Difference between revisions of "Lesson01"

From Visual Analytics and Applications
Jump to navigation Jump to search
(Created page with "<div style=background:#2B3856 border:#A3BFB1> 250px <font size = 5; color="#FFFFFF">ISSS608 Visual Analytics and Applications</font> </div> <!--MAIN HEADER...")
 
 
(11 intermediate revisions by the same user not shown)
Line 28: Line 28:
 
|}
 
|}
 
<br/>
 
<br/>
 +
 +
<Font size =5>'''Demystifying Visual Analytics'''</font>
 +
 +
<font size = 3>'''[https://dl.dropboxusercontent.com/u/1754725/ISSS608/Lesson01/index.html Lesson 1 slides]'''</font>
 +
 +
== Content ==
 +
 +
Introduction to the course
 +
*  Why this course?
 +
*  What does it cover?
 +
*  Who is involved?
 +
*  What assignments?
 +
*  Rules to be followed
 +
 +
Motivations of Visual Analytics
 +
*  Massive data
 +
*  Complex problem
 +
*  Visual Representation
 +
*  New visual paradigm
 +
*  Hidden insight
 +
 +
The Visual Analytics Framework
 +
*  The Science of Analytical Reasoning
 +
*  Sense-Making Methods
 +
*  Components of visual analytics
 +
*  History of visual analytics
 +
*  The visual analytics process
 +
*  Application challenges
 +
*  Technical challenges
 +
 +
A Gallery of Visual Analytics applications
 +
 +
Visualising and Analysing Univariate Data
 +
*  Data discovery with bar chart
 +
*  Data discovery with dotplot
 +
 +
Visualising and Analysing Bivariate Categorical Data</font>
 +
* Exploring two categorical variables
 +
* Working with mosaic plot and trellis
 +
* Bivariate categorical data analysis best practices
 +
 +
 +
== Hands-on Session ==
 +
 +
*
 +
 +
 +
== Daily Readings ==
 +
 +
{| border="1" cellpadding="1"
 +
|-
 +
|width="40pt"|Day
 +
|width="10pt"|Time required
 +
|width="400pt"|Readings
 +
|-
 +
|Day 1||60 mins||
 +
The best stats you've ever seen [http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen]'''Must view!'''
 +
 +
A Tour through the Visualization Zoo [http://queue.acm.org/detail.cfm?id=1805128]'''Must read!'''
 +
 +
|-
 +
|-
 +
|Day 2||60 mins||
 +
Why Visual Analytics [http://www.youtube.com/watch?v=5uGRGqCFryg]'''Must view!'''
 +
 +
Andrew Gelman and Antony Unwin (2011) Infovis and Statistical Graphics: Different Goals, Different Looks [http://www.stat.columbia.edu/~gelman/research/published/vis14.pdf]'''Must read!'''
 +
 +
Robert Kosara (2012) Visualization: It’s More than Pictures! [http://stat-computing.org/newsletter/issues/scgn-22-1.pdf]'''Must read!'''
 +
 +
|-
 +
 +
|Day 3||60 mins||
 +
Demystifying Visual Analytics, ''IEEE Computer Graphics and Applications, March/April 2009'' e-journal @smu library '''Must read!'''
 +
 +
Visual Analytics - Mastering the Information Age [http://www.youtube.com/watch?v=5i3xbitEVfs]'''Must view!'''
 +
 +
Visual analysis for everyone [http://www.tableausoftware.com/whitepapers/visual-analysis-everyone]
 +
|-
 +
 +
|Day 4||60 mins||
 +
 +
Tools for Visualising [http://www.visualisingdata.com/index.php/resources/]
 +
 +
The beauty of data visualization [http://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization.html]'''Must view!'''
 +
 +
Data Visualisation, Australian Bureau of Statistics Research Paper, July 2007 [http://www.abs.gov.au/ausstats/abs@.nsf/mf/1211.0.55.001]
 +
|-
 +
 +
|Day 5||3 hours||
 +
 +
Self-learning Tableau
 +
* Getting Started [http://www.tableau.com/learn/tutorials/on-demand/getting-started]
 +
* Tableau Interface [http://www.tableau.com/learn/tutorials/on-demand/tableau-interface]
 +
* Connecting to Data: From Getting Started with Data to Data Blending [http://www.tableau.com/learn/tutorials/on-demand/getting-started-data] 
 +
 +
|-
 +
|-
 +
|-
 +
|}
 +
 +
== References ==
 +
James J. Thomas & Kristin A. Cook (ed) (2005) Illuminating the Path: The Research and Development Agenda of Visual Analytics [http://vis.pnnl.gov/pdf/RD_Agenda_VisualAnalytics.pdf]
 +
 +
 +
== Discussion ==
 +
 +
[[Talk:Lesson01|Discussion Lesson 01]]

Latest revision as of 10:08, 14 August 2016

Vaa.jpg ISSS608 Visual Analytics and Applications

About

Weekly Session

Assignments

Visual Analytics Project

Course Resources

 


Demystifying Visual Analytics

Lesson 1 slides

Content

Introduction to the course

  • Why this course?
  • What does it cover?
  • Who is involved?
  • What assignments?
  • Rules to be followed

Motivations of Visual Analytics

  • Massive data
  • Complex problem
  • Visual Representation
  • New visual paradigm
  • Hidden insight

The Visual Analytics Framework

  • The Science of Analytical Reasoning
  • Sense-Making Methods
  • Components of visual analytics
  • History of visual analytics
  • The visual analytics process
  • Application challenges
  • Technical challenges

A Gallery of Visual Analytics applications

Visualising and Analysing Univariate Data

  • Data discovery with bar chart
  • Data discovery with dotplot

Visualising and Analysing Bivariate Categorical Data

  • Exploring two categorical variables
  • Working with mosaic plot and trellis
  • Bivariate categorical data analysis best practices


Hands-on Session


Daily Readings

Day Time required Readings
Day 1 60 mins

The best stats you've ever seen [1]Must view!

A Tour through the Visualization Zoo [2]Must read!

Day 2 60 mins

Why Visual Analytics [3]Must view!

Andrew Gelman and Antony Unwin (2011) Infovis and Statistical Graphics: Different Goals, Different Looks [4]Must read!

Robert Kosara (2012) Visualization: It’s More than Pictures! [5]Must read!

Day 3 60 mins

Demystifying Visual Analytics, IEEE Computer Graphics and Applications, March/April 2009 e-journal @smu library Must read!

Visual Analytics - Mastering the Information Age [6]Must view!

Visual analysis for everyone [7]

Day 4 60 mins

Tools for Visualising [8]

The beauty of data visualization [9]Must view!

Data Visualisation, Australian Bureau of Statistics Research Paper, July 2007 [10]

Day 5 3 hours

Self-learning Tableau

  • Getting Started [11]
  • Tableau Interface [12]
  • Connecting to Data: From Getting Started with Data to Data Blending [13]

References

James J. Thomas & Kristin A. Cook (ed) (2005) Illuminating the Path: The Research and Development Agenda of Visual Analytics [14]


Discussion

Discussion Lesson 01