Visualizing Data Journalism
Ritvvij Parrikh,
Founder, www.pykih.com
!
!
Fifth Elephant, Delhi Run-up Event,
India Today Me...
Pykih is a data Visualization company. We build custom visual representations of
large data sets to make data actionable f...
• Data Viz.

• Theory

• Case Study 1

• Case Study 2

• Summary

• Challenges in Data Journalism

• What we are doing abo...
Data Visualization
Let’s explore the humble pie chart…
Party Percentage
E 38%
D 25%
C 20%
B 15%
A 2%
Break the whole into parts.
Let’s explore the humble pie chart…
Party Percentage
E 38%
D 25%
C 20%
B 15%
A 2%
Break the whole into parts.
Data: One di...
New Terms
• Dimension: Columns by which you group data.!
!
• Facts: Numbers that you can count, sum, average, etc.!
!
• Ex...
One-dimensional Charts
PIE is a one-dimensional chart
One-dimensional Charts …
A pie could have been a random shape broken by percentage
One-dimensional Charts …
Pie
Amoeba
Percentage!
Rectangle Donut
Percentage!
Triangle
Bubble
Election Donut
Funnel
Percenta...
One-dimensional Charts …
Source: thehindu.com
What is wrong here?
One-dimensional Charts …
What is wrong here?
Problems:!
• Colour communicates no data!
• 3D communicates no data
Source: t...
One-dimensional Charts …
Source: thehindu.com
#2 - Your goal is to communicate data. Wrong use of visual encoding confuses...
One-dimensional Charts …
Source: firstpost.com
What is wrong here?
One-dimensional Charts …
What is wrong here?
Problems:!
• Colour!
• Too many values. Too
cluttered.
Source: firstpost.com
One-dimensional Charts …
Problems:!
• Colour!
• Too many values. Too
cluttered.
#3 - AREA encoding is useful for only few ...
One-dimensional Charts …
Solution to problem of restricted space? Create a custom chart.
New Data Set
One dimensional: !
Seat count by party
Grouped One dimensional: !
Seat count by party grouped by alliance
Grouped One-dimensional Charts
Party Alliance Percentage
A NDA 38%
B NDA 25%
C NDA 20%
D UPA 15%
E Others 2%
Grouped One-dimensional Charts
Group various bubbles by colours
Party Alliance Percentage
A NDA 38%
B NDA 25%
C NDA 20%
D ...
Grouped One-dimensional Charts
Group various bubbles by colours
Party Alliance Percentage
A NDA 38%
B NDA 25%
C NDA 20%
D ...
New Data Set
One dimensional: !
Seat count by party
Grouped One dimensional: !
Seat count by party grouped by alliance
Two...
Two-dimensional Charts
Plot two data points
Party Constituency
A Z
B Y
C X
D V
E W
23Visual encoding: Position, Length
Two-dimensional Charts…
Connect the dots and you get a line chart.
Two-dimensional Charts…
Scatter Line Area
Bar Column Spider
All these charts require the same data.#5 - Number of dimensio...
New Data Set
One dimensional: !
Seat count by party
Grouped One dimensional: !
Seat count by party grouped by alliance
Two...
Weighted Two-dimensional Charts
This is a 2d chart.
Weighted Two-dimensional Charts …
Let’s add weight to it, hence now we have three data points
X axis Y axis Weight
A Z 40
...
Weighted Two-dimensional Charts …
Weighted Scatter Circle Comparison
All these charts require the same data.#6 - You can a...
New Data Set
One dimensional: !
Seat count by party
Grouped One dimensional: !
Seat count by party grouped by alliance
Two...
Grouped Weighted Two-dimensional Charts
Grouped Weighted Scatter Grouped Circle Comparison
31Visual encoding: Position, Le...
Multi-series Two-dimensional Charts …
RangeGanttMulti-series Line
Group Column Stack Column Group Stack Column
Stack Area ...
Multi-series Two-dimensional Charts …
What is right and wrong here?
Source: livemint.com
Is the equities rally percolating...
Multi-series Two-dimensional Charts …
What is right and wrong here?
Source: livemint.com
Is the equities rally percolating...
Multi-series Two-dimensional Charts …
What is right and wrong here?
Good parts:!
• Y axis from 97 instead of 0
Source: liv...
Multi-series Two-dimensional Charts …
What is wrong here?
Source: livemint.com
Does IMF wear rose-tinted glasses?
Multi-series Two-dimensional Charts …
What is wrong here?
Source: livemint.com
Problems:!
• Cannot find the IMF line.
Does ...
Multi-series Two-dimensional Charts …
What is wrong here?
Source: livemint.com
Does IMF wear rose-tinted glasses?
Problems...
New Data Set
All the data we encountered so far was RDBMS i.e. could fit in a SpreadSheet.
(rows and columns). !
!
Sometime...
Tree Charts
{
"name": "root",
"children": [
{
"name": "A",
"children": [
{"name": "A1"},
{"name": "A2"},
{"name": "A3"},
{...
Tree Charts
Dendrogram Circular Dendrogram
Grouped Weighted Tree Charts
Packed Circle
Sunburst Tree Rectangle
Tree Bar
Grouped Weighted Tree
42Visual encoding: Posit...
Grouped Weighted Tree Charts
Sunburst
43Visual encoding: Position, Size, Colour
Grouped Multi-level Relationship Charts
{
“nodes”: [
{“name”: “A”, “group”: “G1”},
{“name”: “B”, “group”: “G2”},
…
],
"rel...
Grouped Multi-level Relationship Charts
Graph
Collapsible Graph
Hive
#9 - Look for relationships across data sets.
Weighted Grouped Multi-level Relationship Charts
Sankey
46Visual encoding: Position, Color, Size
Case: Mumbai Local Fare Chart
A fare exists for travel between station "A" and “B”. Hence, it is a relationship
chart.
Case: Mumbai Local Fare Chart
Matrix
Half Matrix
[
{"node1": "A", "node2": "B", "weight": 300},
{"node1": "A", "node2": "C...
Case: Mumbai Local Fare Chart
49
#9 - Look for limitations. They can help you improve design.
Weighted Two-level Relationship Charts …
Chord
Number of people travel between various stations
• One dimensional charts!
• Grouped one dimensional charts!
!
• Two dimensional charts!
• Weighted Two dimensional charts!...
The same data can be visualized in many (MANY!)
ways. Without exploring the data, you will end up
visualizing all your dat...
One
Dimension
Two
Dimension
Multi-
Dimension
Relationship Hierarchical Geo Maps
Dimension: Time N Y Y N N N
Dimension:
Gro...
List of Visual Encodings
Source: http://complexdiagrams.com/properties
Case Study #1:
Let’s apply what we learnt
IPL Score Card
ESPNCricInfo Score Card
56
57
Ball by ball!
Commentary
Per Batsman Statistics
Per Bowler Statistics
Fall of Wickets
Partnerships
Two innings Pre-matc...
Overs: Most important data-point
1. Overs = Time!
2. One over !
1. has_many balls!
2. has_one bowler!
3. has_many batsmen!...
Ball by ball Commentary
Partnerships
Combine the two
Weighted two-dimensional chart
Y-axis: Balls per over
X-axis: Overs + Bowlers
Gantt chart
Y-axis: Batsmen
...
Putting it all together
Let’s see it live
http://www.firstpost.com/cricket-live-score/IPL/1-jun-2014-
kolkata-knight-riders-versus-kings-xi-punjab/...
Less reading. No scrolling. More awareness.
Case Study #2:
Let’s apply what we learnt
Election Counting Day
Election Counting Day
Data Set:!
• India has 50+ regional parties and two national parties.!
• During Election Counting Da...
Election Counting Day
Data Set:!
• India has 50+ regional parties and two national parties.!
• During Election Counting Da...
Choosing the right Grouped Weighted Tree Chart
Packed Circle
Sunburst Tree Rectangle
Tree Bar
Grouped Weighted Tree
68Visu...
Election Counting Day …
Sunburst
Sansad Chart
Sansad Chart
Focus on what is most imp.!
Alliance is more imp. than Party. We spent 200% more time reversing hierarchy
Let’s see it live
http://firstpost.com/election-results
Summary
1. Study properties and relationships of your Data Set!
2. Use your visual encodings wisely
Challenges in Data Journalism
Data Collection What’s the story Visualize Story
Journalist
Developer
Designer
• Govt. data!
• APIs!
• Scrape!
• Mine web!...
Data Driven Stories Visualization App
Day-to-day short stories derived
from data
Big apps. to educate large and
important ...
Format #1 - Data Driven Stories
Source: http://factchecker.in/data-are-crimes-against-scheduled-castes-on-an-upswing-in-in...
Format #2 - Visualization Apps
Data Collection What’s the story Visualize Story
• Govt. data!
• APIs!
• Scrape!
• Mine web!
• PDFs
• Clean the data!
• Mo...
High Level
!
1. Quick access to appropriate data set

2. Quick analysis of this data

3. Consistently churn out neat chart...
High Level
!
1. Quick access to appropriate data set

2. Quick analysis of this data

3. Consistently churn out neat chart...
High Level
!
1. Quick access to appropriate data set

2. Quick analysis of this data

3. Consistently churn out neat chart...
What we are doing about it
In-house tool called "Backstage"
#1 - Instead of waiting for data to be
standardised, we want to make large scale, high-
velocity, multi-format, data extra...
Data Visualization company => Data and Visualization company

!
!
Effective Data Journalism leverages: You will end up NoSQ...
We are at @pykih
Fun fact: The word pykih came to us
in a CAPTCHA. That’s the day we
decided that till we do good work it
...
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Visualizing Data Journalism (HasGeek Fifth Elephant)

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The presentation is broken into two parts. First, it introduces the various core fundamentals of data visualization and then we apply those fundamentals in two case studies. The second part revolves around challenges with data journalism and what is pykih doing about them.

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Visualizing Data Journalism (HasGeek Fifth Elephant)

  1. 1. Visualizing Data Journalism Ritvvij Parrikh, Founder, www.pykih.com ! ! Fifth Elephant, Delhi Run-up Event, India Today Mediaplex, June 14, 2014
  2. 2. Pykih is a data Visualization company. We build custom visual representations of large data sets to make data actionable for readers. We have satisfied customers in six countries. Introduction
  3. 3. • Data Viz. • Theory • Case Study 1 • Case Study 2 • Summary • Challenges in Data Journalism • What we are doing about it for ourselves Agenda
  4. 4. Data Visualization
  5. 5. Let’s explore the humble pie chart… Party Percentage E 38% D 25% C 20% B 15% A 2% Break the whole into parts.
  6. 6. Let’s explore the humble pie chart… Party Percentage E 38% D 25% C 20% B 15% A 2% Break the whole into parts. Data: One dimensional Visual Encoding: Area
  7. 7. New Terms • Dimension: Columns by which you group data.! ! • Facts: Numbers that you can count, sum, average, etc.! ! • Examples:! • Seat count by party! • Seat count by party and state! ! • Visual Encoding: Area, Position, Colour, Length, Thickness, etc.
  8. 8. One-dimensional Charts PIE is a one-dimensional chart
  9. 9. One-dimensional Charts … A pie could have been a random shape broken by percentage
  10. 10. One-dimensional Charts … Pie Amoeba Percentage! Rectangle Donut Percentage! Triangle Bubble Election Donut Funnel Percentage Bar Percentage ! Column #1 - The same data can be Visualized in many (MANY!) different ways.
  11. 11. One-dimensional Charts … Source: thehindu.com What is wrong here?
  12. 12. One-dimensional Charts … What is wrong here? Problems:! • Colour communicates no data! • 3D communicates no data Source: thehindu.com
  13. 13. One-dimensional Charts … Source: thehindu.com #2 - Your goal is to communicate data. Wrong use of visual encoding confuses. Problems:! • Colour communicates no data! • 3D communicates no data
  14. 14. One-dimensional Charts … Source: firstpost.com What is wrong here?
  15. 15. One-dimensional Charts … What is wrong here? Problems:! • Colour! • Too many values. Too cluttered. Source: firstpost.com
  16. 16. One-dimensional Charts … Problems:! • Colour! • Too many values. Too cluttered. #3 - AREA encoding is useful for only few values after which it is unreadable. Source: firstpost.com
  17. 17. One-dimensional Charts … Solution to problem of restricted space? Create a custom chart.
  18. 18. New Data Set One dimensional: ! Seat count by party Grouped One dimensional: ! Seat count by party grouped by alliance
  19. 19. Grouped One-dimensional Charts Party Alliance Percentage A NDA 38% B NDA 25% C NDA 20% D UPA 15% E Others 2%
  20. 20. Grouped One-dimensional Charts Group various bubbles by colours Party Alliance Percentage A NDA 38% B NDA 25% C NDA 20% D UPA 15% E Others 2%
  21. 21. Grouped One-dimensional Charts Group various bubbles by colours Party Alliance Percentage A NDA 38% B NDA 25% C NDA 20% D UPA 15% E Others 2% #4 - You can always fit in an extra dimension (GROUP) in charts using colour.
  22. 22. New Data Set One dimensional: ! Seat count by party Grouped One dimensional: ! Seat count by party grouped by alliance Two dimensional: ! Which party won in which year
  23. 23. Two-dimensional Charts Plot two data points Party Constituency A Z B Y C X D V E W 23Visual encoding: Position, Length
  24. 24. Two-dimensional Charts… Connect the dots and you get a line chart.
  25. 25. Two-dimensional Charts… Scatter Line Area Bar Column Spider All these charts require the same data.#5 - Number of dimensions in data determines which chart to use
  26. 26. New Data Set One dimensional: ! Seat count by party Grouped One dimensional: ! Seat count by party grouped by alliance Two dimensional: ! Which party won in which constituency Weighted Two dimensional: ! Which party won in which constituency by what vote margin
  27. 27. Weighted Two-dimensional Charts This is a 2d chart.
  28. 28. Weighted Two-dimensional Charts … Let’s add weight to it, hence now we have three data points X axis Y axis Weight A Z 40 B Y 20 C X 1 D V 300 E W 60 28Visual encoding: Position, Length, Area
  29. 29. Weighted Two-dimensional Charts … Weighted Scatter Circle Comparison All these charts require the same data.#6 - You can always fit in an extra fact (WEIGHT) in charts using size.
  30. 30. New Data Set One dimensional: ! Seat count by party Grouped One dimensional: ! Seat count by party grouped by alliance Two dimensional: ! Which party won in which constituency Weighted Two dimensional: ! Which party won in which constituency by what vote margin Grouped Weighted Two dimensional: ! Which party won in which constituency by what vote margin grouped by alliance
  31. 31. Grouped Weighted Two-dimensional Charts Grouped Weighted Scatter Grouped Circle Comparison 31Visual encoding: Position, Length, Area, Colour
  32. 32. Multi-series Two-dimensional Charts … RangeGanttMulti-series Line Group Column Stack Column Group Stack Column Stack Area Stack Percentage Area Add more dimensions in creative ways.
  33. 33. Multi-series Two-dimensional Charts … What is right and wrong here? Source: livemint.com Is the equities rally percolating into the broader market?
  34. 34. Multi-series Two-dimensional Charts … What is right and wrong here? Source: livemint.com Is the equities rally percolating into the broader market? Bad parts:! • BSE Small-cap lines is not visible and that’s the story.
  35. 35. Multi-series Two-dimensional Charts … What is right and wrong here? Good parts:! • Y axis from 97 instead of 0 Source: livemint.com Is the equities rally percolating into the broader market? Bad parts:! • BSE Small-cap lines is not visible and that’s the story. #7 - Purpose of line chart is to show trend. Focus on it.
  36. 36. Multi-series Two-dimensional Charts … What is wrong here? Source: livemint.com Does IMF wear rose-tinted glasses?
  37. 37. Multi-series Two-dimensional Charts … What is wrong here? Source: livemint.com Problems:! • Cannot find the IMF line. Does IMF wear rose-tinted glasses?
  38. 38. Multi-series Two-dimensional Charts … What is wrong here? Source: livemint.com Does IMF wear rose-tinted glasses? Problems:! • Cannot find the IMF line. #8 - Highlight the story for the user. Use color to highlight, not confuse.
  39. 39. New Data Set All the data we encountered so far was RDBMS i.e. could fit in a SpreadSheet. (rows and columns). ! ! Sometimes data is more complex. It can have“relationships”. ! ! Types of relationships:! • Hierarchy / Tree! • Multi-level relationships
  40. 40. Tree Charts { "name": "root", "children": [ { "name": "A", "children": [ {"name": "A1"}, {"name": "A2"}, {"name": "A3"}, {"name": "A4"} ] 40Visual encoding: Position
  41. 41. Tree Charts Dendrogram Circular Dendrogram
  42. 42. Grouped Weighted Tree Charts Packed Circle Sunburst Tree Rectangle Tree Bar Grouped Weighted Tree 42Visual encoding: Position, Size, Colour
  43. 43. Grouped Weighted Tree Charts Sunburst 43Visual encoding: Position, Size, Colour
  44. 44. Grouped Multi-level Relationship Charts { “nodes”: [ {“name”: “A”, “group”: “G1”}, {“name”: “B”, “group”: “G2”}, … ], "relations": [ {"from": “A”, "to": “B”}, {"from": “A”, "to": “C”}, … ] 44Visual encoding: Position
  45. 45. Grouped Multi-level Relationship Charts Graph Collapsible Graph Hive #9 - Look for relationships across data sets.
  46. 46. Weighted Grouped Multi-level Relationship Charts Sankey 46Visual encoding: Position, Color, Size
  47. 47. Case: Mumbai Local Fare Chart A fare exists for travel between station "A" and “B”. Hence, it is a relationship chart.
  48. 48. Case: Mumbai Local Fare Chart Matrix Half Matrix [ {"node1": "A", "node2": "B", "weight": 300}, {"node1": "A", "node2": "C", "weight": 900}, … ]
  49. 49. Case: Mumbai Local Fare Chart 49 #9 - Look for limitations. They can help you improve design.
  50. 50. Weighted Two-level Relationship Charts … Chord Number of people travel between various stations
  51. 51. • One dimensional charts! • Grouped one dimensional charts! ! • Two dimensional charts! • Weighted Two dimensional charts! • Grouped Two dimensional charts! • Grouped Weighted Two dimensional charts! ! • Multi-dimensional Charts! ! • Tree Charts! • Grouped Weighted Tree Charts! ! • Multi-level Relationships Charts! • Grouped Weighted Multi-level Relationships Charts! ! • Two-level Relationships Charts! • Grouped Weighted Two-level Relationships Charts Taxonomy of Standard Data Visualizations
  52. 52. The same data can be visualized in many (MANY!) ways. Without exploring the data, you will end up visualizing all your data in pies, lines and bars. Most Imp. Lesson
  53. 53. One Dimension Two Dimension Multi- Dimension Relationship Hierarchical Geo Maps Dimension: Time N Y Y N N N Dimension: Group Y Y Y Y Y Y Fact: Weight N Y Y Y Y Y Group and Weight N Y Y Y Y N Fact: Many values May be Y Y Y Y Y Multiple levels / Zoomable N N N Y Y Y Implications
  54. 54. List of Visual Encodings Source: http://complexdiagrams.com/properties
  55. 55. Case Study #1: Let’s apply what we learnt IPL Score Card
  56. 56. ESPNCricInfo Score Card 56
  57. 57. 57 Ball by ball! Commentary Per Batsman Statistics Per Bowler Statistics Fall of Wickets Partnerships Two innings Pre-match: Toss, Playing 11, Location, Time Post-match: Win, by how much, Man of the match Second Innings: Current Run Rate, Required Run Rate, Target score
  58. 58. Overs: Most important data-point 1. Overs = Time! 2. One over ! 1. has_many balls! 2. has_one bowler! 3. has_many batsmen! 3. Existence of batsmen across overs is partnerships! 4. Partnerships and Fall of wickets are the same different data set
  59. 59. Ball by ball Commentary
  60. 60. Partnerships
  61. 61. Combine the two Weighted two-dimensional chart Y-axis: Balls per over X-axis: Overs + Bowlers Gantt chart Y-axis: Batsmen X-axis: Overs + Bowlers All other “zoomable" information is shown via interactions
  62. 62. Putting it all together
  63. 63. Let’s see it live http://www.firstpost.com/cricket-live-score/IPL/1-jun-2014- kolkata-knight-riders-versus-kings-xi-punjab/2173/175977
  64. 64. Less reading. No scrolling. More awareness.
  65. 65. Case Study #2: Let’s apply what we learnt Election Counting Day
  66. 66. Election Counting Day Data Set:! • India has 50+ regional parties and two national parties.! • During Election Counting Day (live), seats are either “Leading” or “Won”! ! Data Properties / Relationships:! • Hierarchical Relation between Alliance and Party! • Won is confirmed. Leading is transient.! ! What did readers want to know this Election:! • How badly would UPA lose! • How big would be the BJP victory! • How big would the impact of AAP would be! ! Real world facts to inspire design! • BJP is a right wing party! • AAP is left most followed by UPA! • The Sansad Hall is a semi-circle
  67. 67. Election Counting Day Data Set:! • India has 50+ regional parties and two national parties.! • During Election Counting Day (live), seats are either “Leading” or “Won”! ! Data Properties / Relationships:! • Hierarchical Relation between Alliance and Party! • Won is confirmed. Leading is transient.! ! What did readers want to know this Election:! • How badly would UPA lose! • How big would be the BJP victory! • How big would the impact of AAP would be! ! Real world facts to inspire design:! • BJP is a right wing party! • AAP is left most followed by UPA! • The Sansad Hall is a semi-circle —> Group —> Tree —> Weight —> Limitation } Hence, all other parties! can be clubbed into ! other —> Shape —> Placement}
  68. 68. Choosing the right Grouped Weighted Tree Chart Packed Circle Sunburst Tree Rectangle Tree Bar Grouped Weighted Tree 68Visual encoding: Position, Size, Colour
  69. 69. Election Counting Day … Sunburst
  70. 70. Sansad Chart
  71. 71. Sansad Chart Focus on what is most imp.! Alliance is more imp. than Party. We spent 200% more time reversing hierarchy
  72. 72. Let’s see it live http://firstpost.com/election-results
  73. 73. Summary 1. Study properties and relationships of your Data Set! 2. Use your visual encodings wisely
  74. 74. Challenges in Data Journalism
  75. 75. Data Collection What’s the story Visualize Story Journalist Developer Designer • Govt. data! • APIs! • Scrape! • Mine web! • PDFs • Clean the data! • Model the data! • Investigate • Design! • Build Write Technology is an integral part of data journalism. Steps in data journalism
  76. 76. Data Driven Stories Visualization App Day-to-day short stories derived from data Big apps. to educate large and important event e.g. budget, election, etc. Formats in data journalism
  77. 77. Format #1 - Data Driven Stories Source: http://factchecker.in/data-are-crimes-against-scheduled-castes-on-an-upswing-in-india/ Badaun Case —> Find legit Data —> Analyse —> Plot —> Story
  78. 78. Format #2 - Visualization Apps
  79. 79. Data Collection What’s the story Visualize Story • Govt. data! • APIs! • Scrape! • Mine web! • PDFs • Clean the data! • Model the data! • Investigate • Design! • Build Write Format: Visualization app Format: Data Driven Stories Journalist Developer Designer Journalist Implication
  80. 80. High Level ! 1. Quick access to appropriate data set 2. Quick analysis of this data 3. Consistently churn out neat charts, graphs and maps Challenges
  81. 81. High Level ! 1. Quick access to appropriate data set 2. Quick analysis of this data 3. Consistently churn out neat charts, graphs and maps ! Technical ! 1. Live Data Modelling 2. SEO 3. How to handle high traffic Challenges
  82. 82. High Level ! 1. Quick access to appropriate data set 2. Quick analysis of this data 3. Consistently churn out neat charts, graphs and maps ! Technical ! 1. Live Data Modelling 2. SEO 3. How to handle high traffic ! From pykih perspective ! 1. How do you consistently build beautiful, real-time Visualizations? Challenges
  83. 83. What we are doing about it In-house tool called "Backstage"
  84. 84. #1 - Instead of waiting for data to be standardised, we want to make large scale, high- velocity, multi-format, data extraction durable. ! #2 - Instead of expecting data-users / journalists to have analytical skills, we are: • simplifying exploration of large data sets • automating extraction of metadata from data sets • simplifying assisted data standardisation • building tools for assisted analysis ! #3 - Instead of expecting data-users / journalists to Visualize data correctly, we are attempting automate meta-data driven Visualization ! Other Experiments • A data-driven blogging software • Configuration Editor Principles —> Demo the worker —> Demo the census dashboard —> ISO example —> Demo NLP based Date Standardiser —> Story is in the outliers Example: If data is ordinal then colour automatically leverages saturation and if data is ordinal then colour is distinct
  85. 85. Data Visualization company => Data and Visualization company ! ! Effective Data Journalism leverages: You will end up NoSQL, Memory based databases, NLP, OLAP modelling, Free Text Search, Statistics, etc. Summary
  86. 86. We are at @pykih Fun fact: The word pykih came to us in a CAPTCHA. That’s the day we decided that till we do good work it does not matter what we are called.

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