Storytelling through data

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STORYTELLING THROUGH DATA

GARTNER QUANT IMMERSION, 11 OCT 2017

A DATA VISUALISATION

CHALLENGE…

You will see 3 questions.

You have 30 seconds.

Try it!

Your timer

starts now

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HOW MANY NUMBERS ARE ABOVE 100?

23 32 71 72 58 87 11 77 70 16

17 21 56 44 68 51 84 20 60 40

37 8 107 14 12 41 69 14 18 71

62 55 59 64 33 55 71 58 103 92

101 56 45 34 43 15 73 78 6 93

39 53 22 26 26 94 60 82 99 74

11 12 36 67 70 71 97 59 73 99

75 74 69 69 51 48 2 66 92 98

15 10 41 58 104 94 92 84 74 82

12 52 10 57 33 77 88 81 81 91

15 56 25 30 21 7 66 66 78 87

29 23 5 34 11 96 74 99 99 88

37 10 43 15 50 71 65 60 101 98

46 34 19 102 57 70 95 84 63 91

3 34 39 37 60 81 65 63 9 71

48 46 25 50 22 64 91 76 71 79

1

4

HOW MANY NUMBERS ARE BELOW 10?

23 32 71 72 58 87 11 77 70 16

17 21 56 44 68 51 84 20 60 40

37 8 107 14 12 41 69 14 18 71

62 55 59 64 33 55 71 58 103 92

101 56 45 34 43 15 73 78 6 93

39 53 22 26 26 94 60 82 99 74

11 12 36 67 70 71 97 59 73 99

75 74 69 69 51 48 2 66 92 98

15 10 41 58 104 94 92 84 74 82

12 52 10 57 33 77 88 81 81 91

15 56 25 30 21 7 66 66 78 87

29 23 5 34 11 96 74 99 99 88

37 10 43 15 50 71 65 60 101 98

46 34 19 102 57 70 95 84 63 91

3 34 39 37 60 81 65 63 9 71

48 46 25 50 22 64 91 76 71 79

2

5

WHICH QUADRANT HAS THE HIGHEST TOTAL?

23 32 71 72 58 87 11 77 70 16

17 21 56 44 68 51 84 20 60 40

37 8 107 14 12 41 69 14 18 71

62 55 59 64 33 55 71 58 103 92

101 56 45 34 43 15 73 78 6 93

39 53 22 26 26 94 60 82 99 74

11 12 36 67 70 71 97 59 73 99

75 74 69 69 51 48 2 66 92 98

15 10 41 58 104 94 92 84 74 82

12 52 10 57 33 77 88 81 81 91

15 56 25 30 21 7 66 66 78 87

29 23 5 34 11 96 74 99 99 88

37 10 43 15 50 71 65 60 101 98

46 34 19 102 57 70 95 84 63 91

3 34 39 37 60 81 65 63 9 71

48 46 25 50 22 64 91 76 71 79

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A DATA VISUALISATION

CHALLENGE…

We’ll answer the same questions again.

But with simple visual cues.

See how long it takes.

Your timer

starts now

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23 32 71 72 58 87 11 77 70 16

17 21 56 44 68 51 84 20 60 40

37 8 107 14 12 41 69 14 18 71

62 55 59 64 33 55 71 58 103 92

101 56 45 34 43 15 73 78 6 93

39 53 22 26 26 94 60 82 99 74

11 12 36 67 70 71 97 59 73 99

75 74 69 69 51 48 2 66 92 98

15 10 41 58 104 94 92 84 74 82

12 52 10 57 33 77 88 81 81 91

15 56 25 30 21 7 66 66 78 87

29 23 5 34 11 96 74 99 99 88

37 10 43 15 50 71 65 60 101 98

46 34 19 102 57 70 95 84 63 91

3 34 39 37 60 81 65 63 9 71

48 46 25 50 22 64 91 76 71 79

HOW MANY NUMBERS ARE ABOVE 100? 1

8

HOW MANY NUMBERS ARE BELOW 10?

23 32 71 72 58 87 11 77 70 16

17 21 56 44 68 51 84 20 60 40

37 8 107 14 12 41 69 14 18 71

62 55 59 64 33 55 71 58 103 92

101 56 45 34 43 15 73 78 6 93

39 53 22 26 26 94 60 82 99 74

11 12 36 67 70 71 97 59 73 99

75 74 69 69 51 48 2 66 92 98

15 10 41 58 104 94 92 84 74 82

12 52 10 57 33 77 88 81 81 91

15 56 25 30 21 7 66 66 78 87

29 23 5 34 11 96 74 99 99 88

37 10 43 15 50 71 65 60 101 98

46 34 19 102 57 70 95 84 63 91

3 34 39 37 60 81 65 63 9 71

48 46 25 50 22 64 91 76 71 79

2

9

WHICH QUADRANT HAS THE HIGHEST TOTAL? 3

23 32 71 72 58 87 11 77 70 16

17 21 56 44 68 51 84 20 60 40

37 8 107 14 12 41 69 14 18 71

62 55 59 64 33 55 71 58 103 92

101 56 45 34 43 15 73 78 6 93

39 53 22 26 26 94 60 82 99 74

11 12 36 67 70 71 97 59 73 99

75 74 69 69 51 48 2 66 92 98

15 10 41 58 104 94 92 84 74 82

12 52 10 57 33 77 88 81 81 91

15 56 25 30 21 7 66 66 78 87

29 23 5 34 11 96 74 99 99 88

37 10 43 15 50 71 65 60 101 98

46 34 19 102 57 70 95 84 63 91

3 34 39 37 60 81 65 63 9 71

48 46 25 50 22 64 91 76 71 79

DRAW FOCUS TO PRIORITIES

THIS IS ONE OF THE REASONS TO VISUALIZE DATA

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CRICKET

FASTEST SCORERS

“I’ve always been curious… who

among India’s prolific one-day

run-getters had the best strike

rate?

Sachin?

Sehwag?

What about the rest of the world?

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LET’S TAKE ONE DAY CRICKET DATA

Country Player Runs ScoreRate MatchDate Ground Versus

Australia Michael J Clarke 99* 93.39 30-06-2010The Oval England

Australia Dean M Jones 99* 128.57 28-01-1985Adelaide Oval Sri Lanka

Australia Bradley J Hodge 99* 115.11 04-02-2007Melbourne Cricket Ground New Zealand

India Virender Sehwag 99* 99 16-08-2010Rangiri Dambulla International Stad. Sri Lanka

New Zealand Bruce A Edgar 99* 72.79 14-02-1981Eden Park India

Pakistan Mohammad Yousuf 99* 95.19 15-11-2007Captain Roop Singh Stadium India

West Indies Richard B Richardson 99* 70.21 15-11-1985Sharjah CA Stadium Pakistan

West Indies Ramnaresh R Sarwan 99* 95.19 15-11-2002Sardar Patel Stadium India

Zimbabwe Andrew Flower 99* 89.18 24-10-1999Harare Sports Club Australia

Zimbabwe Alistair D R Campbell 99* 79.83 01-10-2000Queens Sports Club New Zealand

Zimbabwe Malcolm N Waller 99* 133.78 25-10-2011Queens Sports Club New Zealand

Australia David C Boon 98* 82.35 08-12-1994Bellerive Oval Zimbabwe

Australia Graeme M Wood 98* 63.22 11-01-1981Melbourne Cricket Ground India

England Ian J L Trott 98* 84.48 20-10-2011Punjab Cricket Association Stadium India

India Yuvraj Singh 98* 89.09 01-08-2001Sinhalese Sports Club Ground Sri Lanka

Ireland Kevin J O'Brien 98* 94.23 10-07-2010VRA Ground Scotland

Kenya Collins O Obuya 98* 75.96 13-03-2011M.Chinnaswamy Stadium Australia

Netherlands Ryan N ten Doeschate 98* 73.68 01-09-2009VRA Ground Afghanistan

New Zealand James E C Franklin 98* 142.02 07-12-2010M.Chinnaswamy Stadium India

Pakistan Ijaz Ahmed 98* 112.64 28-10-1994Iqbal Stadium South Africa

South Africa Jacques H Kallis 98* 74.24 06-02-2000St George's Park Zimbabwe

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Against which countries are

higher averages scored?

Which countries’ players

score more per match?

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Which player scores the

most per ball?

The player with the highest strike

rate is an obscure South African

whose name most of us have never

heard of.

In fact, this list is filled with players

we have never heard of.

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ODI STRIKE RATES OF THE WORLD

We want to see the

prioritised performance.

That is, what is the strike

rate of the established

players?

LINK

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Rs 7,700 cr

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Dr Udayakumar

382 IPC cases

Pushparayan

380 IPC cases

SURFACE HIDDEN INSIGHT

THIS IS ONE OF THE REASONS TO VISUALIZE DATA

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100 Y

EA

RS

OF

IND

IA’S

WEA

TH

ER

1901

1911

1921

1931

1941

1951

1961

1971

1981

1991

2001

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

LINK

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RESTAURANT FOUND AN UNUSUAL DIP IN SALES

A restaurant chain had data for every single

transaction made over a few years. Plotting

this as a time series showed them nothing

unusual.

However, the same data on a calendar map

reveals a very different story.

Specifically, at the bottom left point-of-sale terminal, sales dips on every

Wednesday. At the bottom right point-of-sale terminal, sales rises on

every Wednesday (almost as if to compensate for the loss.)

It turns out that the manager closes the bottom-left counter every

Wednesday afternoon due to shortage of staff, assuming that it results in

no loss of sales. There is, however, a net loss every Wednesday.

A similar visual helped a telecom company identify specific days on which their competitors’ market

share rose significantly, enabling them to negate the strategy.

Communicating data visually is the most effective way to a shared understanding

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68% correlation

between AUD & EUR

Plot of 6 month daily

AUD - EUR values

Block of correlated

currencies

… clustered

hierarchically

ALLOW CONSISTENT INTERPRETATION

THIS IS ONE OF THE REASONS TO VISUALIZE DATA

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CONSISTENT CONCLUSIONS FROM DATA

Stock market crash?

Doesn’t look so bad.. This gives the right perspective

Source: http://www.cc.gatech.edu/~stasko/7450/index.html

The same dataset can lead to very

different conclusions.

Visualizations freeze the

rendering of data, allowing a

consistent (and hopefully correct)

interpretation.

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WINNING PARTIES

In the 2004 election to Lok

Sabha there were 1,351

candidates from 6 National

parties, 801 candidates from

36 State parties, 898

candidates from officially

recognised parties and 2385

Independent candidates.

The Congress (INC) won 145

seats in the 2004 elections.

BJP won 138, coming a close

second.

The constituencies where

each party won is shown

here.

Party BJP BSP CPM INC RJD SP

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Party BJP BSP CPM INC RJD SPWINNING PARTIES

In the 2004 election to Lok

Sabha there were 1,351

candidates from 6 National

parties, 801 candidates from

36 State parties, 898

candidates from officially

recognised parties and 2385

Independent candidates.

The Congress (INC) won 145

seats in the 2004 elections.

BJP won 138, coming a close

second.

The constituencies where

each party won is shown

here.

WHAT SHOULD I TALK ABOUT NOW?I’VE ALWAYS HAD A PROBLEM DETERMINING AUDIENCE INTEREST

We have internal

information. Getting

information from outside is

our challenge. There’s no way

of doing that.

– Senior Editor

Leading Media Company

“

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INDIA’S RELIGIONS LINK

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AUSTRALIA’S RELIGIONS LINK

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LINK

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WHAT DO PEOPLE LOOKING FOR IN VISUALIZATION?

USA India

data visualization tools

data visualization software

data visualization examples

data visualization jobs

data visualization tools

data visualization techniques

data visualization examples

data visualization software

Tools &

Software

Techniques &

Examples

WHAT TOOLS SHOULD YOU USE?THIS IS ONE OF THE MOST FREQUENT QUESTIONS I’M ASKED

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DATA SCIENCE TOOLS

Alteryx

Amazon EC2

Azure ML

BigQuery

Birst

Caffe

Cassandra

Cloud Compute

Cloudera

Cognos

CouchDB

D3

Decision tree

ElasticSearch

Excel

Gephi

ggplot2

Hadoop

HP Vertica

IBM Watson

Impala

Julia

Jupyter Notebook

Kafka

Kibana

Kinesis

Lambda

Logstash

MapR

MapReduce

Matplotlib

Microstrategy

MongoDB

NodeXL

Pandas

Pentaho

Pivotal

PowerPoint

Qlikview

R

R Studio

Random Forest

Redis

Redshift

Regression

Revolution R

S3

SAP Hana

SAS

Spark

Spotfire

SPSS

SQL Server

Stanford NLP

Storm

SVM

Tableau

TensorFlow

Teradata

Theano

Thrift

Torch

Weka

Word2Vec

The tool does not matter. A person’s skill with the tool does.

Pick the person. Let them pick the tool.

I’M FAMILIAR WITH EXCEL

I TURN TO IT AS A FIRST CHOICE FOR ALMOST EVERYTHING

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LINK

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LINK

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LINK

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Profits Made: Over the last 6

years, you would have beaten a 10%

Inflation about 82% of the time and lost out

about 18% of the time. So, mostly, you would

have made money on Cipla with an average

return of 14.9%.

Highest Returns: An average return of 14.1%

has been observed when held for a period of one year.

with a maximum of 79.6% if sold in Dec 2009, after being

held for a year. And a maximum of 486.9% if sold at the end

of Nov 2007 after holding for a month. The highest stock price

was Rs 414 in Nov/Dec 2012.

-50% +50%returns

WHEN TO

INVEST

This visual shows the

returns from buying

Cipla’s stock on any

given month, and

selling it in another.

The color of each cell is

the return (red is low,

green is high) if you

had invested in the

stock in a given month

and sold it on another.

For example this mild

red is the slightly

negative return if you

had bought Cipla stock

in Mar 2011 (the row)

and sold it in Jun 2011

(the column).

Link

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LINK

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LINK

I’M FAMILIAR WITH POWERPOINT

IT’S ALSO A TOOL MOST OF OUR CLIENTS USE

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BJP

INC

JD(S)

IND

BJP sweep

INC

majority

80,000 voters

(Shivajinagar)

170,000 voters

(Bangalore

South)

KARNATAKA ASSEMBLY ELECTIONS: WINNING PARTIES (2008)

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PORTFOLIO PERFORMANCE

VISUAL

Worldwide$288.0mn

A: Accelerate$68.9mn

B: Build$77.2mn

C: Cut down$141.9mn

Worldwide:

$288 mn

The visualization shows the market

opportunities across various countries to

identify areas of focus. This chart has

been built as an interactive-app to

present the key findings, while letting

user click-through and drill-down to a

custom view across 4 different levels.

LINK

46LINK

TOOLS DO HELP, OF COURSE

FOR SOME THINGS, YOU NEED THE RIGHT PLATFORM

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How does Mahabharata, one of the largest epics with 1.8

million words lend itself to text analytics?

Can this ‘unstructured data’ be processed to extract

analytical insights?

What does sentiment analysis of this tome convey?

Is there a better way to explore relations between

characters?

How can closeness of characters be analyzed & visualized?

VISUALISING THE MAHABHARATA

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Recruiting top quality developers is always a problem. We decided to use an algorithmic approach and pulled out the social network of developers on Github (a social network for open source code).

In this visualization, each circle is a person. The size of the circle represents the number of followers. Larger circles have more followers (but not in proportion – it’s a log scale.)

The circle’s color represents the city the programmer’s live in. This visual is a slice showing the tale of two cities: Bangalore and Singapore

Two people are connected if one follows the other. This leads to a clustering of people in the form of a network.

Here, you can see that Bangalore and Singapore are reasonably well connected cities. Bangalore has more developers, but Singapore has more popular ones (larger circles).

However, the interaction between Bangalore and Singapore are few and far between. But for a few people across both cities, like:

… etc.

Sudar, Yahoo!Anand C, ConsultantKiran, HasgeekAnand S, Gramener

Mugunth, Steinlogic Honcheng, buUukSau Sheong, HP LabsLim Chee Aung

Bangalore

Singapore

1 follower

100 followers

A follows B (or)

B follows A

Most followed in Bangalore

Most followed in Singapore

Ciju CherianLin JunjieAmudhi Sebastian

There are, of course, a number of smaller independent circles – people who are not connected to others in the same city. (They may be connected to people in other cities.)

Apart from this, there are a few small networks of connected people – often people within the same company or start-up – who form a community of their own.

THE SOCIAL TALE OF TWO CITIES: BANGALORE & SINGAPORE

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SERVICE REQUEST WORKFLOW

THE MEDIUM & AUDIENCE MATTER

ALIGN THE STORY TO WHO WILL CONSUME IT AND HOW

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GRAMENER AND CNN-IBN COVERED THE 2014

GENERAL ELECTIONS

19 M

VIDEO

3 M

VIDEO

MediaMicrosoft

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GRAMENER & TIMES NOW COVERED THE 2016

STATE ELECTIONSMedia

3 M

VIDEO

4 M

VIDEO

Continued… PlatformMicrosoft

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HOW SEATS WERE RE-DISTRIBUTED ACROSS PARTIES

THIS CHORD DIAGRAM WAS THE MOST USED VISUAL DURING THE SHOW

LINK

MediaContinued…

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WHERE DID THE MOST NUMBER OF CANDIDATES CONTEST?

Media

LINK

Continued…

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WE DESIGN OUR OWN WALLS TOO…

Design

57Public SectorVisualizationPlatform

58Design

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VIJAY KARNATAKA’S PUBLICATION ON CANDIDATE WEALTH LINK

Media

Based on candidate declarations, Karnataka 2013

Continued… Microsoft

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IMPACT OF THE BUDGET ON STOCK PRICES LINK

Financial ServicesNarrativesMediaPublic SectorFinancePlatform

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WORLD BANK: INNOVATION, TECHNOLOGY & ENTREPRENEURSHIP

Does access to new Technology facilitate Innovation? Does it

facilitate Entrepreneurship? The Global Information Technology

Report findings tell us that "innovation is increasingly based on

digital technologies and business models, which can drive economic

and social gains from ICTs...".

We were curious about whether the data on TCData360 could tell a

story about influential factors on innovation and entrepreneurship.

With over 1800 indicators, we focused on the Networked Readiness

Index, as it has indicators on entrepreneurship, technology, and

innovation.

LINK

SocietyPlatform

… BUT CONTENT IS KING

KEEP THE STORY AT THE FOREFRONT

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PREDICTING MARKS

EDUCATION

“What determines a child’s marks?

Do girls score better than boys?

Does the choice of subject

matter?

Does the medium of instruction

matter?

Does community or religion

matter?

Does their birthday matter?

Does the first letter of their name

matter?

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TN CLASS X: ENGLISH

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

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TN CLASS X: SOCIAL SCIENCE

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

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TN CLASS X: MATHEMATICS

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

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ICSE 2013 CLASS XII: TOTAL MARKS

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PERFORMANCE

DRIVERS

Do girls score more than

boys, or is it the other way

around?

Gender is a known driver of

performance. Girls generally

score higher. There is

considerable variation across

subjects, however. The

differences in sciences is

minimal. But languages,

commerce and economics

give girls a significant edge.

There is also a correlation

between girls’ dropout ratio

and their over-performance

– indicating perhaps that the

smarter girls tend to stay

back in school.

Subject Girs higher by Girls Boys

Physics 0 119 119

Chemistry 1 123 122

English 4 130 126

Computers 6 137 131

Biology 6 129 123

Mathematics 11 123 112

Language 11 152 141

Accounting 12 138 126

Commerce 13 127 114

Economics 16 142 126

WHO SCORES MORE? BOYS OR

GIRLS?

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The marks shoot

up for Aug borns

… and peaks for

Sep-borns

120 marks out of

1200 explainable

by month of birth

An identical pattern was observed in 2009 and 2010…

… and across districts, gender, subjects, and class X & XII.

“It’s simply that in Canada the eligibility

cut-off for age-class hockey is January

1. A boy who turns ten on January 2,

then, could be playing alongside

someone who doesn’t turn ten until the

end of the year—and at that age, in

preadolescence, a twelve-month gap in

age represents an enormous difference

in physical maturity.”

-- Malcolm Gladwell, Outliers

SUN SIGNS

Based on the results of the

20 lakh students taking the

Class XII exams at Tamil

Nadu over the last 3 years, it

appears that the month you

were born in can make a

difference of as much as 120

marks out of 1,200.

June borns

score the lowest

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This is a dataset (1975 – 1990) that has

been around for several years, and has

been studied extensively. Yet, a

visualization can reveal patterns that

are neither obvious nor well known.

For example,

• Are birthdays uniformly distributed?

• Do doctors or parents exercise the C-section option to move dates?

• Is there any day of the month that has unusually high or low births?

• Are there any months with relatively high or low births?

Very high births in September.

But this is fairly well known. Most

conceptions happen during the

winter holiday season

Relatively few births during the

Christmas and Thanksgiving

holidays, as well as New Year and

Independence Day.

Most people prefer not

to have children on the

13th of any month, given

that it’s an unlucky day

Some special days like April

Fool’s day are avoided, but

Valentine’s Day is quite

popular

More births Fewer births … on average, for each day of the year (from 1975 to 1990)

LET’S LOOK AT 15 YEARS OF US BIRTH DATA

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THE PATTERN IN INDIA IS QUITE DIFFERENT

This is a birth date dataset that’s

obtained from school admission data

for over 10 million children. When we

compare this with births in the US, we

see none of the same patterns.

For example,

• Is there an aversion to the 13th or is there a local cultural nuance?

• Are holidays avoided for births?

• Which months have a higher propensity for births, and why?

• Are there any patterns not found in the US data?

Very few children are born in the

month of August, and thereafter.

Most births are concentrated in

the first half of the year

We see a large number of

children born on the 5th, 10th,

15th, 20th and 25th of each month

– that is, round numbered dates

Such round numbered patterns a

typical indication of fraud. Here,

birthdates are brought forward to

aid early school admission

More births Fewer births … on average, for each day of the year (from 2007 to 2013)

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THIS ADVERSELY IMPACTS CHILDREN’S MARKS

It’s a well established fact that older

children tend to do better at school in

most activities. Since many children

have had their birth dates brought

forward, these younger children suffer.

The average marks of children “born” on the 1st, 5th, 10th, 15th etc. of the

month tend to score lower marks.

• Are holidays avoided for births?

• Which months have a higher propensity for births, and why?

• Are there any patterns not found in the US data?

Higher marks Lower marks … on average, for children born on a given day of the year (from 2007 to 2013)

Children “born” on round numbered days score lower marks on average,

due to a higher proportion of younger children

VISUALIZATION DESIGN TECHNIQUES

THE GRAMMAR OF GRAPHICS

74Source: Designing Data Visualizations by Noah Iliinsky and Julie Steele (O’Reilly).

Copyright 2011 Julie Steele and Noah Iliinsky, 978-1-449-31228-2.

Position is the most powerful encoding.The eye and brain are naturally wired to detect mis-alignment of

the smallest order

1

Colour, when used in context, is powerful.We can detect miniscule changes or variations in colour when

comparing an element with neighbouring elements. This is what

makes true colour (32-pixel colour, i.e. 4 billion) a necessity in

computer graphics

2

Size is a useful differentiator.The eye can detect moderate size variations at

moderate distances. Size also has a natural

interpretation: that of priority.

3

Several other encodings are possibleAesthetics such as angle, shadows, shapes, patterns,

density, labelling, enclosures, etc. can each be used to

map data.

4

VISUAL ENCODINGS VARY IN THEIR EFFECTIVENESS

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POSITION IS EVERYTHING

Absolute & relative departure time (continuous)

Absolute & relative arrival time (continuous)

Absolute & relative length of trip (continuous)

Stopovers (binary)

Absolute & relative stopover duration (continuous)

Absolute & relative stopover start & stop time

(continuous)

Sort order (ranked)

Source: http://hipmunk.com

76

THE CONCEPT OF NATURAL ORDERING

Source: European Soil Bureau. Copyright © 1995–2011, European Union.

http://eusoils.jrc.ec.europa.eu/

Colour is not

ordered

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BETTER USE OF COLOUR

Source: http://mapsof.net/uploads/static-maps/topographic_(altitude)_map_tamil_nadu.png

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A DEFINITIVE HIERARCHY OF ENCODINGS EXISTS

WHERE TO LEARN MORE?REFERENCES

80

BOOKS BY EDWARD TUFTE

GRAMENER.COM/DEMO/MORE EXAMPLES TO EXPLORE