Postgraduate Course: Data analysis with R (PUHR11103)
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 using the R programming environment for statistical data analysis: it focusses on epidemiological applications in a wide variety of health sciences contexts, and assumes students have already undertaken some introductory learning in statistical methodology. |
| 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 using the open source R statistical programming language. The course will introduce good practice in data management and key skills in exploratory and inferential statistical analysis. Extensive use will be made of tidyverse packages such as readr, tidyr, diplyr and ggplot2. Statistical 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, measures for diagnostic testing, and simple logistic regression 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 data sets and/ or common problems in epidemiology.
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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 | Training in introductory-level statistical methods (coverage: 1 and 2-sample problems, hypothesis testing, confidence intervals, logistic regression, graphical methods). |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2022/23, Available to all students (SV1)
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Quota: None |
| Course Start |
Flexible |
| Course Start Date |
08/08/2022 |
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 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework 100 %, Practical Exam 0 %, Written Exam 0 % |
| Feedback |
Students will receive continuous feedback via Discussion Boards, and undertake a short formative practical exercise around week 4 of the course, which will be in the style of the final assessment. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Apply a range of statistical methods to data analysis problems in epidemiology.
- Critically evaluate common problems in epidemiology in order to select appropriate statistical approaches.
- Use the R statistical programming language with confidence and interpret and communicate output
- Show autonomy in the design and execution of basic statistical analysis in epidemiological problems
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Reading List
| R for Data Science (2017). Wickham H. and Grolemund, G.O¿Reilly Media: California |
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 | data analysis,with R,R,R programming |
Contacts
| Course organiser | Dr Niall Anderson
Tel: (0131 6)50 3212
Email: |
Course secretary | Miss Suzanne Newall
Tel: (0131 6)50 3237
Email: |
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