[Sigkm-l] "Data Analysis, Modeling and Visualization" Tutorial on 01/16/2005

[Katy Borner]

===================================================
Data Analysis, Modeling and Visualization
Half Day Tutorial at Electronic Imaging 2005
http://vw.indiana.edu/damv-tutorial05/
===================================================


      Instructors

Katy Börner <http://ella.slis.indiana.edu/%7Ekaty/> <katy at indiana.edu 
<mailto:%20katy at indiana.edu>>
Indiana University, USA

Chaomei Chen <http://www.pages.drexel.edu/%7Ecc345/> 
<Chaomei.Chen at cis.drexel.edu <mailto:Chaomei.Chen at cis.drexel.edu>>
Drexel University, USA


      Time & Place

Sunday, Jan 16th, 2005, 1:30p to 5:30p
San Jose Marriott attached to the San Jose Convention Center, San Jose, CA.


      Description

This half day course introduces commonly used data analysis, modeling 
and visualization techniques. Algorithms available via the InfoVis 
Cyberinfrastructure at http://iv.slis.indiana.edu will be used for 
demonstration purposes. The course will also include walk-throughs of 
case studies of identifying the trends and significant changes in 
scientific literatures using CiteSpace 
<http://cluster.cis.drexel.edu/%7Ecchen/citespace>.

Amongst other things, the course will cover:

    * Visual Perception Principles
    * Time Series Analysis
    * Visualizing Tabular Data
    * Visualizing Tree Data
    * Semantic Data Analysis
    * Network Modeling & Visualization
    * Clustering Algorithms
    * Interaction and Distortion Techniques

See also the existing learning modules available at 
http://iv.slis.indiana.edu/lm. Data sets from a variety of fields such 
as Crime, Social Science, Ecology, Finance, Health, Meteorology, 
Physics, Sales, Sports, etc. will be used to exemplify and contrast 
diverse algorithms.


      Benefits

Upon taking this course, participants will be able to:

    * Identify tasks that can be supported by data analysis and
      visualization.
    * Describe and use major data analysis, modeling and visualization
      techniques.
    * Select and combine appropriate techniques/systems for different
      application scenarios.
    * Judge the potential and limitations of data analysis results and
      visualizations.

More details can be found at http://vw.indiana.edu/damv-tutorial05/

--
Katy Borner, Assistant Professor
Information Science & Cognitive Science
Indiana University, SLIS
10th Street & Jordan Avenue     Phone:  (812) 855-3256   Fax: -6166
Main Library 019                E-mail: katy at indiana.edu
Bloomington, IN 47405, USA      WWW:    ella.slis.indiana.edu/~katy

Check out the new InfoVis Lab Gallery at
http://ella.slis.indiana.edu/~katy/gallery/

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