Data Visualisation

The details
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Postgraduate: Level 7
Tuesday 22 April 2025
Friday 27 June 2025
22 May 2024


Requisites for this module



Key module for

MSC G304JP Data Science with Professional Placement,
MSC G304JS Data Science,
MSC G305JS Applied Data Science,
MSC G306JS Data Science and its Applications

Module description

In a world increasingly driven by data, the need for analysis and visualisation is more important than ever. In this module we will look at data through the eyes of a visual detective.

We will work on the lost art of exploratory data analysis, reviewing appropriate methods for data summaries with the aim to summarise, understand, extract hidden patterns and identify relationships. We will then work on graphical data analysis, using simple graphs to understand the data, but also advanced complex methods to scrutinise data and interactive plots to communicate data information to a wider audience.

Module aims

The aims of this module are:

  • To create data analysts that can identify patterns and display information from several data sources.

  • To encourage statistical thinking by a series of examples of good and not-so-good visualisations.

  • To guide students to develop their creativity within a scientific framework.

  • To highlight how visualization plays a key role in many disciplines.

Module learning outcomes

By the end of the module, students will be expected to be able to:

  1. Summarise and understand information on text, categorical and continuous variables.

  2. Display graphical information and complex relationships in datasets using R.

  3. Use advanced statistical packages like ggplot2 and produce statistical reports with Rmarkdown.

  4. Design and implement interactive plots.

Module information

Indicative syllabus

  • Historical examples of visualization

  • Cognition linked to visualization including linguistics, mathematics, natural sciences, art and wider cultural topics.

  • Data Visualization for Human Perception

  • What makes a good graph – What makes a bad graph

  • Examining variables and basic R charts

  • Exploring relationships, looking for structure

  • Advanced plots with ggplot2

  • Creating statistical reports with Rmarkdown

  • Interactive graphs

  • Testing data quality through graphs

  • Plotting Maps

  • Data visualization within industry

  • Telling a story

For data analysis and visualisations we will use R-studio, ggplot2 and plotly packages, as well as google visualisations and interactive plotting.

Learning and teaching methods

Teaching in the School will be delivered using a range of face-to-face lectures, classes, and lab sessions as appropriate for each module. Modules may also include online only sessions where it is advantageous, for example for pedagogical reasons, to do so.


This module does not appear to have a published bibliography for this year.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Assignment     

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
100% 0%


Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Rishideep Roy, email:
Dr Rishideep Roy; Dr Na You



External examiner

No external examiner information available for this module.
Available via Moodle
No lecture recording information available for this module.


Further information

* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.

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