Module overview
Aims and Objectives
Learning Outcomes
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Devise valid and reliable methods and instruments for data and information collection in relation to one’s own research
- Gather, quantify, analyse, synthesise, critically evaluate and interpret complex information
- Apply scientific and clinical concepts to the development of new ideas and the synthesis of hypotheses
- Analyse problems objectively using key theoretical perspectives and empirical research
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- The value, nature, uses and limitations of a range of research methods
- The practical issues involved in carrying out quantitative research
- The identification and justification of the value of different sources of data in drawing conclusions from published literature
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Use information technology e.g. web/internet, databases, spreadsheets, statistical packages and word processing effectively
- Manage a research project with due attention to time and resource management.
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- Apply investigative skills/methods of enquiry to researching problems and issues in one’s area of research.
Syllabus
Learning and Teaching
Teaching and learning methods
Type | Hours |
---|---|
Independent Study | 150 |
Teaching | 50 |
Total study time | 200 |
Resources & Reading list
General Resources
Access to a computer/laptop computer/workstation. The course is based on computational data analysis. Access to a computer or workstation with working R environment and internet access is essential
Textbooks
Hastie, Tibshirani, Friedman. (2009). The Elements of Statistical Learning. Springer.
Bishop (2006). Pattern Recognition and Machine Learning.
Assessment
Assessment strategy
R programming test (10%) A short computer-based class test, comprising approximately 20 questions to monitor and facilitate the acquisition of basic skills required for the primary substantive summative assessments described below. Coursework 1. (40%) Summary of analysis techniques A written summary of supervised and unsupervised analysis techniques and their applications in quantitative cell biology, using the references provided in lectures and on Blackboard as a start. Students are encouraged to explore cutting-edge methods, that have been adopted for biological data analysis and are widely used in the scientific literature (1,500-word limit). Coursework 2. (50%) Analysis of a dataset Utilising the R software environment, you will be asked to conduct a thorough analysis of a dataset provided. The full problem details and dataset will be provided to the students via Blackboard. Submission will include annotated R code in a scrip format. Students will be assessed by the success of their method to achieve a thorough analysis of the dataset provided to include: 1. A fully annotated R script that executes all analysis commands and runs without error. 2. A set of professionally produced figures with appropriate captions that summarises the analysis. 3. A written summary providing biological interpretation of the analysis (300-word limit). Assessment requirements You must pass the module with an average overall mark of 50% or above. There is compensation between assessment elements provided a mark of 40% or higher is attained in each element. Candidates who fail one or more elements of the module at the first attempt will be permitted to re-sit the failed elements as supplementary assessments. Candidates who achieve at least 50% overall at the second attempt will be permitted to pass the module with a capped mark of 50%Summative
This is how we’ll formally assess what you have learned in this module.
Method | Percentage contribution |
---|---|
Data Analysis | 50% |
Written summary | 40% |
Class Test | 10% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Method | Percentage contribution |
---|---|
Data Analysis | 50% |
Written summary | 40% |
Class Test | 10% |