- Content Description:
- Visualization deals with all aspects that are connected with the visual
representation of data sets from scientific experiments, simulations, medical
scanners, databases, web system, and the like in order to achieve a deeper
understanding or a simpler representation of complex phenomena and to extract
important information visually. To obtain these goals, both well-known
techniques from the field of interactive computer graphics and completely new
methods are applied. The objective of the course is to provide knowledge about
visualization algorithms and data structures as well as acquaintance with
practical applications of visualization. Through several projects the student is
expected to learn methods to explore and visualize different kinds of data sets.
- Introduction and historical remarks
- Abstract visualization concepts and the visualization pipeline
- Data acquisition and representation (sampling and reconstruction; grids and data structures).
- Basic mapping concepts
- Visualization of scalar fields (isosurface extraction, volume rendering)
- Visualization of vector fields (particle tracing, texture-based methods, vector field topology)
- Tensor fields, multi-attribute data, multi-field visualization
- Human visual perception + Color
- Space/Order + Depth/Occlusion
- Focus+Context; Navigation+Zoom
- Visualization of graphs and trees and high-dimensional data
- Evaluation + Interaction models
Be warned that this is mostly just a collection of links to articles and demos by smarter people than I. Areas of interest include Java, C++, Scala, Go, Rust, Python, Networking, Cloud, Containers, Machine Learning, the Web, Visualization, Linux, System Performance, Software Architecture, Microservices, Functional Programming....
Wednesday, 15 April 2015
Torsten Möller: Data Visualization Course
http://www2.cs.sfu.ca/~torsten/Teaching/Cmpt467/
Labels:
data,
data science,
visualisation,
visualization
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