Postgraduate Course: Data Analysis for Epidemiology (PUHR11063)
Course Outline
| School | Deanery of Molecular, Genetic and Population Health Sciences |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | The course introduces the basic principles of statistical data analysis in an epidemiological context using the R programming environment for students who have already undertaken some introductory learning in the subject area. |
| Course description |
This course is designed to equip students who have already undertaken some learning in the principles of statistical methodology with a grounding in the practical skills of data analysis. The course will introduce good practice in data management and key skills in exploratory and inferential statistical analysis, using the open source R statistical programming language. Topics to be covered include numerical and graphical descriptive methods, simple one and two sample comparisons of categorical and continuous data using both confidence intervals and hypothesis tests, contingency tables and risk ratios, direct and indirect standardisation, correlation and simple linear regression as an introduction to general linear models.
The course (delivered online) will be based around 5 weekly case studies/ projects that will require students to undertake software-based analyses after using a mix of short recorded lectures and readings designed to illustrate each set of principles, with each week's activity building on the last. Each week, students will be encouraged to discuss their analysis plans, share useful code 'shortcuts' and discuss their results on the discussion boards. The case studies will be based on real epidemiological data sets and/ or common problems, although the principles will be applicable more broadly across a wide range of medical and scientific research.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | Suitable introductory statistics course equivalent to Epidemiology for Health Professionals (GLHE11016) - contact course organiser to check. |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2021/22, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Flexible |
| Course Start Date |
09/08/2021 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 5,
Seminar/Tutorial Hours 1,
Online Activities 35,
Feedback/Feedforward Hours 5,
Formative Assessment Hours 5,
Revision Session Hours 1,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
46 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Feedback |
Students will receive formative feedback during the course |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Select and apply correct statistical methods for one and two sample problems with continuous and with categorical data
- Prepare data and perform: numerical and graphical summary techniques; assumption checking; and confidence interval and hypothesis testing
- Think critically about and solve common epidemiological statistical problems
- Use statistical software and interpret and communicate output
- Autonomously undertake your own basic epidemiological statistical analysis
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Additional Information
| Graduate Attributes and Skills |
The skills developed by this course are key for most types of epidemiological enquiry, and thus fall broadly under the overarching Enquiry and Lifelong Learning attribute. In particular, the core tasks of analysis and project work involve problem solving, critical thinking and evaluation, which map closely to the Research and Enquiry cluster. However, this will also foster Personal and Intellectual Autonomy, contributing to the student's ability to conceive, design, execute and interpret epidemiological research. |
| Keywords | epidemiology,data,analysis,R,software,statistics |
Contacts
| Course organiser | Dr Niall Anderson
Tel: (0131 6)50 3212
Email: Niall.Anderson@ed.ac.uk |
Course secretary | Mrs Rosemary Porteous
Tel: (0131 6)50 9835
Email: Rosemary.Porteous@ed.ac.uk |
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