Jupyter
Hands-on labs for your apprenticeship programme
Learn Python basics through satellite constellation telemetry data
Lab 2Query a city transport database to uncover ridership patterns and service issues
Lab 3Merge, clean, and transform supply chain data from multiple global sources
Lab 4Analyse emergency department data to identify bottlenecks and improve patient flow
Lab 5Build effective charts and a dashboard using global climate and energy data
Lab 6Analyse a clinical trial dataset to draw conclusions about drug efficacy
Lab 7Build and evaluate regression models to understand urban property prices
Lab 8Analyse and forecast renewable energy generation for grid planning
Lab 9Build fraud detection and customer segmentation models for a retail bank
Learn Python fundamentals through NASA Mars rover telemetry data
Lab 2Master SQL querying with a music streaming database
Lab 3Design robust data models for an NHS hospital trust
Lab 4Work with CSV, JSON, Parquet, and specialist data formats using global climate data
Lab 5Build extract-transform-load pipelines for an e-commerce marketplace
Lab 6Build bulletproof data validation for a fintech startup
Lab 7Prepare data for AI-powered search at The British Library
Lab 8Apply everything you have learned to real-world data engineering challenges
Evaluate a pretrained object detection model on dashcam imagery
Lab 2Explore historical climate data and build your first regression model
Lab 3Build a binary classifier and learn why recall matters in medical screening
Lab 4Build a classifier for heavily imbalanced transaction data and learn why precision matters
Lab 5Classify text comments using NLP techniques and balance precision and recall with F1
Lab 6Forecast power demand with time series data, mixed features, and gradient boosting
Lab 7Compare multiple models with experimental rigour on complex pharmaceutical data
Lab 8Inherit, monitor, evaluate, and maintain a deployed credit scoring model
De-mystify the concepts around validation, loss measurement and convergence.
Hands-on statistics practice: Welch's t-test, effect size, and permutation testing using app review data.
Use supervised learning to train a binary classification model
Evaluate and select the best model and learn to justify your decision.