This document discusses visual analytics and why visualizing data is important. It provides examples of different types of visualizations including charts, dashboards, networks and maps. It discusses key aspects of visual analytics like visual mapping, interaction techniques, and tools. It also covers challenges of big data visualization and provides examples of data visualization resources and projects.

1. VISUAL ANALYTICS
AWALIN SOPAN
@awalinsopan
Research Scientist, MicroStrategy Inc
ACM BUET Chapter
Department of Computer Science and Engineering,
BUET

2. Why visualize data?

3. Anscombe’s Quartet
1 2 3 4
x y x y x y x y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89

4. Anscombe’s Quartet
1 2 3 4
x y x y x y x y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89
Property Value
Mean of x 9.0
Variance of x 11.0
Mean of y 7.5
Linear regression y = 3 + 0.5x

5. Anscombe’s Quartet

6. Charts

7. Stock Market Price Trend

8. Marvel superhero network

9. Friend Network

11. Interactive Dashboard Dashboard
Hans RoslingStorytelling with Data

12. Visual Analytics:
-beyond numbers, static graphs and charts
 Use interactive visual interface
 See the overview
 Find pattern, trend, outlier, correlation
 Sort by rank, group similar things
 Make decision or ask more questions

14. Visual Mapping
 Size
 Shape
 Color
 Position
 Orientation
Attribute
value,
relation

15. Multivariate: scatter plot, bubble chart, parallel
coordinate, table
Relational: Network diagram (nodes and edges)
Temporal: Line chart
Geo-spatial: Map
Hierarchical: Tree, treemap
Part of whole: Pie chart
Data Types

17. Interaction
Sort
Filter
Drill down
Cluster

18. Tools
 MicrosStrategy
 Tableau
 SpotFire
 ManyEyes
 NodeXL
 Gephi
 D3: JavaScript library
Commercial tool for
Business data analysis

19. Information Visualization Mantra
Ben Shneiderman
Overview: see the big picture
Zoom and Filter: select only relevant data
Details on Demand: gain more details of the
selected data point

20. Projects

21. Distributions
-movie rating
-height/weight/mass
How can we compare rows of
distributions in limited screen
space?

22. Visual Representation

23. Visual Representation

24. Saves space, Enables stacking
Movie rating distributions
Each row a movie
From Height to Color

25. Jurassic park: uniform distribution Students vsTeachers

26. Online activity distribution
 Post
 Reply
 Invite
 Report
 Accept request
Each row = one
community’s
members’ activities
500+ communities
10k+ members

27. Community leaders’ network

28. Community Health Map

29. Conference Monitor

30. Twitter conversation

31. #Shahbag

32. Bridge node

34. G-Pare

35. Computer Science:
Algorithm, visual coding,
clustering, predication
alg, data structure
HCI/UX: Enhance user
experience, design
readable viz, make
meaningful navigation
Software engineering:
Enhance performance,
develop app,
dashboard, HTML, js,
java, iOS, openGL

36. Big data
More data (more rows)
Various types (more columns)

37. Challenge of Big Data Visualization
 Performance
 Understand the data
 Meaningful grouping
 Context
 Data quality
 Detecting outliers
 Consolidating various data sources
 Various platforms

38. Census:
https://www.census.gov/dataviz/
Census for STEM:
http://www.census.gov/dataviz/visualizations/stem/stem-html/
NY times:
http://www.nytimes.com/upshot/ Mostly analysis, but they also provide interactive viz
http://www.nytimes.com/interactive/2014/upshot/mapping-the-spread-of-drought-across-the-
us.html
Blogs:
http://datastori.es/
http://fellinlovewithdata.com/
Visualcomplexity
Flowing Data: http://flowingdata.com/
http://www.visualizing.org/
http://www.tableausoftware.com/public/gallery
List of tools: http://selection.datavisualization.ch/
Twitter visualizations: http://twitter.github.io/interactive/newyear2014/

39. Acknowledgement
BUET ACM Chapter
@awalinsopan