Data Visualisation
| Module title | Data Visualisation |
|---|---|
| Module code | SOCM029 |
| Academic year | 2020/1 |
| Credits | 15 |
| Module staff | Susan Banducci (Lecturer) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 25 |
|---|
Module description
This course will introduce you to the field of data visualization.you will learn basic principles of data and model visualization. The focus will be on the principles of turning data into graphical representations for describing and exploring data, analyzing hypotheses and relationships and presenting evidence. Particular attention will be paid to visualizing data for policy audiences. You will learn techniques for visualizing different types and formats of datautilizing industry standard, open source software.
Module aims - intentions of the module
The main aims of the module are:
- To understand and apply principles of data visualization
- To develop skills in capturing and managing data for visualisation
- To analyze subject relevant data sets using data visualisation techniques
- To learn to quantitatively and qualitatively evaluate existing visualizations
- To further develop skills in using the ggplot2 package for R and related packages for data visualisation.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Demonstrate proficiency in the use of R for data visualisation
- 2. understand code in R and implement appropriate commands to perform relevant analyses
- 3. Understand principles of and appropriate use of data visualization to communicate data analysis.
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 4. Develop coding skills in a way that results in high level of synergies with quantitative research skills.
- 5. manipulate data in R and use the appropriate analytic tools.
- 6. interpret output from each program and draw appropriate inference regarding the hypotheses being tested.
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 7. Demonstrate understanding of computing skills
- 8. Demonstrate understanding of data management skills
Syllabus plan
Whilst the module’s precise content and order of syllabus coverage may vary, it is envisaged that it will include the following topics:
- Course Introduction, Terminology
- Introduction to R. Types of data
- Main principles of data visualization
- Types of statistical graphics
- Cleaning and preparing data for visualisations. The package dplyr.
- Basic Charts and Plots, Multivariate Data Visualization
- The package ggplot2: structure of plots and commands
- Principles of Perception, Color, Design, and Evaluation
- Text Data Visualization
- Temporal Data Visualization
- Geospatial Data Visualization
- Network Data Visualization
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 20 | 130 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Lectures with lab | 20 hours in total | 10 x 2 hour lectures and labs .These lectures cover the main concepts of the course. These practical sessions cover the application of techniques |
| Independent study | 60 hours | Reading and preparing for lectures and labs (around 4-6 hours per week); |
| Independent study | 70 hours | researching and writing assessments and assignments (researching, planning and writing the course work). |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| In-class exercises | 15 minutes each x 4 | 1-8 | Peer and oral feedback |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Practical Exercise 1 using the skills/techniques developed to investigate a research problem relevant to the students chosen discipline. | 50 | 1500 words & up to 5 data figures | 1-8 | Written Feedback |
| Practical Exercise 2 using the skills/techniques developed to investigate a research problem relevant to the students chosen discipline. | 50 | 1500 words & up to 5 data figures | 1-8 | Written Feedback |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Practical Exercise 1 | Practical Exercise 1 (1500 words & up to 5 data figures) | 1-8 | August/September reassessment period |
| Practical Exercise 2 | Practical Exercise 2 (1500 words & up to 5 data figures) | 1-8 | August/September reassessment period |
Indicative learning resources - Basic reading
Basic reading:
Hadley Wickham, ggplo2. Elegant Graphics for Data Analysis. 2nd ed. Springer, 2015.
Winston Chang, R Graphics Cookbook. O’Reilly, 2013.
John Chambers, William Cleveland, Beat Kleiner and Paul Tukey, Graphical Methods for Data Analysis. Wadsworth, 1983.
Edward Tufte. The Visual Display of Quantitative Information. Graphics Press, 2001.
Leland Wilkinson, The Grammar of Graphics. 2nd ed. Springer, 2005.
Tamassia, Roberto, ed. Handbook of graph drawing and visualization. CRC press, 2013.
Indicative learning resources - Web based and electronic resources
http://r4ds.had.co.nz/data-visualisation.html
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 28/11/2016 |
