Exploratory Data Analysis and Data Visualisation
Postgraduate: Level 7
Thursday 03 October 2019
Saturday 14 December 2019
01 October 2019
Requisites for this module
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.
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
No information available.
No information available.
No additional information available.
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
Module supervisor and teaching staff
Dr Fanlin Meng, email: firstname.lastname@example.org.
Dr Fanlin Meng (email@example.com) and Dr Andrew Harrison (firstname.lastname@example.org)
Dr Fanlin Meng (email@example.com)
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).
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