Published 3/2026
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 19m | Size: 769.72 MB
Use Python to load, clean, analyze, visualize healthcare data, and build basic predictive models.
What you’ll learn
Use Python and Jupyter/Colab to load, clean, and explore real‑world healthcare datasets.
Perform descriptive statistics and create insightful visualizations to uncover trends in clinical and public health data.
Apply core analytics techniques (grouping, aggregation, time‑series analysis, correlation) with libraries like pandas, NumPy, and Matplotlib/Seaborn.
Build and evaluate basic predictive models (e.g., regression, classification) for common healthcare analytics tasks.
Work with common healthcare data formats (CSV, Excel, simple EHR‑style tables) while applying good practices for data quality and reproducibility.
Requirements
Basic computer literacy and ability to install software or use Google Colab.
No prior programming experience required; all necessary Python basics are taught in the course.
Interest in healthcare, public health, or clinical data and a willingness to work with real datasets.
Description
Learn how to turn raw healthcare data into meaningful, actionable insights using Python.
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.
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