This course provides an introduction to biostatistics with a focus on the practical analysis of biomedical data. Students develop foundational knowledge in statistical reasoning, data analysis, and R programming, and learn how to apply statistical methods to real problems in the biomedical sciences. Through case studies and hands-on exercises, the course emphasizes data wrangling, visualization, hypothesis testing, regression, dimensionality reduction, and introductory machine learning.
Introduction to Biostatistics
Online
Course Overview
Key Topics
- Statistical concepts in biomedical research
- Probability distributions, sampling, estimation, and confidence intervals
- Hypothesis testing and multiple testing
- R programming for data analysis
- Data wrangling and visualization
- Matrix algebra and linear models
- Generalized linear models
- Dimensionality reduction
- Introduction to Bayesian statistics
- Introduction to machine learning in biomedical data analysis
Learning Outcomes
By the end of this course, students will be able to:
- Understand core statistical concepts used in biomedical research
- Use R to analyse and visualise biological data
- Apply data wrangling techniques to prepare datasets for analysis
- Select and apply appropriate statistical methods to biomedical problems
- Interpret results from regression, dimensionality reduction, and other analytical approaches
Teaching & Learning Format: Online
Assessment
- Weekly assignments
- Quizzes
- Capstone project
Indicative Background: A basic background in programming and quantitative reasoning is recommended. Prior familiarity with R is helpful, and an introductory refresher is provided.