Module 1 Introduction to Biology and Programming.
The incoming student needs to meet the learning objectives of Module 1.
Exclusions
Description
This course will provide an introduction (or refresher) to essential quantitative theory that underpins modern bioinformatics. Concepts will be introduced via a series of core problems whose details will be explored in greater depth in later modules.
Quantitative topics will include:
• Linear Algebra: essential matrix-vector operations, least-squares
• Probability Theory: Rules of Probability, Conditional Probability, Bayes’ Rule, distributions
• Correlation and Causation: Parametric and non-parametric measures
• Introduction to Statistical Modelling in the R programming language: linear models, estimation
The module contains a variety of integrated learning environments, including interactive lectures as well as tutorials to explain and give feedback on aspects of assessment.
Learning Outcomes
By the end of the module students should be able to:
Understand essential mathematical and statistical concepts and apply the correct techniques to solve elementary data analysis problems
Correctly apply techniques for the graphical representation and visualisation of data
Perform essential statistical data analysis in a computer programming language, specifically R
Solve quantitative problems inspired by real world bioinformatics that require an understanding of the underlying biology and the application of the correct mathematical and statistical techniques
Demonstrate the qualities and transferable skills necessary for employment requiring the exercise of initiative and personal responsibility, decision making in complex and unpredictable situations, and the independent learning ability required for continuing professional development
Assessment: The course will be assessed via a two-hour examination consisting of structured questions (70%) and a coursework assignment that will involve the use of the R programming language to perform a statistical analysis of an assigned data set using the techniques covered in the course (30%).
Reassessment: If a student fails the module then they will be required to repeat the failed components only.
