SlideShare a Scribd company logo
1 of 48
Data Visualization
• http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html
Data
Ambiguity
Failure to
precisely define
just what the data
represent
0
0.5
1
1.5
2
2.5
3
3.5
0 1 2 3
Y-Value 1
Data Distortion
Exaggerating or
understating the
values of some of the
data points
Data
Distraction
Extraneous lines,
graphics, etc.
1st Qtr
58%
2nd Qtr
10%
3rd Qtr
23%
4th Qtr
9%
Sales
How to make graphs that work
(advice from Seth Godin)
1. Don't let popular spreadsheets be in charge
of the way you look.
2. Tell a story.
3. Follow some simple rules.*
4. Break some other rules.
Classics – The Table
• While it might be possible to display data
better graphically, a table often does the job
quite nicely.
*Godin’s Rules
• Time goes from left to right.
Sales data in units
1st
Quarter
2nd
Quarter
3rd
Quarter
4th
Quarter
8.2 1.4 3.2 1.2
Classics – Pie Charts
• Pie charts have a mixed reputation.
• They are popular in business and the media but
many information designers have criticized the
technique.
• Some claim that the pie slice shape
communicates numbers less exactly than other
possibilities such as line length.
• At least one study indicates that use of a pie chart
for analyzing a problem as opposed to a bar chart
changes the way people think about the problem.
*Godin’s Rules
• Pie charts are spectacularly overrated. If you
want to show me that four out of five
dentists prefer Trident and that we need to
target the fifth one, show me a picture of 5
dentists, but make one of them stand out. I'll
remember that.
Sales
Sales
1st Qtr
2nd Qtr
3rd Qtr
4th Qtr
Sales
Sales
1st Qtr
2nd Qtr
3rd Qtr
4th Qtr
Sales (% of total units)
1st Qtr
58%
2nd Qtr
10%
3rd Qtr
23%
4th Qtr
9%
Sales
Sales (% of total units)
1st Qtr
58%
2nd Qtr
10%
3rd Qtr
23%
4th Qtr;
9%
Sales
Your Options
(according to Yoda)
Do.
Do not.
Try.
Classics – Line Graphs
• Line graphs are classic diagrams that usually
give a good picture of the data.
• Line graphs should only be used when the
positions on the x-axis have a natural
ordering. If your labels are "2000, 2001,
2002" that's fine. If your labels are "US,
England, Germany" you should consider a bar
graph instead.
*Godin’s Rules
• Good results should go up on the Y axis. This
means that if you're charting weight loss,
don't chart "how much I weigh" because
good results would go down. Instead, chart
"percentage of goal" or "how much I lost.
Sales (total units)
1st Qtr, 8.2
2nd Qtr, 1.4
3rd Qtr, 3.2
4th Qtr; 1.2
0
1
2
3
4
5
6
7
8
9
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
Sales
*Godin’s Rules
• "Don't connect unrelated events. For
example, a graph of IQs of everyone in your
kindergarten class should be a series of
unrelated points, not a line graph. On the
other hand, your weight loss is in fact a
continuous function, so each piece of data
should be attached.
Classics – Bar Charts
• Bar charts are classic diagrams that usually
give a good picture of the data.
• Their main problem is that when there are
many bars, labeling becomes problematic.
• They also imply that the data is discrete; if
your data is something that is plausibly
continuously changing over time, for
instance, you might consider a line graph
instead.
Sales (total units)
8.2
1.4
3.2
1.2
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
0
1
2
3
4
5
6
7
8
9
New Classics – Network Diagram
• Real-world information often comes in the
form of relationships between entities or
items, such as people who know each other
(social networks), or Web pages that are
connected to each other.
• In a network diagram, entities are connected
to each other in the form of a node and link
diagram.
New Classics – Word Cloud
• A "Word Cloud" enables you to see how
frequently words appear in a given text, or
see the relationship between a column of
words and a column of numbers.
• You can tweak your word "clouds" with
different fonts, layouts, and color schemes.
• Wordle.net
New Classics - Infographics
• Information graphics or infographics are
graphic visual representations of
information, data or knowledge.
The future of visualization
• One word: DATA
Example: NYT Cascade
• Cascade allows for precise analysis of the
structures which underly sharing activity on the
web.
• Links browsing behavior on a site to sharing
activity to construct a detailed picture of how
information propagates through the social media
space.
• The tool and its underlying logic may be applied
to any publisher or brand interested in
understanding how its messages are shared.
• http://nytlabs.com/projects/cascade.html

More Related Content

Viewers also liked

Behaviour driven development
Behaviour driven developmentBehaviour driven development
Behaviour driven developmentFraboni Ec
 
Google mock for dummies
Google mock for dummiesGoogle mock for dummies
Google mock for dummiesFraboni Ec
 
Nlp naive bayes
Nlp naive bayesNlp naive bayes
Nlp naive bayesFraboni Ec
 
Exception handling
Exception handlingException handling
Exception handlingFraboni Ec
 
Decision analysis
Decision analysisDecision analysis
Decision analysisFraboni Ec
 
Text categorization as a graph
Text categorization as a graphText categorization as a graph
Text categorization as a graphFraboni Ec
 
Text classificationmethods
Text classificationmethodsText classificationmethods
Text classificationmethodsFraboni Ec
 
Database introduction
Database introductionDatabase introduction
Database introductionFraboni Ec
 
Sql database object
Sql database objectSql database object
Sql database objectFraboni Ec
 
Crypto theory practice
Crypto theory practiceCrypto theory practice
Crypto theory practiceFraboni Ec
 
Overview prolog
Overview prologOverview prolog
Overview prologFraboni Ec
 
Introduction toprolog
Introduction toprologIntroduction toprolog
Introduction toprologFraboni Ec
 
Text classification
Text classificationText classification
Text classificationFraboni Ec
 

Viewers also liked (20)

Behaviour driven development
Behaviour driven developmentBehaviour driven development
Behaviour driven development
 
Network
NetworkNetwork
Network
 
Google mock for dummies
Google mock for dummiesGoogle mock for dummies
Google mock for dummies
 
Prolog resume
Prolog resumeProlog resume
Prolog resume
 
Nlp naive bayes
Nlp naive bayesNlp naive bayes
Nlp naive bayes
 
Exception handling
Exception handlingException handling
Exception handling
 
Decision analysis
Decision analysisDecision analysis
Decision analysis
 
Text categorization as a graph
Text categorization as a graphText categorization as a graph
Text categorization as a graph
 
Text classificationmethods
Text classificationmethodsText classificationmethods
Text classificationmethods
 
Database introduction
Database introductionDatabase introduction
Database introduction
 
Polymorphism
PolymorphismPolymorphism
Polymorphism
 
Sql database object
Sql database objectSql database object
Sql database object
 
Basic dns-mod
Basic dns-modBasic dns-mod
Basic dns-mod
 
Cryptography
CryptographyCryptography
Cryptography
 
Crypto theory practice
Crypto theory practiceCrypto theory practice
Crypto theory practice
 
Game theory
Game theoryGame theory
Game theory
 
Overview prolog
Overview prologOverview prolog
Overview prolog
 
Exception
ExceptionException
Exception
 
Introduction toprolog
Introduction toprologIntroduction toprolog
Introduction toprolog
 
Text classification
Text classificationText classification
Text classification
 

Similar to Data visualization

Guidelines for data visualisation: eye vegetables and eye candy
Guidelines for data visualisation: eye vegetables and eye candyGuidelines for data visualisation: eye vegetables and eye candy
Guidelines for data visualisation: eye vegetables and eye candyJen Stirrup
 
DutchMLSchool. Automating Decision Making
DutchMLSchool. Automating Decision MakingDutchMLSchool. Automating Decision Making
DutchMLSchool. Automating Decision MakingBigML, Inc
 
UNIT_4_data visualization.pptx
UNIT_4_data visualization.pptxUNIT_4_data visualization.pptx
UNIT_4_data visualization.pptxBhagyasriPatel2
 
MLSD18. Feature Engineering
MLSD18. Feature EngineeringMLSD18. Feature Engineering
MLSD18. Feature EngineeringBigML, Inc
 
A Little Graph Theory for the Busy Developer - Jim Webber @ GraphConnect Chic...
A Little Graph Theory for the Busy Developer - Jim Webber @ GraphConnect Chic...A Little Graph Theory for the Busy Developer - Jim Webber @ GraphConnect Chic...
A Little Graph Theory for the Busy Developer - Jim Webber @ GraphConnect Chic...Neo4j
 
Exploratory Data Analysis week 4
Exploratory Data Analysis week 4Exploratory Data Analysis week 4
Exploratory Data Analysis week 4Manzur Ashraf
 
Top 50 Diagrams in Editable Powerpoint
Top 50 Diagrams in Editable PowerpointTop 50 Diagrams in Editable Powerpoint
Top 50 Diagrams in Editable PowerpointAurelien Domont, MBA
 
VSSML18. Clustering and Latent Dirichlet Allocation
VSSML18. Clustering and Latent Dirichlet AllocationVSSML18. Clustering and Latent Dirichlet Allocation
VSSML18. Clustering and Latent Dirichlet AllocationBigML, Inc
 
Data displays in statistics
Data displays in statisticsData displays in statistics
Data displays in statisticsannieg8989
 
VSSML18. Feature Engineering
VSSML18. Feature EngineeringVSSML18. Feature Engineering
VSSML18. Feature EngineeringBigML, Inc
 
Tableau Visual Guidebook
Tableau Visual GuidebookTableau Visual Guidebook
Tableau Visual GuidebookAndy Kriebel
 
L8 scientific visualization of data
L8 scientific visualization of dataL8 scientific visualization of data
L8 scientific visualization of dataSeppo Karrila
 
Measurecamp 6 Workshop: Data Visualisation
Measurecamp 6 Workshop: Data VisualisationMeasurecamp 6 Workshop: Data Visualisation
Measurecamp 6 Workshop: Data VisualisationSean Burton
 
MLSEV. Automating Decision Making
MLSEV. Automating Decision MakingMLSEV. Automating Decision Making
MLSEV. Automating Decision MakingBigML, Inc
 
BSSML16 L1. Introduction, Models, and Evaluations
BSSML16 L1. Introduction, Models, and EvaluationsBSSML16 L1. Introduction, Models, and Evaluations
BSSML16 L1. Introduction, Models, and EvaluationsBigML, Inc
 
Data Visualization using different python libraries.pptx
Data Visualization using different python libraries.pptxData Visualization using different python libraries.pptx
Data Visualization using different python libraries.pptxHamzaAli998966
 
BSSML17 - Feature Engineering
BSSML17 - Feature EngineeringBSSML17 - Feature Engineering
BSSML17 - Feature EngineeringBigML, Inc
 

Similar to Data visualization (20)

Guidelines for data visualisation: eye vegetables and eye candy
Guidelines for data visualisation: eye vegetables and eye candyGuidelines for data visualisation: eye vegetables and eye candy
Guidelines for data visualisation: eye vegetables and eye candy
 
DutchMLSchool. Automating Decision Making
DutchMLSchool. Automating Decision MakingDutchMLSchool. Automating Decision Making
DutchMLSchool. Automating Decision Making
 
Data cube
Data cubeData cube
Data cube
 
UNIT_4_data visualization.pptx
UNIT_4_data visualization.pptxUNIT_4_data visualization.pptx
UNIT_4_data visualization.pptx
 
MLSD18. Feature Engineering
MLSD18. Feature EngineeringMLSD18. Feature Engineering
MLSD18. Feature Engineering
 
A Little Graph Theory for the Busy Developer - Jim Webber @ GraphConnect Chic...
A Little Graph Theory for the Busy Developer - Jim Webber @ GraphConnect Chic...A Little Graph Theory for the Busy Developer - Jim Webber @ GraphConnect Chic...
A Little Graph Theory for the Busy Developer - Jim Webber @ GraphConnect Chic...
 
07 learning
07 learning07 learning
07 learning
 
Exploratory Data Analysis week 4
Exploratory Data Analysis week 4Exploratory Data Analysis week 4
Exploratory Data Analysis week 4
 
Top 50 Diagrams in Editable Powerpoint
Top 50 Diagrams in Editable PowerpointTop 50 Diagrams in Editable Powerpoint
Top 50 Diagrams in Editable Powerpoint
 
VSSML18. Clustering and Latent Dirichlet Allocation
VSSML18. Clustering and Latent Dirichlet AllocationVSSML18. Clustering and Latent Dirichlet Allocation
VSSML18. Clustering and Latent Dirichlet Allocation
 
Data displays in statistics
Data displays in statisticsData displays in statistics
Data displays in statistics
 
Data visualisationresearch
Data visualisationresearchData visualisationresearch
Data visualisationresearch
 
VSSML18. Feature Engineering
VSSML18. Feature EngineeringVSSML18. Feature Engineering
VSSML18. Feature Engineering
 
Tableau Visual Guidebook
Tableau Visual GuidebookTableau Visual Guidebook
Tableau Visual Guidebook
 
L8 scientific visualization of data
L8 scientific visualization of dataL8 scientific visualization of data
L8 scientific visualization of data
 
Measurecamp 6 Workshop: Data Visualisation
Measurecamp 6 Workshop: Data VisualisationMeasurecamp 6 Workshop: Data Visualisation
Measurecamp 6 Workshop: Data Visualisation
 
MLSEV. Automating Decision Making
MLSEV. Automating Decision MakingMLSEV. Automating Decision Making
MLSEV. Automating Decision Making
 
BSSML16 L1. Introduction, Models, and Evaluations
BSSML16 L1. Introduction, Models, and EvaluationsBSSML16 L1. Introduction, Models, and Evaluations
BSSML16 L1. Introduction, Models, and Evaluations
 
Data Visualization using different python libraries.pptx
Data Visualization using different python libraries.pptxData Visualization using different python libraries.pptx
Data Visualization using different python libraries.pptx
 
BSSML17 - Feature Engineering
BSSML17 - Feature EngineeringBSSML17 - Feature Engineering
BSSML17 - Feature Engineering
 

More from Fraboni Ec

Hardware multithreading
Hardware multithreadingHardware multithreading
Hardware multithreadingFraboni Ec
 
What is simultaneous multithreading
What is simultaneous multithreadingWhat is simultaneous multithreading
What is simultaneous multithreadingFraboni Ec
 
Directory based cache coherence
Directory based cache coherenceDirectory based cache coherence
Directory based cache coherenceFraboni Ec
 
Business analytics and data mining
Business analytics and data miningBusiness analytics and data mining
Business analytics and data miningFraboni Ec
 
Big picture of data mining
Big picture of data miningBig picture of data mining
Big picture of data miningFraboni Ec
 
Data mining and knowledge discovery
Data mining and knowledge discoveryData mining and knowledge discovery
Data mining and knowledge discoveryFraboni Ec
 
How analysis services caching works
How analysis services caching worksHow analysis services caching works
How analysis services caching worksFraboni Ec
 
Hardware managed cache
Hardware managed cacheHardware managed cache
Hardware managed cacheFraboni Ec
 
Data structures and algorithms
Data structures and algorithmsData structures and algorithms
Data structures and algorithmsFraboni Ec
 
Cobol, lisp, and python
Cobol, lisp, and pythonCobol, lisp, and python
Cobol, lisp, and pythonFraboni Ec
 
Abstract data types
Abstract data typesAbstract data types
Abstract data typesFraboni Ec
 
Optimizing shared caches in chip multiprocessors
Optimizing shared caches in chip multiprocessorsOptimizing shared caches in chip multiprocessors
Optimizing shared caches in chip multiprocessorsFraboni Ec
 
Abstraction file
Abstraction fileAbstraction file
Abstraction fileFraboni Ec
 
Object oriented analysis
Object oriented analysisObject oriented analysis
Object oriented analysisFraboni Ec
 
Abstract class
Abstract classAbstract class
Abstract classFraboni Ec
 
Concurrency with java
Concurrency with javaConcurrency with java
Concurrency with javaFraboni Ec
 

More from Fraboni Ec (20)

Hardware multithreading
Hardware multithreadingHardware multithreading
Hardware multithreading
 
Lisp
LispLisp
Lisp
 
What is simultaneous multithreading
What is simultaneous multithreadingWhat is simultaneous multithreading
What is simultaneous multithreading
 
Directory based cache coherence
Directory based cache coherenceDirectory based cache coherence
Directory based cache coherence
 
Business analytics and data mining
Business analytics and data miningBusiness analytics and data mining
Business analytics and data mining
 
Big picture of data mining
Big picture of data miningBig picture of data mining
Big picture of data mining
 
Data mining and knowledge discovery
Data mining and knowledge discoveryData mining and knowledge discovery
Data mining and knowledge discovery
 
Cache recap
Cache recapCache recap
Cache recap
 
How analysis services caching works
How analysis services caching worksHow analysis services caching works
How analysis services caching works
 
Hardware managed cache
Hardware managed cacheHardware managed cache
Hardware managed cache
 
Data structures and algorithms
Data structures and algorithmsData structures and algorithms
Data structures and algorithms
 
Cobol, lisp, and python
Cobol, lisp, and pythonCobol, lisp, and python
Cobol, lisp, and python
 
Abstract data types
Abstract data typesAbstract data types
Abstract data types
 
Optimizing shared caches in chip multiprocessors
Optimizing shared caches in chip multiprocessorsOptimizing shared caches in chip multiprocessors
Optimizing shared caches in chip multiprocessors
 
Abstraction file
Abstraction fileAbstraction file
Abstraction file
 
Object model
Object modelObject model
Object model
 
Object oriented analysis
Object oriented analysisObject oriented analysis
Object oriented analysis
 
Abstract class
Abstract classAbstract class
Abstract class
 
Concurrency with java
Concurrency with javaConcurrency with java
Concurrency with java
 
Inheritance
InheritanceInheritance
Inheritance
 

Recently uploaded

Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitecturePixlogix Infotech
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxMalak Abu Hammad
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Scott Keck-Warren
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking MenDelhi Call girls
 
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Alan Dix
 
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024BookNet Canada
 
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphSIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphNeo4j
 
SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024Scott Keck-Warren
 
Pigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions
 
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024BookNet Canada
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationRidwan Fadjar
 
Hyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your Budget
Hyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your BudgetHyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your Budget
Hyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your BudgetEnjoy Anytime
 
Key Features Of Token Development (1).pptx
Key  Features Of Token  Development (1).pptxKey  Features Of Token  Development (1).pptx
Key Features Of Token Development (1).pptxLBM Solutions
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticscarlostorres15106
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Allon Mureinik
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slidespraypatel2
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...HostedbyConfluent
 
Azure Monitor & Application Insight to monitor Infrastructure & Application
Azure Monitor & Application Insight to monitor Infrastructure & ApplicationAzure Monitor & Application Insight to monitor Infrastructure & Application
Azure Monitor & Application Insight to monitor Infrastructure & ApplicationAndikSusilo4
 

Recently uploaded (20)

Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC Architecture
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
 
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
 
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphSIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
 
SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024
 
Pigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food Manufacturing
 
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 Presentation
 
Hyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your Budget
Hyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your BudgetHyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your Budget
Hyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your Budget
 
Key Features Of Token Development (1).pptx
Key  Features Of Token  Development (1).pptxKey  Features Of Token  Development (1).pptx
Key Features Of Token Development (1).pptx
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slides
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
 
Azure Monitor & Application Insight to monitor Infrastructure & Application
Azure Monitor & Application Insight to monitor Infrastructure & ApplicationAzure Monitor & Application Insight to monitor Infrastructure & Application
Azure Monitor & Application Insight to monitor Infrastructure & Application
 

Data visualization

  • 3. Data Ambiguity Failure to precisely define just what the data represent 0 0.5 1 1.5 2 2.5 3 3.5 0 1 2 3 Y-Value 1
  • 4. Data Distortion Exaggerating or understating the values of some of the data points
  • 5. Data Distraction Extraneous lines, graphics, etc. 1st Qtr 58% 2nd Qtr 10% 3rd Qtr 23% 4th Qtr 9% Sales
  • 6. How to make graphs that work (advice from Seth Godin) 1. Don't let popular spreadsheets be in charge of the way you look. 2. Tell a story. 3. Follow some simple rules.* 4. Break some other rules.
  • 7. Classics – The Table • While it might be possible to display data better graphically, a table often does the job quite nicely.
  • 8. *Godin’s Rules • Time goes from left to right.
  • 9. Sales data in units 1st Quarter 2nd Quarter 3rd Quarter 4th Quarter 8.2 1.4 3.2 1.2
  • 10. Classics – Pie Charts • Pie charts have a mixed reputation. • They are popular in business and the media but many information designers have criticized the technique. • Some claim that the pie slice shape communicates numbers less exactly than other possibilities such as line length. • At least one study indicates that use of a pie chart for analyzing a problem as opposed to a bar chart changes the way people think about the problem.
  • 11. *Godin’s Rules • Pie charts are spectacularly overrated. If you want to show me that four out of five dentists prefer Trident and that we need to target the fifth one, show me a picture of 5 dentists, but make one of them stand out. I'll remember that.
  • 14. Sales (% of total units) 1st Qtr 58% 2nd Qtr 10% 3rd Qtr 23% 4th Qtr 9% Sales
  • 15. Sales (% of total units) 1st Qtr 58% 2nd Qtr 10% 3rd Qtr 23% 4th Qtr; 9% Sales
  • 16. Your Options (according to Yoda) Do. Do not. Try.
  • 17. Classics – Line Graphs • Line graphs are classic diagrams that usually give a good picture of the data. • Line graphs should only be used when the positions on the x-axis have a natural ordering. If your labels are "2000, 2001, 2002" that's fine. If your labels are "US, England, Germany" you should consider a bar graph instead.
  • 18. *Godin’s Rules • Good results should go up on the Y axis. This means that if you're charting weight loss, don't chart "how much I weigh" because good results would go down. Instead, chart "percentage of goal" or "how much I lost.
  • 19. Sales (total units) 1st Qtr, 8.2 2nd Qtr, 1.4 3rd Qtr, 3.2 4th Qtr; 1.2 0 1 2 3 4 5 6 7 8 9 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr Sales
  • 20. *Godin’s Rules • "Don't connect unrelated events. For example, a graph of IQs of everyone in your kindergarten class should be a series of unrelated points, not a line graph. On the other hand, your weight loss is in fact a continuous function, so each piece of data should be attached.
  • 21. Classics – Bar Charts • Bar charts are classic diagrams that usually give a good picture of the data. • Their main problem is that when there are many bars, labeling becomes problematic. • They also imply that the data is discrete; if your data is something that is plausibly continuously changing over time, for instance, you might consider a line graph instead.
  • 22. Sales (total units) 8.2 1.4 3.2 1.2 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr 0 1 2 3 4 5 6 7 8 9
  • 23. New Classics – Network Diagram • Real-world information often comes in the form of relationships between entities or items, such as people who know each other (social networks), or Web pages that are connected to each other. • In a network diagram, entities are connected to each other in the form of a node and link diagram.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30. New Classics – Word Cloud • A "Word Cloud" enables you to see how frequently words appear in a given text, or see the relationship between a column of words and a column of numbers. • You can tweak your word "clouds" with different fonts, layouts, and color schemes. • Wordle.net
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37.
  • 38. New Classics - Infographics • Information graphics or infographics are graphic visual representations of information, data or knowledge.
  • 39.
  • 40.
  • 41.
  • 42.
  • 43.
  • 44.
  • 45. The future of visualization • One word: DATA
  • 46. Example: NYT Cascade • Cascade allows for precise analysis of the structures which underly sharing activity on the web. • Links browsing behavior on a site to sharing activity to construct a detailed picture of how information propagates through the social media space. • The tool and its underlying logic may be applied to any publisher or brand interested in understanding how its messages are shared.
  • 47.