simple data
visualizations
Tech Day 2015
why bother?
 long history of using pictures to show
significance of numbers
 increasingly “visual” society
 visual data can be interpreted and
consumed quickly
 using open-source, social tools, data can
be shared easily
in the classroom …
 particularly useful for classes where
students do not have “data” backgrounds
 encourages exploration of
different data visuals (from bar charts to
tree maps to “live” visualizations)
3 (really only 2)
primary examples
many eyes (IBM Watson)
datawrapper
tableau public
many eyes
 PROS:
 easy to use
 easy to distribute
 social, web-based
visualization
 can “show” evidence
by including data’s
URL
 CONS:
 transitioning to IBM
Watson Analytics
datawrapper
 PROS:
 easy to use
 fast results
 URL to share or embed
 “transpose data”
option
 CONS:
 only five chart
types
 free to try, but paid
for real use
(inexpensive)
tableau public
 PROS:
 more sophisticated
visualization
 more ways to
customize project
 can embed or share
 example datasets
(resources) & Sarah
Ryley
 CONS:
 can be more time
consuming to use
(depends on the
data set)
 though free, need
to download
software
others: infogr.am
 PROS:
 can embed in blogs
 can download and
share privately
 CON:
 restricted free
version; however
educational pricing
available
others: raw
 PROS:
 web app/open
source
 no registration
 lots of customization
 teaches a higher
level of
understanding
among data
elements
 CON:
 designed for
embedding into
websites
others: timeline
 PROS:
 open source
 linked to media
sources (YouTube,
Flickr, etc.) for easy
inclusion
 site tutorials/step-by-
step guides
 CON:
 designed for
embedding into
websites

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data visualization ~ tech day 2015

  • 2. why bother?  long history of using pictures to show significance of numbers  increasingly “visual” society  visual data can be interpreted and consumed quickly  using open-source, social tools, data can be shared easily
  • 3. in the classroom …  particularly useful for classes where students do not have “data” backgrounds  encourages exploration of different data visuals (from bar charts to tree maps to “live” visualizations)
  • 4. 3 (really only 2) primary examples many eyes (IBM Watson) datawrapper tableau public
  • 5. many eyes  PROS:  easy to use  easy to distribute  social, web-based visualization  can “show” evidence by including data’s URL  CONS:  transitioning to IBM Watson Analytics
  • 6. datawrapper  PROS:  easy to use  fast results  URL to share or embed  “transpose data” option  CONS:  only five chart types  free to try, but paid for real use (inexpensive)
  • 7. tableau public  PROS:  more sophisticated visualization  more ways to customize project  can embed or share  example datasets (resources) & Sarah Ryley  CONS:  can be more time consuming to use (depends on the data set)  though free, need to download software
  • 8. others: infogr.am  PROS:  can embed in blogs  can download and share privately  CON:  restricted free version; however educational pricing available
  • 9. others: raw  PROS:  web app/open source  no registration  lots of customization  teaches a higher level of understanding among data elements  CON:  designed for embedding into websites
  • 10. others: timeline  PROS:  open source  linked to media sources (YouTube, Flickr, etc.) for easy inclusion  site tutorials/step-by- step guides  CON:  designed for embedding into websites