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PX5503: DATA VISUALISATION (2020-2021)

Last modified: 07 Dec 2020 10:15


Course Overview

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.

Course Details

Study Type Postgraduate Level 5
Session Second Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
Co-ordinators
  • Dr Alessandro P. S. (Physics) Moura

What courses & programmes must have been taken before this course?

  • Any Postgraduate Programme

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

Are there a limited number of places available?

No

Course Description

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.


Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

  • 2 Practicals during University weeks 25 - 27

More Information about Week Numbers


In light of Covid-19 and the move to blended learning delivery the assessment information advertised for courses may be subject to change. All updates for first-half session courses will be actioned no later than 1700 (GMT) on 18 September 2020. All updates for second half-session courses will be actioned in advance of second half-session teaching starting. Please check back regularly for updates.

Summative Assessments

Computer Programming Exercise

Assessment Type Summative Weighting 34
Assessment Weeks 27 Feedback Weeks 28

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
ReflectionCreateLearn how to implement modern data visualisation techniques in the Mathematica scientific environment and programming language.
ReflectionCreateLearn to apply modern data visualisation techniques to find patterns and correlations in data, and test hypotheses.
ReflectionCreateUse modern data visualisation techniques to make compelling presentations; and learn narrative-based approaches to present data (storytelling with data), enhanced by appropriate data visualisation.

Computer Programming Exercise

Assessment Type Summative Weighting 33
Assessment Weeks 26 Feedback Weeks 27

Look up Week Numbers

Feedback
Learning Outcomes
Knowledge LevelThinking SkillOutcome
ReflectionCreateUse modern data visualisation techniques to make compelling presentations; and learn narrative-based approaches to present data (storytelling with data), enhanced by appropriate data visualisation.
ReflectionCreateLearn how to implement modern data visualisation techniques in the Mathematica scientific environment and programming language.
ReflectionCreateLearn to apply modern data visualisation techniques to find patterns and correlations in data, and test hypotheses.

Computer Programming Exercise

Assessment Type Summative Weighting 33
Assessment Weeks 25 Feedback Weeks 26

Look up Week Numbers

Feedback
Learning Outcomes
Knowledge LevelThinking SkillOutcome
ReflectionCreateLearn to apply modern data visualisation techniques to find patterns and correlations in data, and test hypotheses.
ReflectionCreateUse modern data visualisation techniques to make compelling presentations; and learn narrative-based approaches to present data (storytelling with data), enhanced by appropriate data visualisation.
ReflectionCreateLearn how to implement modern data visualisation techniques in the Mathematica scientific environment and programming language.

Formative Assessment

There are no assessments for this course.

Resit Assessments

Computer Programming Exercise

Assessment Type Summative Weighting 33
Assessment Weeks 49 Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Computer Programming Exercise

Assessment Type Summative Weighting 33
Assessment Weeks 49 Feedback Weeks

Look up Week Numbers

Feedback
Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Computer Programming Exercise

Assessment Type Summative Weighting 34
Assessment Weeks 49 Feedback Weeks

Look up Week Numbers

Feedback
Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
ReflectionCreateLearn how to implement modern data visualisation techniques in the Mathematica scientific environment and programming language.
ReflectionCreateLearn to apply modern data visualisation techniques to find patterns and correlations in data, and test hypotheses.
ReflectionCreateUse 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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