In this practical, project‑based course, you’ll work with real clinical and public‑health datasets to build a complete end‑to‑end analytics pipeline. You’ll start by setting up your Python environment (locally or in Google Colab) and refreshing the core Python skills you need for data work. From there, you’ll learn how to load healthcare data from CSV files, understand variable meanings, and deal with real‑world issues like missing values, outliers, and messy categorical fields.
Next, you’ll explore data visually using pandas, seaborn, and matplotlib. You’ll create clear histograms, boxplots, and subgroup plots that help clinicians and decision‑makers quickly understand patterns in risk factors, outcomes, and patient subgroups. Building on this, you’ll engineer practical features and train your first predictive models with scikit‑learn, including logistic regression and random forests, to tackle problems such as disease risk prediction.
Throughout the course, we emphasise careful evaluation (accuracy, sensitivity, specificity, ROC AUC) and responsible interpretation so you avoid overclaiming from models. You’ll finish with an end‑to‑end capstone project—cleaning, analysing, modelling, and reporting on a healthcare dataset—resulting in a portfolio‑ready notebook and summary you can share with employers or supervisors. This course is ideal for students, clinicians, analysts, and aspiring data scientists who know basic Python and want to apply it confidently to real healthcare problems.
