Last modified: 05 Aug 2021 13:04
Visualising the outcome of a data analysis is critical to communicate the results. In this course we will study standard and cutting edge visualisation techniques to make sense of data, and present it in a compelling, narrative-focused story.
Presenting and visualising data and reporting on the result of an analysis are a crucial skill when making sense of data.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | Second Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
| Co-ordinators |
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In this course we will start discussing standard methods to visualise data, such as histograms, bar charts, different representations of time series, etc. These methods are critical for an exploratory data analysis.
Many modern problems in (big) data analysis require innovative and artistic representation of data so that the analysis can generate maximal impact.
Computer Programming Exercises (33%, 33%, 34%)
Resit (for students taking the course in AY20/21)
An online programming assessment
There are no assessments for this course.
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Reflection | Create | Learn how to implement modern data visualisation techniques in the Mathematica scientific environment and programming language. |
| Reflection | Create | Learn to apply modern data visualisation techniques to find patterns and correlations in data, and test hypotheses. |
| Reflection | Create | Use modern data visualisation techniques to make compelling presentations; and learn narrative-based approaches to present data (storytelling with data), enhanced by appropriate data visualisation. |
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