Computational biology and data science have undergone tremendous expansion in recent years, resulting from increased computational power and accessibility of quantitative biological measurements. The course aims to enthuse students about the power of computational and statistical analyses, and their roles in biological research. This will be achieved through sessions that combine lectures and computer practicals on data carpentry, handling, modelling and data analysis using different types of biological measurements from the areas of Genomics, Evolutionary Biology and Biodiversity research. You will gain confidence in searching and curating real-life datasets, and combining them to answer biological questions that are inaccessible without the use of computers.

Learning objectives

On successful completion of the module, a student will be able to:

Access, curate, integrate, visualise and analyse messy real life biological datasets using Data Carpentry principles.
	Appreciate command-line computing. Understand the Linux operating system, practice commands for extracting, editing and examining data.
	Apply logic and basic syntax, which are the fundamental rules of any programming language, in order to write functioning code in Python.
	Understand data modelling. Apply different models to better plan experiments, compare outcomes and interpret data.
	Understand mathematically and statistically guided experimental design and identify stochastic phenomena in biology.
	Understand the interplay between modelling and experiments in biological research.
Structure 

BIOL0053 will run once per week. Each 4-hour session is a combination of background information (lecture) and in class computational exercises (tutorial). The module will start with sessions on data carpentry, followed by sessions on the Linux command line and Python for retrieving, organising and analysing large-scale data. Subsequent sessions will cover data science methods used in biology with main focus on data modelling, parameter estimation using real experimental data, model assumptions and data interpretation. The module will present biological research topics requiring quantitative handling, and the corresponding approaches to address them.