Exploratory Data Analysis and Data Visualisation

The details
Mathematical Sciences
Colchester Campus
Postgraduate: Level 7
Thursday 03 October 2019
Saturday 14 December 2019
01 October 2019


Requisites for this module



Key module for


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.


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.

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


- 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
- Telling a story

Module aims

No information available.

Module learning outcomes

No information available.

Module information

No additional information available.

Learning and teaching methods

Weeks: 2 and 3: 1 Lecture, 1 PC lab Weeks: 4 -9: 2 pc labs Week: 10, 3 practical pc labs Week 11: 3 lectures


This module does not appear to have any essential texts. To see non-essential items, please refer to the module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Coursework 1  31/10/2019  30% 
Coursework   Coursework 2  06/12/2019  45% 
Coursework   Coursework 3  12/12/2019  25% 

Additional coursework information

Coursework comprises two pieces of coursework, the first worth 30%, the second worth 45%, plus a group project worth 25%.

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 Fanlin Meng, email:
Dr Fanlin Meng ( and Dr Andrew Harrison (
Dr Fanlin Meng (



External examiner

No external examiner information available for this module.
Available via Moodle
Of 82 hours, 20 (24.4%) hours available to students:
62 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).


Further information
Mathematical Sciences

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