Data Organisation and Management in Epidemiology.
| Code | School | Level | Credits | Semesters |
| EPID4022 | School of Medicine | 4 | 10 | Spring UK |
- Code
- EPID4022
- School
- School of Medicine
- Level
- 4
- Credits
- 10
- Semesters
- Spring UK
Summary
The course will focus on the process of creating a dataset suitable for analysis (from source data) rather than on the analysis itself. It will provide a practical introduction to specialist statistical analysis software and demonstrate how basic programming techniques can be used to carry out repetitive tasks. Taught by a mixture of practical computer sessions and mini-lectures supplemented by self-directed learning outside of contact time to consolidate the learning experience. The initial sessions will cover formatting of data, entering/importing data, how to perform calculations on data (including use of functions), merging and appending data and processing subsets of data values. The later sessions will cover organisation of work using syntax files and advanced automation features. These will be supplemented with two assessment clinics where students will have the opportunity to ask the instructors to go over topics. There will also be the opportunity to practice assessment questions in these clinics.
Target Students
Primarily postgraduate students on the Master of Public Health, Master of Public Health (Global Health) and Master of Public Health (Research Methods). Places will be available to new staff and PhD students in School of Medicine who require training as part of their research role.
Classes
- One 3-hour lecture each week for 12 weeks
Assessment
- 100% Coursework 1: Data based coursework
Assessed by end of spring semester
Educational Aims
For students to become proficient in the use of statistical analysis software for organising and manipulating data. This will include an understanding of the importance of adequate data management prior to the commencement of statistical analysis and how to use advanced functionality to ensure that data management is carried out efficiently and with the potential for errors minimised.Learning Outcomes
Knowledge and understanding
- To understand that data management represents the gap between raw data and statistical analysis and that specialised data management and analysis packages have many useful facilities for bridging this gap.
Intellectual skills
- To distinguish between different types of data and understand how data are arranged within files, rows and columns.
Professional practical skills
- To understand the advantages of the R programming language and gain practical skills in using R Studio to maximise the potential of this.
- To learn how to arrange commands in syntax files to keep an audit trail of tasks performed.
- To explore strategies and techniques to carry out tasks as efficiently as possible.
Transferable skills
- To develop logical and organised thinking, understand the importance of attention to detail and develop the ability to present complex information as simply as possible.
- To appreciate how developing good habits in working with data can aid with the management and analysis of real-world public health datasets
Information technology skills
- To demonstrate competence in a range of IT skills with respect to achieving an end goal - the production of a final data set for which future statistical analysis can be performed.
- To acknowledge the potential of developments in the field such as web scraping and artificial intelligence to make the most effective use of available data