PyTorch
Embeddings & Transfer Learning
Deep Learning in a Data-Science Workflow