Visual Analytics for Data Scientists 2015
1. Fundamentals of Visual Analytics
This session will define visual analytics and show why visualisation is important. It will provide the basic principles of the visual representation of data, the types of tasks that visual analytics can support and the consequent requirements for analysis-supporting visual displays.
By end of the session you'll be able to interpret different types of data display, use various interactive operations designed to support data exploration, and will be acquainted with the software system V-Analytics.
2. Data structures and types
This session will introduce the major types of data structures will you will encounter, the analytical tasks they support and the methods needed to do visual data analysis.
3. Complex data structures. Use of clustering
This session will show how you can use clustering as an instrument for interactive visual analysis.
4. Space and Time
This session will consider types of spatial and temporal data including origin-destination data.
5. Analysis of mobility (movement data)
This session will introduce you to analysing trajectories of moving objects.
By the end of the session, you'll be able to: understand the difference between quasi-continuous and episodic movement data and the implications for analysis; identify stops in trajectories; how to divide trajectories into trips; extract other movement events from trajectories; spatially summarise and abstract movement; transform into spatial time series; use density-based clustering with them.
6. Further abilities and topics of visual analytics
This session will discuss predictive visual analytics, get an overview of existing visual analytics approaches to analysing other types of data (networks, images, videos, texts), list visual analytics software and wrap up.
Data import and export in V-Analytics:
Example data sets for practicals:
This module is a part of the Data Science MSc course at City University London, January-April 2015
last updated: April 2015