Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Data Visualisation and Analysis | |||
| Reference | CMM020 | Version | 6 |
| Created | April 2023 | SCQF Level | SCQF 11 |
| Approved | January 2013 | SCQF Points | 15 |
| Amended | August 2023 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| To introduce the principles and techniques involved in the displaying of data to provide greater insight into the information contained within the data. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Critically appraise different data visualisation methods for a variety of data types. |
| 2 | Produce fitted models for data. |
| 3 | Critically evaluate the output of data visualisation and analysis tasks. |
| 4 | Produce solutions for the effective display and analysis of data. |
| Indicative Module Content |
|---|
| Visualisations: reasons for data visualisation; visualisation requirements; cognitive processes in visualisation; lie factor; data-ink ratio; data variation vs. design variation; mapping data to visual representations; basic charts and their uses; display of quantitative data including univariate, bivariate, trivariate, multidimensional, tree and network data; data visualisation design; data and task abstractions; visual encodings; marks and channels. Analysis: general considerations in data analysis; descriptive statistics; univariate distributions; bivariate data and linear regression; time series; smoothing including moving average and exponential; seasonal effects; additive, multiplicative and mixed models; professional use of data; ethical and legal issues within data analysis. |
| Module Delivery |
|---|
| The module is taught using a structured programme of lectures, tutorials, practical exercises and student-centred learning. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 30 | 30 |
| Non-Contact Hours | 120 | 120 |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | 150 |
| Actual Placement hours for professional, statutory or regulatory body |
| ASSESSMENT PLAN | |||||
|---|---|---|---|---|---|
| If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
| Component 1 | |||||
| Type: | Practical Exam | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4 |
| Description: | Practical examination where the student applies analysis and visualisation techniques to a given dataset and evaluates the results obtained. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| To achieve a pass in this module requires a minimum of Grade D in Component 1. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | A in Component 1 |
| B | B in Component 1 |
| C | C in Component 1 |
| D | D in Component 1 |
| E | E in Component 1 |
| F | F in Component 1 |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | None. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | WARE,C.,2019.Information Visualization: Perception for Design. 4th ed. Morgan Kaufmann. |
| 2 | KIRK, A., 2019. Data Visualisation, A Handbook for Data Driven Design. 2nd ed. Sage Publishing |
| 3 | TUFTE, E.,2001. The Visual Display of Quantitative Information. Graphics Press. |
| 4 | MUNZER, T.2014 Visualisation Analysis and Design. CRC Press. |
| 5 | DIEZ, D.M., BARR, C.D., CETINKAYA-RUNDEL, M., 2015. [online] OpenIntro Statistics. 3rd ed. OpenIntro. Available from: https://www.openintro.org/stat/textbook.php [Accessed 25th March 2016] |
| 6 | COWPERTWAIT, P.S.P. , METCALFE, A.V., 2009. Introductory Time Series with R. Springer. |