MA304-6-SP-CO:
Data Visualisation
2021/22
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Spring
Undergraduate: Level 6
Current
Monday 17 January 2022
Friday 25 March 2022
15
28 October 2021
Requisites for this module
(none)
(none)
(none)
(none)
(none)
BSC 5B43 Statistics (Including Year Abroad),
BSC 9K12 Statistics,
BSC 9K13 Statistics (Including Placement Year),
BSC 9K18 Statistics (Including Foundation Year),
BA Q120 Linguistics with Data Science,
BA Q121 Linguistics with Data Science (Including Foundation Year),
BA Q122 Linguistics with Data Science (Including Placement Year),
BA Q123 Linguistics with Data Science (Including Year Abroad)
In 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.
For data analysis and visualisations we will use R-studio, ggplot2 and plotly packages, as well as google visualisations and 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. The module will highlight how visualization plays a key role in many disciplines.
At the end of the module students will 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. Text and Sentiment and visualization
Syllabus
1. Historical examples of visualization
2. Cognition linked to visualization including linguistics, mathematics, natural sciences, art and wider cultural topics
3. Data Visualization for Human Perception
4. What makes a good graph – What makes a bad graph
5. Examining variables and basic R charts
6. Exploring relationships, looking for structure
7. Advanced plots with ggplot2
8. Creating statistical reports with Rmarkdown
9. Interactive graphs
10. Testing data quality through graphs
11. Text,Sentiment and Natural Language visualization
12. Data visualization within Industry
13. Telling a story
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.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
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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 Andrew Harrison, email: harry@essex.ac.uk.
Dr Andrew Harrison & Dr Osama Mahmoud
Dr Andrew Harrison (harry@essex.ac.uk), Dr Osama Mahmoud (o.mahmoud@essex.ac.uk)
No
No
No
Prof Fionn Murtagh
University of Huddersfield
Professor of Data Science
Dr Yinghui Wei
University of Plymouth
Available via Moodle
Of 1769 hours, 7 (0.4%) hours available to students:
1762 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
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