Exploratory Data Analysis and Data Visualisation

The details
Mathematical Sciences
Colchester Campus
Postgraduate: Level 7
Sunday 17 January 2021
Friday 26 March 2021
15 July 2020


Requisites for this module



Key module for

MSC G30512 Applied Data Science,
MSC G305JS Applied Data Science,
MSC G30624 Data Science and its Applications,
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 course we will look at data through the eyes of a numerical 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.
For data analysis and visualisations we will use R-studio, and a combination of R-shiny applications and google visualisations for interactive plotting.

Module aims

The aim of the course will be to create data analysts that can identify patterns and display information from data of several sources. The course will encourage statistical thinking by a series of examples of good and not-so-good visualisations and will guide students to develop their creativity within a scientific framework.

Module learning outcomes

At the end of the course students will be able to:

- Summarise and understand information on categorical and continuous variables
- Explore relationships between different variables
- Display graphical information and complex relationships in datasets using R
- Use advanced statistical packages like ggplot2 and produce statistical reports with Rmarkdown
- Create interactive graphs with R shiny and googleVis

Module information


-Historical examples of visualization with a particular focus on the role of visualization in the development of the scientific worldview
- 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
- Interactive graphs
- Testing data quality through graphs
- Telling a story
- High dimensional data visualization

Learning and teaching methods

Teaching will be delivered in a way that blends face-to-face classes, for those students that can be present on campus, with a range of online lectures, teaching, learning and collaborative support.



Assessment items, weightings and deadlines

Coursework / exam Description Deadline Weighting
Coursework   Coursework 1    50% 
Coursework   Coursework 2    25% 
Coursework   Coursework 3    25% 

Overall assessment

Coursework Exam
100% 0%


Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Andrew Harrison, email:
Dr Andew Harrison, Dr Osama Mahmoud & Dr Xinan Yang
Dr Andrew Harrison (, Dr Osama Mahmoud (, Dr Xinan Yang (



External examiner

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


Further information
Mathematical Sciences

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

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