MA304-7-SP-CO:
Data Visualisation
2023/24
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Spring
Postgraduate: Level 7
Current
Monday 15 January 2024
Friday 22 March 2024
15
08 February 2024
Requisites for this module
(none)
(none)
(none)
(none)
(none)
MSC G30412 Data Science,
MSC G30424 Data Science,
MSC G304PP Data Science with Professional Placement,
DIP G20109 Optimisation and Data Analytics,
MSC G20312 Optimisation and Data Analytics,
MSC G30624 Data Science and its Applications,
MPHDG20048 Operational Research,
PHD G20048 Operational Research,
MPHDG30448 Data Science,
PHD G30448 Data Science
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 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. We will also explore the use of visualizations linked to textual analysis.
The aims of this module are:
- to create data analysts that can identify patterns and display information from data of several 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.
By the end of the module, students will be expected to:
- Summarise and understand information on text, categorical and continuous 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 plots
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
Text, Sentiment and Natural Language visualization
Data visualization within industry
Telling a story
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.
The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's
reading list.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Assignment |
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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
Reassessment
Module supervisor and teaching staff
Dr Vasileios Giagos, email: v.giagos@essex.ac.uk.
Dr Vasileios Giagos, Dr Rishideep Roy
v.giagos@essex.ac.uk
No
No
No
Dr Yinghui Wei
University of Plymouth
Dr Murray Pollock
Newcastle University
Director of Statistics / Senior Lecturer
Available via Moodle
Of 67 hours, 31 (46.3%) hours available to students:
36 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.
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