Visual Analytics for Data Scientists 2016

1. Introduction to Visual Analytics and Visualisation [lecture]

2. Using partition-based clustering in Visual Analytics [lecture]

3. Visual analytics of spatio-temporal data [lecture], [practicals]

4. Use of density-based clustering [lecture], [practicals]

5. Analysis of mobility (movement data) [lecture], [practicals]

6. Further abilities and topics of visual analytics [lecture]

Further links:
Recommended software: V-Analytics, Mondrian, Tableau
Data import and export in V-Analytics: [practicals]
Example data sets for practicals: [zip]

This module is a part of the Data Science MSc course at City University London, January-April 2016
http://www.city.ac.uk/courses/postgraduate/data-science-msc
last updated: April 2016

Visual Analytics for Data Scientists 2015

1. Fundamentals of Visual Analytics [lecture], [practicals]
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 [lecture], [practicals]
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 [lecture], [practicals]
This session will show how you can use clustering as an instrument for interactive visual analysis.

4. Space and Time [lecture], [practicals]
This session will consider types of spatial and temporal data including origin-destination data.

5. Analysis of mobility (movement data) [lecture], [practicals]
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 [lecture]
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.

Further links:
Recommended software: V-Analytics, Mondrian, Tableau
Data import and export in V-Analytics: [practicals]
Example data sets for practicals: [zip]

This module is a part of the Data Science MSc course at City University London, January-April 2015
http://www.city.ac.uk/courses/postgraduate/data-science-msc
last updated: April 2015

Visual Analytics

1. Introduction to GeoVisualization. Special focus: spatio-temporal and movement data slides
2. An Introduction to Visual Analytics. Special focus: movement data slides

Older version:
Lecture 1. From Visual Data Mining towards Visual Analytics slides
Lecture 2. Visual Analytics of Movement slides

These lectures are regularly presented at Univ. Bonn
as a part of the Introductory course to machine learning and data mining
last updated: July 2014

Geospatial Visual Analytics

Lecture 1. Introduction to Visual Analytics slides
Lecture 2. Interactive Maps and Multiple Coordinated Views in Geospatial Visual Analytics slides
Lecture 3. Data Transformations for Geospatial Visual Analytics slides
Lecture 4. Visual Analysis of spatio-temporal Data (time series) slides
Lecture 5. Visual Analysis of spatio-temporal Data (events) slides
Lecture 6. Visual Analysis of spatio-temporal Data (behaviors of moving objects) slides
Lecture 7. Visual Analysis of spatio-temporal Data (collective movement) slides
Lecture 8. Spatial Decision Support slides
Concluding Remarks slides

This course has been presented at various occasions including
KTH Stockholm (October 2008), EPFL Lausanne (June 2009),
HFT Stuttgart (July 2009), Univ. Gavle (August 2009)
See also tutorial at GIScience 2010 Zuirch (September 2010)

Questions and comments:
Natalia and Gennady Andrienko http://geoanalytics.net/and