Communicating and Presenting Results - MAST5954

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2022 to 2023
Canterbury
Spring Term 5 15 (7.5) Owen Lyne checkmark-circle

Overview

The module will begin by reviewing simple data presentation techniques such as graphical and numerical summaries and consider how these can be used to appropriately present data. The module will then develop these themes through the use of more advanced graphical summaries and data visualisation methods (including their production in a suitable statistical software package), the presentation of results in different media and the reporting of complicated analyses (including the correct interpretation of results and discussion of modelling assumptions).
Syllabus: Review of simple data presentation; Appropriate use of graphics; presenting data in different media; advanced graphics using a package such as the R Shiny package; Writing a report about a more complicated data analysis (discussing assumptions and interpreting results); automating report writing using a package such as the R Markdown package.

Details

Contact hours

30 contact hours comprising a series of workshops
120 hours of private study
Total number of study hours: 150

Method of assessment

100% coursework

Indicative reading

C. Knaflic (2015) Storytelling with data: a data visualization guide for business professionals. Hoboken, New Jersey: Wiley.
W. Chang (2013) R Graphica Cookbook: Practical Recipes for Visualizing Data. O"Reilly
H. Wickham (2010) ggplot2: Elegant graphics for data anlysis (Use R!). Springer
H. Wickham and G. Grolemund (2017) R for Data Science. O'Reilly.
P. Bruce (2017) Practical Statistics for Data Scientists: 50 Essential Concepts. O'Reilly.

See the library reading list for this module (Canterbury)

Learning outcomes

The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
1 demonstrate knowledge and critical understanding of the underlying concepts and principles of data visualisation;
2 demonstrate the capability to use a range of established data visualisation techniques in appropriate media;
3 select and deploy appropriate data visualisation techniques in the communication and presentation of results of data analyses;
4 make appropriate use of statistical software.

The intended generic learning outcomes.
On successfully completing the module students will be able to:
1 make effective use of IT facilities for solving problems;
2 demonstrate the skills needed to work and communicate in a group, including an understanding of the roles of different individuals within a team;
3 communicate straightforward arguments and conclusions reasonably accurately and clearly;
4 manage their own learning and development;
5 communicate technical and non-technical material competently;
6 present and debate using data;
7 apply techniques from different academic disciplines to a problem;
8 demonstrate critical thinking skills.

Notes

  1. Credit level 5. Intermediate level module usually taken in Stage 2 of an undergraduate degree.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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