SlideShare a Scribd company logo
INTRODUCTION TO DATA
ANALYTICS
LEARNING OUTCOME
• Awareness on Data Visualization Tool
VISUALIZATION IS…
• Based on (non-visual) data
• Primarily producing an image
• Readable and Recognizable
The conversion of any
abstract data into a
graphical format so the
characteristics and
relationships of the data can
be explored and analyzed.
Mark Madsen, Third Nature
Chartjunk or Art?
EXAMPLES OF DATA VISUALIZATION
PURPOSE OF VISUALIZATION
• TO DIRECT
PURPOSE OF VISUALIZATION
• TO INFORM
PURPOSE OF VISUALIZATION
• TO EDUCATE
PURPOSE OF VISUALIZATION
• TO ENTERTAIN
PURPOSE OF VISUALIZATION
• TO LIE
2000
2100
2200
2300
2400
YES NO DON'T KNOW
Happy with Management
PURPOSE OF VISUALIZATION
• TO DISCOVER
PURPOSE OF VISUALIZATION
• TO KEEP TRACK OF THINGS
Beware of your Brain
WORKSHOP
Which is darker A or B? Equal size or not?
Which is Darker?
WORKSHOP
How many 5’s?
Again, how many 5’s?
The first Bar Chart (1786!)
……and Pie Charts
William Playfair, 1801
Florence Nightingale, 1826
Did you know?
90% of all information to our brains is visual.
People remember…
Bad Infographics: 10 mistakes you
never want to make
1. It just doesn’t add up
We’ve all seen these before. A
pie chart with slices that simply
don’t add up to a 100 percent,
like the one below which totals
188 percent.
Bad Infographics: 10 mistakes you
never want to make
2. Choosing the wrong type of chart
Another common mistake is
selecting a data visualization that
does not accurately reflect the
information, like in the case
above.
To choose the right type of chart, just ask yourself if you want to:
Bad Infographics: 10 mistakes you
never want to make
Compare Values:
• Bar Chart
• Line Chart
Show the individual
parts that make up a
whole:
• Pie Chart
• Stacked Bar
• Stacked Column
Understand how data is
distributed:
• Scatter Plot
• Line Chart
• Bar Chart
Analyzed Trends:
• Bar Chart
• Line Chart
Comprehend
relationship between
data sets:
• Line Chart
• Scatter Plot
• Bubble Chart
Bad Infographics: 10 mistakes you
never want to make
3. Including too much information
A common mistake made by
amateur infographic creators is
including too much information
in a single piece. Instead of
including long chunks of text, use
concise, impactful sound bites
combined with strong visuals to
drive your message home.
Bad Infographics: 10 mistakes you
never want to make
4. Inaccurate scales
Another common chart-making
sin is using scales that don’t
accurately reflect the data. This
commonly occurs with charts
that show relationships
between data sets, such as the
bubble chart.
Bad Infographics: 10 mistakes you
never want to make
5. Wrong placement of axes
When making charts for
infographics, remember to
always prioritize clarity and
accuracy above aesthetics.
Bad Infographics: 10 mistakes you
never want to make
6. Forcing the reader to do more work
The mark of an effective
infographic is its ability to make
complex information easy to
understand and interesting at
the same time.
Bad Infographics: 10 mistakes you
never want to make
7. Hard-to-understand comparisons
Another error that ties in with
the previous mistake is using
comparisons that don’t actually
make it easier to get an overall
sense of the data.
Bad Infographics: 10 mistakes you
never want to make
8. Arranging data non-intuitively
To ensure that the reader
understands your visual
information at first glance,
always arrange the data in an
intuitive manner.
x
Bad Infographics: 10 mistakes you
never want to make
9. Misrepresenting data with 3D charts
3D charts make look better than
flat ones, but don’t make the
mistake of using them as they
can skew data and make it
difficult to make accurate
comparisons.
Bad Infographics: 10 mistakes you
never want to make
10. Trying too hard to be different
Lastly, many mistakes are made
when you ignore common
conventions in order to be original.
Since most data visualizations such
as charts and graphs follow certain
standards that are recognized
worldwide, it is best to stick to the
rules when it comes to making
these.
Directions: (Group yourselves into 5)
Study the following pie-chart and the table and answer the questions based on
them.
Proportion of Population of Seven Villages in 1997
WORKSHOP
Question:
If in 1998, the population of villages Y and V increase by
10% each and the percentage of population below
poverty line remains unchanged for all the villages, then
find the population of village V below poverty line in
1998, given that the population of village Y in 1997 was
30000.
Option A):
13140
Option B):
12760
Option C):
11250
Option D):
13780
WORKSHOP
Correct Answer is Option B):
12760
WORKSHOP
“ The goal of most
visualization is
decision making”
Colin Ware
Thank You!

More Related Content

Similar to introduction to analytics

5 Data Visualization Pitfalls
5 Data Visualization Pitfalls5 Data Visualization Pitfalls
5 Data Visualization Pitfalls
Data IQ Argentina
 
Creating Functional Art in Excel
Creating Functional Art in ExcelCreating Functional Art in Excel
Creating Functional Art in Excel
Amanda Makulec
 
diseñando datos
diseñando datosdiseñando datos
diseñando datos
darwin465743
 
Measurecamp 7 Workshop: Data Visualisation
Measurecamp 7 Workshop: Data VisualisationMeasurecamp 7 Workshop: Data Visualisation
Measurecamp 7 Workshop: Data Visualisation
Sean Burton
 
Week2 day1slide
Week2 day1slideWeek2 day1slide
Week2 day1slide
RohitKar2
 
DATA VISUALIZATION
DATA VISUALIZATIONDATA VISUALIZATION
DATA VISUALIZATION
Aabhika Samantaray
 
Storytelling with data and data visualization
Storytelling with data and data visualizationStorytelling with data and data visualization
Storytelling with data and data visualization
Frehiwot Mulugeta
 
How to-create-5-fabulous-infographics-v4
How to-create-5-fabulous-infographics-v4How to-create-5-fabulous-infographics-v4
How to-create-5-fabulous-infographics-v4
www.makedigitalwork.com
 
Using datawrapper mirko lorenz
Using datawrapper   mirko lorenzUsing datawrapper   mirko lorenz
Using datawrapper mirko lorenz
DataFest Tbilisi
 
Working With Infographics
Working With InfographicsWorking With Infographics
Working With Infographics
UNCResearchHub
 
Design for Delight
Design for DelightDesign for Delight
Design for Delight
Amanda Makulec
 
How to-create-5-fabulous-infographics-final
How to-create-5-fabulous-infographics-finalHow to-create-5-fabulous-infographics-final
How to-create-5-fabulous-infographics-final
Vanitha Pillay
 
Sql rally amsterdam Aanalysing data with Power BI and Hive
Sql rally amsterdam Aanalysing data with Power BI and HiveSql rally amsterdam Aanalysing data with Power BI and Hive
Sql rally amsterdam Aanalysing data with Power BI and Hive
Jen Stirrup
 
Infographics
InfographicsInfographics
Infographics
Jennifer Janviere
 
Data Driven Marketing
Data Driven MarketingData Driven Marketing
Data Driven Marketing
DemandSphere
 
lsntap1707dataviztools-170726205609.pdf
lsntap1707dataviztools-170726205609.pdflsntap1707dataviztools-170726205609.pdf
lsntap1707dataviztools-170726205609.pdf
NIMMANAGANTI RAMAKRISHNA
 
Explanationforengineeringfornoreason.pptx
Explanationforengineeringfornoreason.pptxExplanationforengineeringfornoreason.pptx
Explanationforengineeringfornoreason.pptx
arshdeepsingh631069
 
Data Visualization Tools
Data Visualization ToolsData Visualization Tools
Data Visualization Tools
Data Visualization Tools Data Visualization Tools
Charts and visual lies - Elena Levi - DevOpsDays Tel Aviv 2018
Charts and visual lies - Elena Levi - DevOpsDays Tel Aviv 2018Charts and visual lies - Elena Levi - DevOpsDays Tel Aviv 2018
Charts and visual lies - Elena Levi - DevOpsDays Tel Aviv 2018
DevOpsDays Tel Aviv
 

Similar to introduction to analytics (20)

5 Data Visualization Pitfalls
5 Data Visualization Pitfalls5 Data Visualization Pitfalls
5 Data Visualization Pitfalls
 
Creating Functional Art in Excel
Creating Functional Art in ExcelCreating Functional Art in Excel
Creating Functional Art in Excel
 
diseñando datos
diseñando datosdiseñando datos
diseñando datos
 
Measurecamp 7 Workshop: Data Visualisation
Measurecamp 7 Workshop: Data VisualisationMeasurecamp 7 Workshop: Data Visualisation
Measurecamp 7 Workshop: Data Visualisation
 
Week2 day1slide
Week2 day1slideWeek2 day1slide
Week2 day1slide
 
DATA VISUALIZATION
DATA VISUALIZATIONDATA VISUALIZATION
DATA VISUALIZATION
 
Storytelling with data and data visualization
Storytelling with data and data visualizationStorytelling with data and data visualization
Storytelling with data and data visualization
 
How to-create-5-fabulous-infographics-v4
How to-create-5-fabulous-infographics-v4How to-create-5-fabulous-infographics-v4
How to-create-5-fabulous-infographics-v4
 
Using datawrapper mirko lorenz
Using datawrapper   mirko lorenzUsing datawrapper   mirko lorenz
Using datawrapper mirko lorenz
 
Working With Infographics
Working With InfographicsWorking With Infographics
Working With Infographics
 
Design for Delight
Design for DelightDesign for Delight
Design for Delight
 
How to-create-5-fabulous-infographics-final
How to-create-5-fabulous-infographics-finalHow to-create-5-fabulous-infographics-final
How to-create-5-fabulous-infographics-final
 
Sql rally amsterdam Aanalysing data with Power BI and Hive
Sql rally amsterdam Aanalysing data with Power BI and HiveSql rally amsterdam Aanalysing data with Power BI and Hive
Sql rally amsterdam Aanalysing data with Power BI and Hive
 
Infographics
InfographicsInfographics
Infographics
 
Data Driven Marketing
Data Driven MarketingData Driven Marketing
Data Driven Marketing
 
lsntap1707dataviztools-170726205609.pdf
lsntap1707dataviztools-170726205609.pdflsntap1707dataviztools-170726205609.pdf
lsntap1707dataviztools-170726205609.pdf
 
Explanationforengineeringfornoreason.pptx
Explanationforengineeringfornoreason.pptxExplanationforengineeringfornoreason.pptx
Explanationforengineeringfornoreason.pptx
 
Data Visualization Tools
Data Visualization ToolsData Visualization Tools
Data Visualization Tools
 
Data Visualization Tools
Data Visualization Tools Data Visualization Tools
Data Visualization Tools
 
Charts and visual lies - Elena Levi - DevOpsDays Tel Aviv 2018
Charts and visual lies - Elena Levi - DevOpsDays Tel Aviv 2018Charts and visual lies - Elena Levi - DevOpsDays Tel Aviv 2018
Charts and visual lies - Elena Levi - DevOpsDays Tel Aviv 2018
 

Recently uploaded

06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
Timothy Spann
 
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
ihavuls
 
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
bopyb
 
Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......
Sachin Paul
 
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
slg6lamcq
 
一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理
aqzctr7x
 
Experts live - Improving user adoption with AI
Experts live - Improving user adoption with AIExperts live - Improving user adoption with AI
Experts live - Improving user adoption with AI
jitskeb
 
Monthly Management report for the Month of May 2024
Monthly Management report for the Month of May 2024Monthly Management report for the Month of May 2024
Monthly Management report for the Month of May 2024
facilitymanager11
 
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Aggregage
 
一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理
一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理
一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理
y3i0qsdzb
 
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docxDATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
SaffaIbrahim1
 
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
hyfjgavov
 
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataPredictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Kiwi Creative
 
End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024
Lars Albertsson
 
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more importantChallenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
Sm321
 
一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理
一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理
一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理
z6osjkqvd
 
Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...
Bill641377
 
writing report business partner b1+ .pdf
writing report business partner b1+ .pdfwriting report business partner b1+ .pdf
writing report business partner b1+ .pdf
VyNguyen709676
 
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
apvysm8
 
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
wyddcwye1
 

Recently uploaded (20)

06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
 
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
 
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
 
Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......
 
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
 
一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理
 
Experts live - Improving user adoption with AI
Experts live - Improving user adoption with AIExperts live - Improving user adoption with AI
Experts live - Improving user adoption with AI
 
Monthly Management report for the Month of May 2024
Monthly Management report for the Month of May 2024Monthly Management report for the Month of May 2024
Monthly Management report for the Month of May 2024
 
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
 
一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理
一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理
一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理
 
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docxDATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
 
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
 
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataPredictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
 
End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024
 
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more importantChallenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
 
一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理
一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理
一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理
 
Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...
 
writing report business partner b1+ .pdf
writing report business partner b1+ .pdfwriting report business partner b1+ .pdf
writing report business partner b1+ .pdf
 
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
 
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
 

introduction to analytics

  • 2. LEARNING OUTCOME • Awareness on Data Visualization Tool
  • 3. VISUALIZATION IS… • Based on (non-visual) data • Primarily producing an image • Readable and Recognizable The conversion of any abstract data into a graphical format so the characteristics and relationships of the data can be explored and analyzed. Mark Madsen, Third Nature
  • 5. EXAMPLES OF DATA VISUALIZATION
  • 10. PURPOSE OF VISUALIZATION • TO LIE 2000 2100 2200 2300 2400 YES NO DON'T KNOW Happy with Management
  • 12. PURPOSE OF VISUALIZATION • TO KEEP TRACK OF THINGS
  • 13. Beware of your Brain WORKSHOP Which is darker A or B? Equal size or not?
  • 16. Again, how many 5’s?
  • 17. The first Bar Chart (1786!)
  • 18. ……and Pie Charts William Playfair, 1801 Florence Nightingale, 1826
  • 19. Did you know? 90% of all information to our brains is visual. People remember…
  • 20. Bad Infographics: 10 mistakes you never want to make 1. It just doesn’t add up We’ve all seen these before. A pie chart with slices that simply don’t add up to a 100 percent, like the one below which totals 188 percent.
  • 21. Bad Infographics: 10 mistakes you never want to make 2. Choosing the wrong type of chart Another common mistake is selecting a data visualization that does not accurately reflect the information, like in the case above.
  • 22. To choose the right type of chart, just ask yourself if you want to: Bad Infographics: 10 mistakes you never want to make Compare Values: • Bar Chart • Line Chart Show the individual parts that make up a whole: • Pie Chart • Stacked Bar • Stacked Column Understand how data is distributed: • Scatter Plot • Line Chart • Bar Chart Analyzed Trends: • Bar Chart • Line Chart Comprehend relationship between data sets: • Line Chart • Scatter Plot • Bubble Chart
  • 23. Bad Infographics: 10 mistakes you never want to make 3. Including too much information A common mistake made by amateur infographic creators is including too much information in a single piece. Instead of including long chunks of text, use concise, impactful sound bites combined with strong visuals to drive your message home.
  • 24. Bad Infographics: 10 mistakes you never want to make 4. Inaccurate scales Another common chart-making sin is using scales that don’t accurately reflect the data. This commonly occurs with charts that show relationships between data sets, such as the bubble chart.
  • 25. Bad Infographics: 10 mistakes you never want to make 5. Wrong placement of axes When making charts for infographics, remember to always prioritize clarity and accuracy above aesthetics.
  • 26. Bad Infographics: 10 mistakes you never want to make 6. Forcing the reader to do more work The mark of an effective infographic is its ability to make complex information easy to understand and interesting at the same time.
  • 27. Bad Infographics: 10 mistakes you never want to make 7. Hard-to-understand comparisons Another error that ties in with the previous mistake is using comparisons that don’t actually make it easier to get an overall sense of the data.
  • 28. Bad Infographics: 10 mistakes you never want to make 8. Arranging data non-intuitively To ensure that the reader understands your visual information at first glance, always arrange the data in an intuitive manner. x
  • 29. Bad Infographics: 10 mistakes you never want to make 9. Misrepresenting data with 3D charts 3D charts make look better than flat ones, but don’t make the mistake of using them as they can skew data and make it difficult to make accurate comparisons.
  • 30. Bad Infographics: 10 mistakes you never want to make 10. Trying too hard to be different Lastly, many mistakes are made when you ignore common conventions in order to be original. Since most data visualizations such as charts and graphs follow certain standards that are recognized worldwide, it is best to stick to the rules when it comes to making these.
  • 31. Directions: (Group yourselves into 5) Study the following pie-chart and the table and answer the questions based on them. Proportion of Population of Seven Villages in 1997 WORKSHOP
  • 32. Question: If in 1998, the population of villages Y and V increase by 10% each and the percentage of population below poverty line remains unchanged for all the villages, then find the population of village V below poverty line in 1998, given that the population of village Y in 1997 was 30000. Option A): 13140 Option B): 12760 Option C): 11250 Option D): 13780 WORKSHOP
  • 33. Correct Answer is Option B): 12760 WORKSHOP
  • 34.
  • 35. “ The goal of most visualization is decision making” Colin Ware

Editor's Notes

  1. Target Airplane Restroom School Crossing
  2. Intro: In an increasingly visual world, bad infographics have become the bane of the Internet. Just ask users who are bombarded on a daily basis with everything from poorly designed visuals to flat-out inaccurate data visualizations. This pandemic has gotten so bad that up to 95% of infographics from unknown sites have distorted the truth or just plain lied. It’s ruining the Web--so much so that users have gotten better and better at spotting misleading data as soon as they see it. Although you don’t have to be a math wiz to figure this one out, the mistake is more common than we think. This can occur, for example, when presenting the results of a poll that allowed for more than one response. In this case, the responses will not add up to 100, in which case another type of chart should be used.
  3. Or take, for example, this data visualization. Since the respondents were given a single choice, the answers add up to a 100 percent. This means that a pie chart--not a bar chart-- is the most appropriate format. Also, the information is not presented in descending order, which makes it all the more difficult to understand and compare the figures.
  4. Also, too much information in your graphs and charts can defeat the whole purpose of your infographic: to make information and data easy to understand at first glance.
  5. the size of the bubbles do not accurately reflect the relationship between the amount of money donated to the treatment of a disease and the numbers of deaths caused by it.
  6. The chart above, for instance, might look clean and pleasing to the eye, but it fails to place data into context by including an x- and y-axis.
  7. Infographics like this one, though, force the reader to do more--not less--work in the process of trying to understand the information. A common mistake is to separate the legend from the main data, forcing the reader to look back and forth between the central visualization and the meaning of each icon or color.
  8. The stacked columns above, for example, don’t allow the reader to contrast values because they aren’t placed side by side but scattered all over a map. This makes it difficult to compare the size of each color-coded bar segment.
  9. What exactly does this mean? For example, if you’re including a pie chart in your infographic, don’t place your segments randomly but in order of size. In the above example, segments are arranged in any order, which makes for a messy-looking chart. In the next pie chart, however, the segments are arranged in order of size in a counterclockwise direction.
  10. Just look at how the bar graph above misleads the viewer by making the first bar appear so much greater in value than the rest of the bars in comparison.
  11. For example, the chart tries to defy the norm by turning the chart upside down, but it only makes the reader have to work harder to fully comprehend the data.