Data Science for the Health Sciences
Foundations and applications to Nigerian health systems
Develop a reproducible health-data workflow, from data collection and cleaning to statistical inference, machine learning, disease modelling and responsible communication. The textbook uses R and Python alongside Nigerian health-system examples.
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18 chapters · 512 body pages · R and Python · Worked examples and exercises
Before you start
Basic statistical reasoning and a willingness to practise in R and Python. Start with the foundations before moving to modelling.
Read the preface and contents.
The textbook’s teaching datasets and mini-projects are simulated or illustrative. Use them to practise the workflow and interpretation.
Your study route
Foundations
Data wrangling and exploratory analysis
Statistical inference and modelling
Machine learning and advanced methods
Big data, ethics and practice
Python for disease epidemiology
Solutions and reference materials
How to use the course
Follow the chapters in sequence, or use the sidebar to revisit a method. Each reading has page controls, text search and a downloadable PDF. Attempt the exercises independently before opening the answer key. Keep a record of the assumptions, computations and interpretation for each worked example.
The chapter readers contain the complete supplied textbook pages. The website adds navigation and study guidance; the original mathematical notation, code, diagrams, exercises and solutions are preserved. The full download combines the supplied preliminary material and body into one bookmarked textbook.