SlideShare a Scribd company logo
1 of 91
Download to read offline
INTRODUCTION TO
DATA VISUALIZATION
February 3, 2015Hunter Whitney
1
DRAFT
INTRODUCTION
HUNTER WHITNEY
2
! UX Design and Data Visualization Consultant
! Author and Contributing Editor
! @hunterwhitney"
INTRODUCTION
HELLO!
‣ Who are you?
‣ What do you do?
‣ What’s your learning goal for today?
‣ Is there a topic you’d like to
visualize in the exercise today?
3
Sections:
1) What is Data Visualization?
2) Data Visualization Purposes
3) Data and Design
4) People and Process
5) Examples to Discuss
6) Class Exercise
7) Resources and Conclusions
4
CLASS EXERCISE PRELIMINARIES
DISCUSSION
Toward the end of class, we’re going to split up into groups and create data visualization concept
designs. As we go through each section, think about applying the ideas we cover to a project you might
choose.
Topic suggestion for the final exercise - create a visualization that shows how a series of events
unfolds over time. Be creative. It doesn’t have to be just a timeline on an x-axis.
This can be applied to many areas including - business (e.g., patterns of timing from VC funding to
IPO), sports (e.g., changes ball possession during a game), medicine (e.g., the spread of an epidemic)
START THINKING…
5
KEY QUESTIONS TO ADDRESS IN YOUR PROJECTS
‣ What is the purpose/value of the visualization?
‣ Who are the intended users?
‣ How was the data selected and acquired?
‣ What design elements were used and why?
CLASS EXERCISE PRELIMINARIES 6
! We’re only scratching the
surface of every topic
presented here
! The main goal is for you to
look at data visualization
with a holistic perspective
! Whatever your levels of
skill and experience are,
you have something to
offer
KEEP IN MIND… 7
INTRODUCTION TO DATA VISUALIZATION
SECTION 1: WHAT IS
DATA VISUALIZATION?
8
9
VISUALIZATIONS MAKE IT EASIER TO SEE
PATTERNS IN DATA
SECTION 1: WHAT IS DATA VISUALIZATION?
http://data.oecd.org/healthcare/child-vaccination-rates.htm
The key to effectively exposing
meaningful patterns in data comes
down to thoughtful visual encoding.
http://www.gapminder.org/
SECTION 1: WHAT IS DATA VISUALIZATION? 10
720349656089226535931140790070
322302076958689027429003358787
115045223998424533087922668417
382319480046553364246202505406
711172160430997890121737608183
566145635519888049583302306957
749597705315240714467203496560
892265359311407900703223020769
586890274290033587871150452239
984245330879226684173823194800
465533642462025054067111721604
309978901217376081835661456355
How does encoding work?
Guess how many ‘7’s there
are in this set-
SECTION 1: WHAT IS DATA VISUALIZATION? 11
720349656089226535931140790070
322302076958689027429003358787
115045223998424533087922668417
382319480046553364246202505406
711172160430997890121737608183
566145635519888049583302306957
749597705315240714467203496560
892265359311407900703223020769
586890274290033587871150452239
984245330879226684173823194800
465533642462025054067111721604
309978901217376081835661456355
They’re the same set of
numbers, but now the
7’s pop out at us.
Now, try guessing again-
SECTION 1: WHAT IS DATA VISUALIZATION? 12
720349656089226535931140790070
322302076958689027429003358787
115045223998424533087922668417
382319480046553364246202505406
711172160430997890121737608183
566145635519888049583302306957
749597705315240714467203496560
892265359311407900703223020769
586890274290033587871150452239
984245330879226684173823194800
465533642462025054067111721604
309978901217376081835661456355
Effective visualizations
require thoughtful
encoding.
SECTION 1: WHAT IS DATA VISUALIZATION? 13
Design decisions have a
big impact on what
people will see in the
data.
SECTION 1: WHAT IS DATA VISUALIZATION? 14
720349656089226535931140790070
720349656089226535931140790070
A substantial portion of the human brain is devoted to visual processing
Source:

http://www.flickr.com/photos/orangeacid/234358923/

Creative Commons Attribution License

Source:

http://en.wikipedia.org/wiki/File:Brodmann_areas_17_18_19.png

GNU Free Documentation License
WE ARE WIRED FOR VISUALIZATION
10 Million Bits
Per Second
Source:

Current Biology (July 2006) by Judith McLean
and Michael A. Freed
SECTION 1: WHAT IS DATA VISUALIZATION? HUMAN BRAIN 15
TAPPING IN TO OUR PERCEPTUAL POWERS
The pop-out effects are due to your brain’s pre-attentive processing
SECTION 1: WHAT IS DATA VISUALIZATION? PRE-ATTENTIVE PROCESSING 16
COLOR HUE ORIENTATION TEXTURE POSITION & ALIGNMENT
COLOR BRIGHTNESS COLOR SATURATION SIZE SHAPE
What is easier to
distinguish here - color
or shape differences?
Some attributes pop out more
than others.
17SECTION 1: WHAT IS DATA VISUALIZATION? PRE-ATTENTIVE PROCESSING
http://www.slideshare.net/slideshow/view?login=johnwhalen&title=cognitive-science-of-design-in-10-minutes-or-less
SECTION 1: WHAT IS DATA VISUALIZATION? PRE-ATTENTIVE PROCESSING
SHAPE
18
http://www.slideshare.net/slideshow/view?login=johnwhalen&title=cognitive-science-of-design-in-10-minutes-or-less
SECTION 1: WHAT IS DATA VISUALIZATION? BRAIN SYSTEMS 19
SECTION 1: DATA VISUALIZATION PROCESS AND PRACTICES
Adapted from Stephen Few.
20
PUTTING THE PIECES TOGETHER
The components of visualizations fit into a larger context of goals, users,
and the media in which they are presented.
SECTION 1: WHAT IS DATA VISUALIZATION? BUILDING OUT 21
SECTION 2: DATA
VISUALIZATION
PURPOSES
INTRODUCTION TO DATA VISUALIZATION 22
Overview first, zoom and filter, then details-on-demand.
‣ Time Series and Event Sequences
‣ Part-to-Whole
‣ Geospatial
‣ Ranking
‣ Distribution
‣ Correlation
‣ Deviation
‣ Nominal Comparison
There can be overlaps in what can be shown and related
in one visualization
I CAN RELATE!
SECTION 2: DATA VISUALIZATION PURPOSES 23
24
TIME-SERIES GRAPH
SECTION 2: DATA VISUALIZATION PURPOSES
http://www.businessinsider.com/india-and-america-come-meet-mum-2015-1
25
STREAMGRAPH
SECTION 2: DATA VISUALIZATION PURPOSES
26
TEMPORAL HEATMAP
SECTION 2: DATA VISUALIZATION PURPOSES
SECTION 2: DATA VISUALIZATION USES 27
EARLY EXAMPLES
28
NEAR REAL-TIME DATA
SECTION 2: DATA VISUALIZATION PURPOSES
29
MORE TIME EXAMPLES
SECTION 2: DATA VISUALIZATION PURPOSES
30
FOR A DEEPER DIVE INTO
TEMPORAL DATA VIS..
http://www.oreilly.com/pub/e/3139
http://uxmag.com/articles/its-about-time
SECTION 2: DATA VISUALIZATION PURPOSES
Overview first, zoom and filter, then details-on-demand.
PART-TO-WHOLE: A TREEMAP OF TITANIC PROPORTIONS
SECTION 2: DATA VISUALIZATION PURPOSES 31
Overview first, zoom and filter, then details-on-demand.
Source: http://blog.visual.ly/the-whole-story-on-part-to-whole-relationships/
PART-TO-WHOLE: OTHER EXAMPLES
SECTION 2: DATA VISUALIZATION PURPOSES 32
* Source: http://blog.visual.ly/the-whole-story-on-part-to-whole-relationships/
**
Pie Stacked Area
Parallel Sets Sankey Diagram
FRUIT TREEMAPS: HIERARCHY AND PROPORTIONS
SECTION 2: DATA VISUALIZATION PURPOSES 33
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
34SECTION 2: DATA VISUALIZATION PURPOSES
GEOSPATIAL: THE POLITICAL LANDSCAPE
GEOSPATIAL: EARLY EXAMPLE
Source:"
http://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak"
SECTION 2: DATA VISUALIZATION PURPOSES 35
http://uxmag.com/articles/leveraging-the-kano-model-for-optimal-results
RANKING
36SECTION 2: DATA VISUALIZATION PURPOSES
37
http://datavizblog.com/category/distribution/
SECTION 2: DATA VISUALIZATION PURPOSES
DISTRIBUTION
38
http://www.statsblogs.com/2014/08/20/creating-heat-maps-in-sasiml/
CORRELATION
SECTION 2: DATA VISUALIZATION PURPOSES
39SECTION 2: DATA VISUALIZATION PURPOSES
DEVIATION
SECTION 2: DATA VISUALIZATION PURPOSES 40
NOMINAL COMPARISON: BAR CHART
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
41
DIFFERENT PERSPECTIVES: NOMINAL COMPARISON AND
PART-TO-WHOLE
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 2: DATA VISUALIZATION PURPOSES
CLASS EXERCISE (KEEP IN MIND)
DISCUSSION KEY QUESTIONS TO ADDRESS
‣ What are the main functions
(e.g., exploratory, tracking,
explanatory, etc.?)
‣ What kinds of design elements
might you want to use?
‣ What level of interactivity
might be good to include?
For whichever subject area you choose, think about the
basic design elements and functions that might work
best. These questions will come into sharper focus as
you learn more about the goals of the users.
CONSIDERATIONS FOR YOUR CLASS PROJECT
42
SECTION 3: DATA AND
DESIGN
INTRODUCTION TO DATA VISUALIZATION 43
http://phys.org/news/2013-10-visualization.html
THERE ARE ENDLESS FORMS OF VISUALIZATION
SECTION 3: DATA AND DESIGN 44
THE MARRIAGE OF DESIGN AND DATA
DATA CAN BE BROKEN INTO TWO MAJOR CLASSES: DISCRETE AND CONTINUOUS
45
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 3: DATA AND DESIGN
THE MARRIAGE OF DESIGN AND DATA
46
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 3: DATA AND DESIGN
Nominal Scale: This is simply putting items
together without ordering or ranking them (e.g.,
an apple, an orange, and a tomato).
Ordinal Scale: Elements of the data describe
properties of objects or events that are ordered by
some characteristic.
THE MARRIAGE OF DESIGN AND MEASUREMENTS
47
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 3: DATA AND DESIGN
Interval Scale: These are data that are
measured on some kind of scale, often
temporal (e.g., the days of the week, hours of
the day).
THE MARRIAGE OF DESIGN AND MEASUREMENTS
Ratio Scale: An ordered series of numbers
assigned to items (objects, events, etc.)
that allow for estimating and comparing
different measures in terms of multiples,
such as “half as many” or “four times as
heavy.”
48
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 2: DATA VISUALIZATION PURPOSES
STATISTICAL SUMMARIZATION AND ANALYSIS
Visualizations can clarify or obscure the statistical summarization of
http://blog.visual.ly/using-visual-reasoning-to-understand-numbers/
49SECTION 3: DATA AND DESIGN
50
Source: Reprinted in Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 3: DATA AND DESIGN
CHART
EFFECTIVENESS
Source:
Enrico Bertini, Assistant Professor at NYU-Poly (@filwd)
51SECTION 3: DATA AND DESIGN
Think about good design practices: selective labeling
52
Schwabish, Jonathan A. 2014. "An Economist's Guide to Visualizing Data." Journal of Economic Perspectives, 28(1): 209-34. DOI: 10.1257/jep.28.1.209
SECTION 3: DATA AND DESIGN
Which one
is bigger?
A B
A
B
53
Think about good design practices: proximity
SECTION 3: DATA AND DESIGN
Think about good design practices: multiples
54
Schwabish, Jonathan A. 2014. "An Economist's Guide to Visualizing Data." Journal of Economic Perspectives, 28(1): 209-34. DOI: 10.1257/jep.28.1.209
SECTION 3: DATA AND DESIGN
55SECTION 3: DATA AND DESIGN
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
COLOR AND VALUE
http://blog.visual.ly/building-effective-color-scales/
YOUR VISUAL SYSTEM
56
http://www.lottolab.org/articles/illusionsoflight.asp http://adaynotwasted.com/2010/02/light-and-color-illusionsgin-art/
SECTION 3: DATA AND DESIGN
57
CONSTANCY
SECTION 3: DATA AND DESIGN
Idea: Forms or patterns transcend the stimuli used to
create them.
Why do patterns emerge?
Under what circumstances?
Principles of Pattern Recognition:
“Gestalt” is German for “pattern” or “form,
configuration”.
GESTALT PRINCIPLES
http://sixrevisions.com/web_design/gestalt-principles-applied-in-design/http://graphicdesign.spokanefalls.edu/tutorials/process/gestaltprinciples/gestaltprinc.htm
58SECTION 3: DATA AND DESIGN
What do you see here?
http://sixrevisions.com/web_design/gestalt-principles-applied-in-design/
59SECTION 3: DATA AND DESIGN
‣ How do you design the “perfect” visualization?
‣ There’s no perfect visualization: the design space is just too big!
‣ But it’s up to you to design the one that fits...
60SECTION 3: DATA AND DESIGN
! Visualization Display Choices
http://scitechdaily.com/scientists-manage-flood-big-data-space/ http://www.steema.com/tags/mobile
61SECTION 3: DATA AND DESIGN
A FEW DATA
VISUALIZATION
DEVELOPMENT
TOOLS:
62SECTION 3: DATA AND DESIGN
SECTION 4: PEOPLE AND
PROCESS
INTRODUCTION TO DATA VISUALIZATION 63
SECTION 4: PEOPLE AND PROCESS 64
http://cnr.ncsu.edu/geospatial/wp-content/uploads/sites/6/2014/02/earth_observation-574_crop1-1500x600.jpg
VISUALIZATION IS ONLY THE TIP
OF THE ICEBERG
Data visualization is only a part of a
much larger process that includes
identifying the purpose of the
visualization, the kinds of people who
will use it, the types of data that can
be collected and analyzed, and good
design choices.
65SECTION 4: PEOPLE AND PROCESS
VISUALIZATION IS
PART OF AN
ITERATIVE PROCESS
66
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 4: PEOPLE AND PROCESS
PERSPECTIVE: BIOTECHNOLOGY EXECUTIVE
67
‣ “We usually have an underlying narrative or hypothesis that is driving the
analysis, but even with that you have to be ready for a surprise. Be willing to
go where the data leads you, provided you have good data from multiple
sources.”
‣ “We try to have teams involved in the data collection and analysis process
‘from soup to nuts’. If people join only at the end of the process, you could be
setting yourself up for failure.”
‣ “If you rely on just one data set, you can be totally misled.”
SECTION 4: PEOPLE AND PROCESS
ROLE
• RESEARCHER
• PUBLIC
PRIOR KNOWLEDGE
• NONE
• SUBJECT EXPERT
USE FREQUENCY
• ONCE A DECADE
• EVERY HOUR
USERS
USER QUESTION 1 - WHO VIEWS THE DATA?
68SECTION 4: PEOPLE AND PROCESS
PURPOSE
HYPOTHESIS?
• WHAT ARE WE

TRYING TO LEARN OR
SHOW?
• HOW DO WE KNOW

IF WE ACHIEVED IT?
GOAL?
• WHAT ARE THE

BOUNDARIES?
PARAMETERS?
69SECTION 4: PEOPLE AND PROCESS
DATA QUESTION 1 - WHO OWNS IT?
PRIMARY
• YOU COLLECT IT
• YOU OWN IT
• NOBODY ELSE HAS IT
• OTHERS COLLECT IT
• OTHERS OWN IT
• OTHERS HAVE IT
SECONDARY
DATA
70SECTION 4: PEOPLE AND PROCESS
DATA QUESTION 2 - DOES IT CHANGE?
DYNAMIC
• CHANGES OFTEN
• COLLECTED OFTEN
• TIME WINDOW

MATTERS
• DOES NOT CHANGE
• COLLECT IT ONCE
• TIME WINDOW

MATTERS
STATIC
DATA
71SECTION 4: PEOPLE AND PROCESS
72
“Applied field ethnography”, data, and map visualizations
SECTION 4: PEOPLE AND PROCESS
USER CONTROL:
HIGH
STATIC
EXPLAINEXPLORE
(e.g., data-intensive research
applications)
(e.g., print infographic
advocacy )
(e.g., interactive infographic
journalism)
(e.g., data-rich visualizations with
limited interactivity)
DYNAMIC
USER CONTROL:
LOW
73SECTION 4: PEOPLE AND PROCESS
SECTION 5: EXAMPLES
TO DISCUSS
INTRODUCTION TO DATA VISUALIZATION 74
SECTION 5: EXAMPLES TO DISCUSS 75
After Nate Silver moved on to other things,
New York Times filled the gap with a data-
centric journalism section called “The
Upshot.”
Let’s discuss, deconstruct, and critique a few
examples from the site. These are screen
shots to you may not have full context, but
let’s see how these visualizations stand up.
You might want to visit the site and play with
it more on your own and practice evaluation
it based on what we’ve already discussed.
http://www.nytimes.com/upshot/
76
http://www.nytimes.com/interactive/2014/07/08/upshot/how-the-year-you-were-born-influences-your-politics.html?abt=0002&abg=1
SECTION 5: EXAMPLES TO DISCUSS
77SECTION 5: EXAMPLES TO DISCUSS
http://www.nytimes.com/newsgraphics/2014/senate-model/
78SECTION 5: EXAMPLES TO DISCUSS
79
http://www.nytimes.com/interactive/2014/upshot/buy-rent-calculator.html?abt=0002&abg=0
SECTION 5: EXAMPLES TO DISCUSS
80
https://source.opennews.org/en-US/articles/nyts-512-paths-white-house/
SECTION 5: EXAMPLES TO DISCUSS
SECTION 6: CLASS
EXERCISE
INTRODUCTION TO DATA VISUALIZATION 81
‣ Get into groups 4 or more, and discuss the ideas and examples you
have in mind.
‣ Then...
• Select the purpose, audience, and data you want to use for a
visualization
• Design the visualization on the provided poster paper
• Be ready to share your results and describe your thought process
EXERCISE IDEA: THINK TIME
82SECTION 6: CLASS EXERCISE
StreamgraphSpace Time CubeGantt Chart
83SECTION 6: CLASS EXERCISE
Food for thought..
Food for thought..
84
http://www.gapminder.org
SECTION 6: CLASS EXERCISE
SECTION 7: RESOURCES
AND CONCLUSIONS
INTRODUCTION TO DATA VISUALIZATION 85
DATA VISUALIZATION RESOURCES
‣ Flowing Data (http://flowingdata.com/
‣ Fast Company Co.design (http://www.fastcodesign.com/)
‣ UX Magazine (http://uxmag.com/)
‣ The Human-Computer Interaction Lab (http://www.cs.umd.edu/hcil/)
‣ A Periodic Table of Visualization Methods (www.visual-literacy.org/
periodic_table/periodic_table.html)
Sites:
86SECTION 7: RESOURCES AND CONCLUSIONS
DATA VISUALIZATION BOOKS:
‣ Bertin, J. (2011). Semiology of graphics: Diagrams, networks, maps. (Berg, W. J., Trans.) Redlands, CA: Esri
Press. (Original work published 1965)
‣ Card, S. K., Mackinlay, J. D., & Shneiderman, B. (Eds.). (1999). Readings in information visualization: Using
vision to think. San Francisco, CA: Morgan Kaufmann Publishers.
‣ Few, S. C. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Oakland, CA:
Analytics Press.
‣ Few, S. C. (2004). Show me the numbers: Designing tables and graphs to enlighten. Oakland, CA: Analytics
Press.
‣ Fry, B. (2008). Visualizing data. Sebastopol, CA: O’Reilly Media, Inc.
‣ Segaran, T., & Hammerbacher, J. (Eds.) (2009). Beautiful data: The stories behind elegant data solutions.
Sebastopol, CA: O’Reilly Media, Inc.
‣ Tufte, E.R. (1997). Visual explanations: Images and quantities, evidence and narrative. Cheshire, CT: Graphics
Press, LLC.
‣ Ware, C. (2008). Visual thinking for design. Burlington, MA: Morgan Kaufmann Publishers.
‣ Whitney, H. (2012) Data Insights New Ways to Visualize and Make Sense of Data Morgan Kaufmann/Elsevier
2012.
‣ Wilkinson, L. (2005). The grammar of graphics. Chicago, IL: Springer.
‣ Yau, N. (2011). Visualize this: The flowing data guide to design, visualization, and statistics. Indianapolis, IN:
Wiley Publishing, Inc.
87SECTION 7: RESOURCES AND CONCLUSIONS
‣ Length Triesman & Gormican [1988]
‣ Width Julesz [1985]
‣ Size Triesman & Gelade [1980]
‣ Curvature Triesman & Gormican [1988]
‣ Number Julesz [1985]; Trick & Pylyshyn [1994]
‣ Terminators Julesz & Bergen [1983]
‣ Intersection Julesz & Bergen [1983]
‣ Closure Enns [1986]; Triesman & Souther [1985]
‣ Color (hue) Nagy & Sanchez [1990, 1992]; D'Zmura [1991]Kawai et al.
‣ Intensity Beck et al. [1983]; Triesman & Gormican [1988]
‣ Flicker Julesz [1971]
‣ Direction of motion Nakayama & Silverman [1986]; Driver & McLeod [1992]
‣ Binocular luster Wolfe & Franzel [1988]
‣ Stereoscopic depth Nakayama & Silverman [1986]
‣ 3-D depth cues Enns [1990]
‣ Lighting direction Enns [1990]
88SECTION 7: RESOURCES AND CONCLUSIONS
CONCLUDING THOUGHTS
•Data visualization involves learning about the rules and the process
•Start with the problem, not with the data or the visualization
•Think big: find the data you need
•Visualize your data in multiple ways
•Know your audience and their goals
89SECTION 7: RESOURCES AND CONCLUSIONS
Keep in mind - the value of data depends on what you do with it
90
Source: Reprinted in Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012.

SECTION 7: RESOURCES AND CONCLUSIONS
QUESTIONS?
CONTACT:
HUNTER WHITNEY
HUNTER@HUNTERWHITNEY.COM
@HUNTERWHITNEY
91SECTION 7: RESOURCES AND CONCLUSIONS

More Related Content

What's hot

Introduction to Data Visualization
Introduction to Data Visualization Introduction to Data Visualization
Introduction to Data Visualization
Ana Jofre
 

What's hot (20)

Brief introduction to data visualization
Brief introduction to data visualizationBrief introduction to data visualization
Brief introduction to data visualization
 
Data Visualization - What can you see? #baai17
Data Visualization - What can you see? #baai17Data Visualization - What can you see? #baai17
Data Visualization - What can you see? #baai17
 
Data Visualization Design Best Practices Workshop
Data Visualization Design Best Practices WorkshopData Visualization Design Best Practices Workshop
Data Visualization Design Best Practices Workshop
 
Data visualization
Data visualizationData visualization
Data visualization
 
The Importance of Data Visualization
The Importance of Data VisualizationThe Importance of Data Visualization
The Importance of Data Visualization
 
Data Visualization
Data VisualizationData Visualization
Data Visualization
 
Data visualization
Data visualizationData visualization
Data visualization
 
Data visualization
Data visualizationData visualization
Data visualization
 
Telling stories with data slideshare
Telling stories with data   slideshareTelling stories with data   slideshare
Telling stories with data slideshare
 
Data visualization introduction
Data visualization introductionData visualization introduction
Data visualization introduction
 
Visualisation & Storytelling in Data Science & Analytics
Visualisation & Storytelling in Data Science & AnalyticsVisualisation & Storytelling in Data Science & Analytics
Visualisation & Storytelling in Data Science & Analytics
 
Data Visualization - A Brief Overview
Data Visualization - A Brief OverviewData Visualization - A Brief Overview
Data Visualization - A Brief Overview
 
Data Storytelling: The only way to unlock true insight from your data
Data Storytelling: The only way to unlock true insight from your dataData Storytelling: The only way to unlock true insight from your data
Data Storytelling: The only way to unlock true insight from your data
 
Data Visualization: Impact, Intrigue, Value Add for APLIC 2014
Data Visualization: Impact, Intrigue, Value Add for APLIC 2014Data Visualization: Impact, Intrigue, Value Add for APLIC 2014
Data Visualization: Impact, Intrigue, Value Add for APLIC 2014
 
5 Data Visualization Pitfalls
5 Data Visualization Pitfalls5 Data Visualization Pitfalls
5 Data Visualization Pitfalls
 
Data visualization in a Nutshell
Data visualization in a NutshellData visualization in a Nutshell
Data visualization in a Nutshell
 
Storytelling with Data - See | Show | Tell | Engage
Storytelling with Data - See | Show | Tell | EngageStorytelling with Data - See | Show | Tell | Engage
Storytelling with Data - See | Show | Tell | Engage
 
Data visualization
Data visualizationData visualization
Data visualization
 
Introduction to Data Visualization
Introduction to Data Visualization Introduction to Data Visualization
Introduction to Data Visualization
 
Benefits of data visualization
Benefits of data visualizationBenefits of data visualization
Benefits of data visualization
 

Similar to "Introduction to Data Visualization" Workshop for General Assembly by Hunter Whitney Feb 2015

thesis_submitted
thesis_submittedthesis_submitted
thesis_submitted
Alex Streit
 
1Dr. LaMar D. Brown PhD, MBAExecutive MSITUniv
1Dr. LaMar D. Brown PhD, MBAExecutive MSITUniv1Dr. LaMar D. Brown PhD, MBAExecutive MSITUniv
1Dr. LaMar D. Brown PhD, MBAExecutive MSITUniv
EttaBenton28
 
Literature survey andrei_manta_0
Literature survey andrei_manta_0Literature survey andrei_manta_0
Literature survey andrei_manta_0
darshanahiren
 

Similar to "Introduction to Data Visualization" Workshop for General Assembly by Hunter Whitney Feb 2015 (20)

Big data
Big dataBig data
Big data
 
ADV: Solving the data visualization dilemma
ADV: Solving the data visualization dilemmaADV: Solving the data visualization dilemma
ADV: Solving the data visualization dilemma
 
Challenges in Analytics for BIG Data
Challenges in Analytics for BIG DataChallenges in Analytics for BIG Data
Challenges in Analytics for BIG Data
 
thesis_submitted
thesis_submittedthesis_submitted
thesis_submitted
 
Data-Driven Design for User Experience
Data-Driven Design for User Experience Data-Driven Design for User Experience
Data-Driven Design for User Experience
 
Data Visualization in Big Data Analytics
Data Visualization in Big Data AnalyticsData Visualization in Big Data Analytics
Data Visualization in Big Data Analytics
 
Vivarana literature survey
Vivarana literature surveyVivarana literature survey
Vivarana literature survey
 
Excellence in visulization
Excellence in visulizationExcellence in visulization
Excellence in visulization
 
1Dr. LaMar D. Brown PhD, MBAExecutive MSITUniv
1Dr. LaMar D. Brown PhD, MBAExecutive MSITUniv1Dr. LaMar D. Brown PhD, MBAExecutive MSITUniv
1Dr. LaMar D. Brown PhD, MBAExecutive MSITUniv
 
Toward supporting decision-making under uncertainty in digital humanities wit...
Toward supporting decision-making under uncertainty in digital humanities wit...Toward supporting decision-making under uncertainty in digital humanities wit...
Toward supporting decision-making under uncertainty in digital humanities wit...
 
Data visualization trends in Business Intelligence: Allison Sapka at Analytic...
Data visualization trends in Business Intelligence: Allison Sapka at Analytic...Data visualization trends in Business Intelligence: Allison Sapka at Analytic...
Data visualization trends in Business Intelligence: Allison Sapka at Analytic...
 
Literature survey andrei_manta_0
Literature survey andrei_manta_0Literature survey andrei_manta_0
Literature survey andrei_manta_0
 
التنقيب في البيانات - Data Mining
التنقيب في البيانات -  Data Miningالتنقيب في البيانات -  Data Mining
التنقيب في البيانات - Data Mining
 
How to collect and organize data
How to collect and organize dataHow to collect and organize data
How to collect and organize data
 
Výzkum digitálních kompetencí
Výzkum digitálních kompetencíVýzkum digitálních kompetencí
Výzkum digitálních kompetencí
 
Around Data Science
Around Data ScienceAround Data Science
Around Data Science
 
Luciano uvi hackfest.28.10.2020
Luciano uvi hackfest.28.10.2020Luciano uvi hackfest.28.10.2020
Luciano uvi hackfest.28.10.2020
 
Barga, roger. predictive analytics with microsoft azure machine learning
Barga, roger. predictive analytics with microsoft azure machine learningBarga, roger. predictive analytics with microsoft azure machine learning
Barga, roger. predictive analytics with microsoft azure machine learning
 
User Experience Versus Marketing
User Experience Versus MarketingUser Experience Versus Marketing
User Experience Versus Marketing
 
Challenges Faced by Novices While Developing and Designing the Visualization ...
Challenges Faced by Novices While Developing and Designing the Visualization ...Challenges Faced by Novices While Developing and Designing the Visualization ...
Challenges Faced by Novices While Developing and Designing the Visualization ...
 

Recently uploaded

Fuzzy Sets decision making under information of uncertainty
Fuzzy Sets decision making under information of uncertaintyFuzzy Sets decision making under information of uncertainty
Fuzzy Sets decision making under information of uncertainty
RafigAliyev2
 
一比一原版纽卡斯尔大学毕业证成绩单如何办理
一比一原版纽卡斯尔大学毕业证成绩单如何办理一比一原版纽卡斯尔大学毕业证成绩单如何办理
一比一原版纽卡斯尔大学毕业证成绩单如何办理
cyebo
 
Exploratory Data Analysis - Dilip S.pptx
Exploratory Data Analysis - Dilip S.pptxExploratory Data Analysis - Dilip S.pptx
Exploratory Data Analysis - Dilip S.pptx
DilipVasan
 
一比一原版西悉尼大学毕业证成绩单如何办理
一比一原版西悉尼大学毕业证成绩单如何办理一比一原版西悉尼大学毕业证成绩单如何办理
一比一原版西悉尼大学毕业证成绩单如何办理
pyhepag
 
一比一原版麦考瑞大学毕业证成绩单如何办理
一比一原版麦考瑞大学毕业证成绩单如何办理一比一原版麦考瑞大学毕业证成绩单如何办理
一比一原版麦考瑞大学毕业证成绩单如何办理
cyebo
 
一比一原版(Monash毕业证书)莫纳什大学毕业证成绩单如何办理
一比一原版(Monash毕业证书)莫纳什大学毕业证成绩单如何办理一比一原版(Monash毕业证书)莫纳什大学毕业证成绩单如何办理
一比一原版(Monash毕业证书)莫纳什大学毕业证成绩单如何办理
pyhepag
 
一比一原版加利福尼亚大学尔湾分校毕业证成绩单如何办理
一比一原版加利福尼亚大学尔湾分校毕业证成绩单如何办理一比一原版加利福尼亚大学尔湾分校毕业证成绩单如何办理
一比一原版加利福尼亚大学尔湾分校毕业证成绩单如何办理
pyhepag
 
一比一原版阿德莱德大学毕业证成绩单如何办理
一比一原版阿德莱德大学毕业证成绩单如何办理一比一原版阿德莱德大学毕业证成绩单如何办理
一比一原版阿德莱德大学毕业证成绩单如何办理
pyhepag
 

Recently uploaded (20)

How can I successfully sell my pi coins in Philippines?
How can I successfully sell my pi coins in Philippines?How can I successfully sell my pi coins in Philippines?
How can I successfully sell my pi coins in Philippines?
 
Fuzzy Sets decision making under information of uncertainty
Fuzzy Sets decision making under information of uncertaintyFuzzy Sets decision making under information of uncertainty
Fuzzy Sets decision making under information of uncertainty
 
Slip-and-fall Injuries: Top Workers' Comp Claims
Slip-and-fall Injuries: Top Workers' Comp ClaimsSlip-and-fall Injuries: Top Workers' Comp Claims
Slip-and-fall Injuries: Top Workers' Comp Claims
 
Pre-ProductionImproveddsfjgndflghtgg.pptx
Pre-ProductionImproveddsfjgndflghtgg.pptxPre-ProductionImproveddsfjgndflghtgg.pptx
Pre-ProductionImproveddsfjgndflghtgg.pptx
 
How I opened a fake bank account and didn't go to prison
How I opened a fake bank account and didn't go to prisonHow I opened a fake bank account and didn't go to prison
How I opened a fake bank account and didn't go to prison
 
一比一原版纽卡斯尔大学毕业证成绩单如何办理
一比一原版纽卡斯尔大学毕业证成绩单如何办理一比一原版纽卡斯尔大学毕业证成绩单如何办理
一比一原版纽卡斯尔大学毕业证成绩单如何办理
 
2024 Q1 Tableau User Group Leader Quarterly Call
2024 Q1 Tableau User Group Leader Quarterly Call2024 Q1 Tableau User Group Leader Quarterly Call
2024 Q1 Tableau User Group Leader Quarterly Call
 
basics of data science with application areas.pdf
basics of data science with application areas.pdfbasics of data science with application areas.pdf
basics of data science with application areas.pdf
 
Atlantic Grupa Case Study (Mintec Data AI)
Atlantic Grupa Case Study (Mintec Data AI)Atlantic Grupa Case Study (Mintec Data AI)
Atlantic Grupa Case Study (Mintec Data AI)
 
Machine Learning for Accident Severity Prediction
Machine Learning for Accident Severity PredictionMachine Learning for Accident Severity Prediction
Machine Learning for Accident Severity Prediction
 
Exploratory Data Analysis - Dilip S.pptx
Exploratory Data Analysis - Dilip S.pptxExploratory Data Analysis - Dilip S.pptx
Exploratory Data Analysis - Dilip S.pptx
 
一比一原版西悉尼大学毕业证成绩单如何办理
一比一原版西悉尼大学毕业证成绩单如何办理一比一原版西悉尼大学毕业证成绩单如何办理
一比一原版西悉尼大学毕业证成绩单如何办理
 
一比一原版麦考瑞大学毕业证成绩单如何办理
一比一原版麦考瑞大学毕业证成绩单如何办理一比一原版麦考瑞大学毕业证成绩单如何办理
一比一原版麦考瑞大学毕业证成绩单如何办理
 
Easy and simple project file on mp online
Easy and simple project file on mp onlineEasy and simple project file on mp online
Easy and simple project file on mp online
 
AI Imagen for data-storytelling Infographics.pdf
AI Imagen for data-storytelling Infographics.pdfAI Imagen for data-storytelling Infographics.pdf
AI Imagen for data-storytelling Infographics.pdf
 
一比一原版(Monash毕业证书)莫纳什大学毕业证成绩单如何办理
一比一原版(Monash毕业证书)莫纳什大学毕业证成绩单如何办理一比一原版(Monash毕业证书)莫纳什大学毕业证成绩单如何办理
一比一原版(Monash毕业证书)莫纳什大学毕业证成绩单如何办理
 
一比一原版加利福尼亚大学尔湾分校毕业证成绩单如何办理
一比一原版加利福尼亚大学尔湾分校毕业证成绩单如何办理一比一原版加利福尼亚大学尔湾分校毕业证成绩单如何办理
一比一原版加利福尼亚大学尔湾分校毕业证成绩单如何办理
 
一比一原版阿德莱德大学毕业证成绩单如何办理
一比一原版阿德莱德大学毕业证成绩单如何办理一比一原版阿德莱德大学毕业证成绩单如何办理
一比一原版阿德莱德大学毕业证成绩单如何办理
 
Webinar One View, Multiple Systems No-Code Integration of Salesforce and ERPs
Webinar One View, Multiple Systems No-Code Integration of Salesforce and ERPsWebinar One View, Multiple Systems No-Code Integration of Salesforce and ERPs
Webinar One View, Multiple Systems No-Code Integration of Salesforce and ERPs
 
Artificial_General_Intelligence__storm_gen_article.pdf
Artificial_General_Intelligence__storm_gen_article.pdfArtificial_General_Intelligence__storm_gen_article.pdf
Artificial_General_Intelligence__storm_gen_article.pdf
 

"Introduction to Data Visualization" Workshop for General Assembly by Hunter Whitney Feb 2015