Preface
This book is designed as the supporting textbook for BIOF1001: Introduction to Biomedical Data Science, an undergraduate course (Year 1) at the University of Hong Kong.
This book is not aimed to be a comprehensive textbook, but rather more Rmarkdown notebooks as supplementary to lecture notes so that students can reproduce the teaching contents more easily.
Introduction for readers
What you will learn from this course/book
In part I, you will find a general introduction to data science (by Dr YH Huang):
- Basic programming and visualisation skills: R scripts for the quantitative methods and data visualisation.
- Quantitative methods: t-test, correlation analysis, clustering, linear regression, linear classification.
- Gain familiarity with common databases in the biomedical domain.
- Introduce ethical, legal, social and technological issues related to biomedical data sciences.
- Introduce good practice in managing a data science project and communicate results to key stakeholders.
In part II, you will experience data types in four different biomedical topics, which will be illustrated with both introduction and cases that are suitable for problem-based learning format:
- Medical imaging and digital health, by Dr Joshua Ho and Dr Rachel Kwan
- Cancer genomics and epidemiology, by Dr David Shih and Dr Jason Wong
- Population genetics and diseases, by Dr Clara Tang and Dr Yuanhua Huang
Other reference books
Besides this online book as a collection of R materials for the teaching contents, we also recommend the following online books as reference:
- Introduction to Data Science: Data Wrangling and Visualization with R by Rafeal A. Irizarry
- Advanced Data Science: Statistics and Prediction Algorithms Through Case Studies by Rafeal A. Irizarry