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VISUALISING DATA
S.A N AN D@ G RAME N E R .CO M , C HIE F DATA SCIE N TIST
A DATA VISUALISATION
CHALLENGE
You will see 3 questions.
You have 30 seconds.
Try it!
Your timer
starts now
HOW MANY NUMBERS ARE ABOVE 100? 1
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 BELOW 10? 2
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
WHICH QUADRANT HAS 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
The same questions again.
But with a few visual cues.
See how long it takes now.
Your timer
starts now
A DATA VISUALISATION
CHALLENGE
HOW MANY NUMBERS ARE ABOVE 100? 1
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 BELOW 10? 2
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
WHICH QUADRANT HAS 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
3
You will be shown a set of numbers
along with a summary (average, etc)
Can you make sense of the figures?
WHY VISUALISE?
So is the variance in sales.Variance in price is the same.
Average sales is the same too.Average price is the same.
Take a look at the sales report
alongside. A company has
branches in 4 cities, and each
branch changes the product
price every month. This leads to
a corresponding change in the
sales.
Here is the performance of the
4 branches with their monthly
price and sales for each month.
Looking at the average, the four
branches have an identical
performance.
2010 Boston Chicago Detroit New York
Month Price Sales Price Sales Price Sales Price Sales
Jan 10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
Feb 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
Mar 13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
Apr 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
May 11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
Jun 14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
Jul 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
Aug 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
Sep 12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
Oct 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
Nov 5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89
Average 9.0 7.50 9.0 7.50 9.0 7.50 9.0 7.50
Variance 10.0 3.75 10.0 3.75 10.0 3.75 10.0 3.75
DO THESE FOUR CITIES LOOK IDENTICAL TO YOU?
DO YOU AGREE?
ARE THEY REALLY IDENTICAL? CHECK AGAIN…
But in fact, the four cities are
totally different in behaviour.
Boston’s sales has generally
increased with price.
Detroit has a nearly perfect
increase in sales with price,
except for one aberration.
Chicago shows a decline in sales
beyond a price of 10.
New York’s sales fluctuates
despite a nearly constant price.
Boston Chicago
New YorkDetroit
100YEARSOFINDIA’SWEATHER
1901
1911
1921
1931
1941
1951
1961
1971
1981
1991
2001
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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
Party BJP BSP CPM INC RJD SPWINNING PARTIES
It is not often easy to see
which party won the overall
elections on a map.
In the previous page, BJP (in
red), which won in
constituencies with a large
physical area (Rajasthan,
Madhya Pradesh), appeared
to have swept the elections.
This cartogram resizes the
constituencies proportional
to the number of voters, and
it’s easier to see that the
Congress (in blue) won
about as many seats as the
BJP.
The Candidate
Their Caste
Their Party
What do people consider important when voting?
Karnataka, Assembly Elections 2008
Top 1/3rd
Next 1/3rd
Lowest 1/3rd
Sarvagnanagar: 35.7% out of 107941
(K J George, INC)
Low polling
Low polling
Hosakote: 89.3% out of 141953
What was the polling percentage?
Karnataka, Assembly Elections 2008
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 2 4 6 8 10 12 14 16 18
# contestants
Winnermargin
More contestants did not reduce the winner margin
Karnataka, Assembly Elections 2008
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 2 4 6 8 10 12 14 16 18
# contestants
Runer-upmargin
More contestants did reduce the runner-up margin
Karnataka, Assembly Elections 2004
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?
INDIAN ODI BATTING
http://gramener.com/cricket
http://gramener.com/cricket
Here are all public Indian companies, grouped by Industry. The size of the
box indicates revenue (2012) and the colour indicates net profit
(red is low, green is high). Click on the group to see companies below.
Here are all public Indian companies, grouped by Industry. The size of the
box indicates revenue (2012) and the colour indicates net profit
(red is low, green is high). Click on the group to see companies below.
68% correlation
between AUD & EUR
Plot of 6 month daily
AUD - EUR values
Block of correlated
currencies
… clustered
hierarchically
PRE-2009 2009 AND AFTER
Decisions to increase the number of
lanes on highways grew significantly
post-2009, especially as part of the CCI
(Cabinet Committee on Infrastructure)
decisions
A significant rise in the number of
decisions related to the States is
seen post 2009 – in contrast with
the focus on “Central” pre-2009
The number of international
agreements has declined
dramatically between pre-2009 and
post-2009
Decisions related to
intervention, assistance and relief
were almost entirely concentrated in
pre-2009
Adult
Educat
ion
Adminisr
ative
Reforms
Agric
ultura
l
Mark
eting
Agricul
tureAnimal
Husban
dry
Coope
rative
Excis
e
Fina
nce
Fishe
ries
Fishe
ries
&
Inlan
d
wate
r
trans
port
Food &
Civil
Supplies
Fore
st
Fuel
Haz &
Wakf
Health
and
family
welfare
Higher
Educati
on
Hom
e Horticu
lture
Hous
ing
Info
rma
tion
&
Tec
hno
logy
Kannad
a &
Culture
Labo
ur
Law
&
Hu
man
Righ
ts
Major &
Medium
Industri
es
Medical
Educatio
n
Medium
and
Large
Industrie
s
Mines
&
Geolo
gy
Minor
Irrigati
on
Muz
rai
P.W.D.
Parlia
mentar
y
Affairs
and
Human
Rights
Plan
ning
Planni
ng
and
Statist
ics
Primary
and
Secondary
Education
Primary
Educati
on
Pris
on
Pub
lic
Libr
ary
Reve
nue
Rural
Developme
nt and
Panchayat
Raj
Rural
Wate
r
Suppl
y
Rural
Water
Supply
and
Sanitat
ion
Seri
cult
ure
Smal
l
Scale
Indu
strie
s
Small
Indust
ries
Social
Welfar
e
Suga
r
Textil
e
Touri
sm
Tran
sport
Transp
ortatio
n
Urban
Develo
pment
Water
Resourc
es
Woman &
Child
Developm
ent
Youth
and
Sports
Yout
h
Servi
ce &
Spor
ts
BJP focus
JD(S)
focus
INC focus
What topics did parties focus on during questions?
Karnataka, 2008-2012
P.W.D.
Health and
family
welfare
Reven
ue
Rural
Developme
nt and
Panchayat
Raj
Social
Welfar
e
Urban
Develo
pment
Water
Resour
ces
Minor
Irrigati
on
Fuel
Hous
ing
Agric
ulture
Primary
Educati
on
Primary and
Secondary
Education
Woman &
Child
Developme
nt
Higher
Educati
on
Hom
eCoope
rative
Fore
st
Adminisra
tive
Reforms
Labo
ur
Food &
Civil
Supplies
Tour
ism
Fina
nce
Animal
Husba
ndry
Transpo
rtation
Hortic
ulture
Muzr
ai
Haz &
Wakf
Trans
portMedical
Educatio
n
Medium
and Large
Industries
Excis
e
Major &
Medium
Industrie
s
Kannad
a &
Culture
Text
ile
Fishe
ries
Parliam
entary
Affairs
and
Human
Rights
Adult
Educati
on
Rural
Water
Supply
and
Sanitati
on
Mines
&
Geolog
y
Small
Industr
ies
Youth
and
Sports
Suga
r
Planni
ng and
Statisti
cs
Agricul
tural
Marke
ting
Rural
Water
Supply
Fisher
ies &
Inland
water
trans
port
Small
Scale
Indus
tries
Yout
h
Servi
ce &
Sport
s
Seric
ultur
e
Law
&
Hum
an
Righ
ts
Priso
n
Plan
ning
Info
rma
tion
&
Tec
hnol
ogy
Publ
ic
Libr
ary
What topics did the young & old focus on during questions?
Karnataka, 2008-2012
Young Old
VISUALISING THE MAHABHARATHA
The only other such times were
Feb 23, 2008 (28 decisions) &
Dec 26, 2008 (23 decisions).
Nearly two-thirds of decisions
are taken on Thursday
sessions, which is also visible
on the calendar alongside.
UPA's best cabinet performance was last
Friday, with a record 23 decisions taken in a
single day, including some long pending key
reform measures.
PARLIAMENT DECISIONS (CABINET + CCEA* + CCI**)
* CCEA: Cabinet Committee on Economic Affairs
** CCI: Cabinet Committee on Infrastructure
Mon 63 5%
Tue 56 4%
Wed 105 8%
Thu 854 65%
Fri 223 17%
Sat 6 0%
EDUCATION
PREDICTING MARKS
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?
District
Gender G B
Month Sep Nov Oct Dec Aug Feb Mar Jan Apr May Jul Jun
Caste OTHERS CAT-1 ST SC
Govt False True
Medium E K U MHLT
WHAT INFLUENCES STUDENTS’ MARKS?
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
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
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
LANGUAGE
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
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
MATHEMATICS
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
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
cutoff 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
BOOKS BY EDWARD TUFTE
We handle terabyte-size data via non-traditional analytics and visualise it in real-time.
Gramener visualises
your data
Gramener transforms your data into concise dashboards
that make your business problem & solution visually obvious.
We help you find insights quickly, based on cognitive research,
and our visualisations guide you towards actionable decisions.
A data analytics and visualisation company
WHAT WE OFFER
PLATFORM CUSTOM APPS SERVICES
Buy & create your
own visualisations
using our library of
visual and analytical
components
WHO’D USE THIS?
If you have a strong
analytics & technology
team, and want to
customise visualisations
based on your needs
We build your
domain-specific BI
solutions to integrate
visualisations with
your platform
WHO’D USE THIS?
If your business needs are
clear, you require regular
visual intelligence, but
prefer to outsource
development
We take your
data, analyse it, and
share insights that
you can re-create
with revised data
yourself
WHO’D USE THIS?
If your needs are unclear, or
ad-hoc, and you need a
partner to help extract
actionable insights quickly
out of existing data.
We handle terabyte-size data via non-traditional analytics and visualise it in real-time.
Gramener visualises
your data
Gramener transforms your data into concise dashboards
that make your business problem & solution visually obvious.
We help you find insights quickly, based on cognitive research,
and our visualisations guide you towards actionable decisions.
A data analytics and visualisation company
s.anand@gramener.com
+91 9741 552 552

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Editors Lab Delhi

  • 1. VISUALISING DATA S.A N AN D@ G RAME N E R .CO M , C HIE F DATA SCIE N TIST
  • 2.
  • 3.
  • 4.
  • 5. A DATA VISUALISATION CHALLENGE You will see 3 questions. You have 30 seconds. Try it! Your timer starts now
  • 6. HOW MANY NUMBERS ARE ABOVE 100? 1 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
  • 7. HOW MANY NUMBERS ARE BELOW 10? 2 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
  • 8. WHICH QUADRANT HAS 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
  • 9. The same questions again. But with a few visual cues. See how long it takes now. Your timer starts now A DATA VISUALISATION CHALLENGE
  • 10. HOW MANY NUMBERS ARE ABOVE 100? 1 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
  • 11. HOW MANY NUMBERS ARE BELOW 10? 2 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
  • 12. WHICH QUADRANT HAS 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 3
  • 13. You will be shown a set of numbers along with a summary (average, etc) Can you make sense of the figures? WHY VISUALISE?
  • 14. So is the variance in sales.Variance in price is the same. Average sales is the same too.Average price is the same. Take a look at the sales report alongside. A company has branches in 4 cities, and each branch changes the product price every month. This leads to a corresponding change in the sales. Here is the performance of the 4 branches with their monthly price and sales for each month. Looking at the average, the four branches have an identical performance. 2010 Boston Chicago Detroit New York Month Price Sales Price Sales Price Sales Price Sales Jan 10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58 Feb 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76 Mar 13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 Apr 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84 May 11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47 Jun 14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04 Jul 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25 Aug 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50 Sep 12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56 Oct 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91 Nov 5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89 Average 9.0 7.50 9.0 7.50 9.0 7.50 9.0 7.50 Variance 10.0 3.75 10.0 3.75 10.0 3.75 10.0 3.75 DO THESE FOUR CITIES LOOK IDENTICAL TO YOU? DO YOU AGREE?
  • 15. ARE THEY REALLY IDENTICAL? CHECK AGAIN… But in fact, the four cities are totally different in behaviour. Boston’s sales has generally increased with price. Detroit has a nearly perfect increase in sales with price, except for one aberration. Chicago shows a decline in sales beyond a price of 10. New York’s sales fluctuates despite a nearly constant price. Boston Chicago New YorkDetroit
  • 17. 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
  • 18. Party BJP BSP CPM INC RJD SPWINNING PARTIES It is not often easy to see which party won the overall elections on a map. In the previous page, BJP (in red), which won in constituencies with a large physical area (Rajasthan, Madhya Pradesh), appeared to have swept the elections. This cartogram resizes the constituencies proportional to the number of voters, and it’s easier to see that the Congress (in blue) won about as many seats as the BJP.
  • 19. The Candidate Their Caste Their Party What do people consider important when voting? Karnataka, Assembly Elections 2008
  • 20. Top 1/3rd Next 1/3rd Lowest 1/3rd Sarvagnanagar: 35.7% out of 107941 (K J George, INC) Low polling Low polling Hosakote: 89.3% out of 141953 What was the polling percentage? Karnataka, Assembly Elections 2008
  • 21. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 2 4 6 8 10 12 14 16 18 # contestants Winnermargin More contestants did not reduce the winner margin Karnataka, Assembly Elections 2008
  • 22. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 2 4 6 8 10 12 14 16 18 # contestants Runer-upmargin More contestants did reduce the runner-up margin Karnataka, Assembly Elections 2004
  • 23. 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?
  • 27. Here are all public Indian companies, grouped by Industry. The size of the box indicates revenue (2012) and the colour indicates net profit (red is low, green is high). Click on the group to see companies below.
  • 28. Here are all public Indian companies, grouped by Industry. The size of the box indicates revenue (2012) and the colour indicates net profit (red is low, green is high). Click on the group to see companies below.
  • 29. 68% correlation between AUD & EUR Plot of 6 month daily AUD - EUR values Block of correlated currencies … clustered hierarchically
  • 30. PRE-2009 2009 AND AFTER Decisions to increase the number of lanes on highways grew significantly post-2009, especially as part of the CCI (Cabinet Committee on Infrastructure) decisions A significant rise in the number of decisions related to the States is seen post 2009 – in contrast with the focus on “Central” pre-2009 The number of international agreements has declined dramatically between pre-2009 and post-2009 Decisions related to intervention, assistance and relief were almost entirely concentrated in pre-2009
  • 31. Adult Educat ion Adminisr ative Reforms Agric ultura l Mark eting Agricul tureAnimal Husban dry Coope rative Excis e Fina nce Fishe ries Fishe ries & Inlan d wate r trans port Food & Civil Supplies Fore st Fuel Haz & Wakf Health and family welfare Higher Educati on Hom e Horticu lture Hous ing Info rma tion & Tec hno logy Kannad a & Culture Labo ur Law & Hu man Righ ts Major & Medium Industri es Medical Educatio n Medium and Large Industrie s Mines & Geolo gy Minor Irrigati on Muz rai P.W.D. Parlia mentar y Affairs and Human Rights Plan ning Planni ng and Statist ics Primary and Secondary Education Primary Educati on Pris on Pub lic Libr ary Reve nue Rural Developme nt and Panchayat Raj Rural Wate r Suppl y Rural Water Supply and Sanitat ion Seri cult ure Smal l Scale Indu strie s Small Indust ries Social Welfar e Suga r Textil e Touri sm Tran sport Transp ortatio n Urban Develo pment Water Resourc es Woman & Child Developm ent Youth and Sports Yout h Servi ce & Spor ts BJP focus JD(S) focus INC focus What topics did parties focus on during questions? Karnataka, 2008-2012
  • 32. P.W.D. Health and family welfare Reven ue Rural Developme nt and Panchayat Raj Social Welfar e Urban Develo pment Water Resour ces Minor Irrigati on Fuel Hous ing Agric ulture Primary Educati on Primary and Secondary Education Woman & Child Developme nt Higher Educati on Hom eCoope rative Fore st Adminisra tive Reforms Labo ur Food & Civil Supplies Tour ism Fina nce Animal Husba ndry Transpo rtation Hortic ulture Muzr ai Haz & Wakf Trans portMedical Educatio n Medium and Large Industries Excis e Major & Medium Industrie s Kannad a & Culture Text ile Fishe ries Parliam entary Affairs and Human Rights Adult Educati on Rural Water Supply and Sanitati on Mines & Geolog y Small Industr ies Youth and Sports Suga r Planni ng and Statisti cs Agricul tural Marke ting Rural Water Supply Fisher ies & Inland water trans port Small Scale Indus tries Yout h Servi ce & Sport s Seric ultur e Law & Hum an Righ ts Priso n Plan ning Info rma tion & Tec hnol ogy Publ ic Libr ary What topics did the young & old focus on during questions? Karnataka, 2008-2012 Young Old
  • 34. The only other such times were Feb 23, 2008 (28 decisions) & Dec 26, 2008 (23 decisions). Nearly two-thirds of decisions are taken on Thursday sessions, which is also visible on the calendar alongside. UPA's best cabinet performance was last Friday, with a record 23 decisions taken in a single day, including some long pending key reform measures. PARLIAMENT DECISIONS (CABINET + CCEA* + CCI**) * CCEA: Cabinet Committee on Economic Affairs ** CCI: Cabinet Committee on Infrastructure Mon 63 5% Tue 56 4% Wed 105 8% Thu 854 65% Fri 223 17% Sat 6 0%
  • 35.
  • 36. EDUCATION PREDICTING MARKS 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?
  • 37. District Gender G B Month Sep Nov Oct Dec Aug Feb Mar Jan Apr May Jul Jun Caste OTHERS CAT-1 ST SC Govt False True Medium E K U MHLT WHAT INFLUENCES STUDENTS’ MARKS?
  • 38. 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
  • 39. 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 SOCIAL SCIENCE
  • 40. 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 LANGUAGE
  • 41. 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 SCIENCE
  • 42. 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 MATHEMATICS
  • 43. 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 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 cutoff 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
  • 44.
  • 46. We handle terabyte-size data via non-traditional analytics and visualise it in real-time. Gramener visualises your data Gramener transforms your data into concise dashboards that make your business problem & solution visually obvious. We help you find insights quickly, based on cognitive research, and our visualisations guide you towards actionable decisions. A data analytics and visualisation company
  • 47. WHAT WE OFFER PLATFORM CUSTOM APPS SERVICES Buy & create your own visualisations using our library of visual and analytical components WHO’D USE THIS? If you have a strong analytics & technology team, and want to customise visualisations based on your needs We build your domain-specific BI solutions to integrate visualisations with your platform WHO’D USE THIS? If your business needs are clear, you require regular visual intelligence, but prefer to outsource development We take your data, analyse it, and share insights that you can re-create with revised data yourself WHO’D USE THIS? If your needs are unclear, or ad-hoc, and you need a partner to help extract actionable insights quickly out of existing data.
  • 48. We handle terabyte-size data via non-traditional analytics and visualise it in real-time. Gramener visualises your data Gramener transforms your data into concise dashboards that make your business problem & solution visually obvious. We help you find insights quickly, based on cognitive research, and our visualisations guide you towards actionable decisions. A data analytics and visualisation company

Notas del editor

  1. The earliest data visualisations were seen as far back as the mid-19th century. This is a visualisation prepared by Florence Nightingale for Queen Victoria during England’s war with France. It shows in RED the number of people that died from war wounds, in BLACK the number of people that died from other war related causes and in BLUE the number of people who died due to avoidable hospital diseases. A war is won by people and the main reason England was losing people wasn't bullets or swords but diseases. Florence Nightingale used this visualisation to request funding for hospitals, got it, and England won the war.
  2. In 1854, London suffered from a Cholera epidemic. The popular theory at that time was that cholera was caused by pollution. Dr. John Snow was sceptical about this. By talking to local residents, he identified the source of the outbreak as the public water pump on Broad Street. Dr.Snow used this map to illustrate the cluster of cholera cases around the pump. He also used statistics to illustrate the connection between the quality of the water source and cholera cases. This visual was convincing enough to persuade the local council to disable the well pump by removing its handle,is regarded as the founding event of the science of epidemiology.
  3. This is a map of London drawn purely using data. Every blue dot is a twitter message posted from that location. Every red dot is a photograph on Flickr taken at that location. You can see the structure of the city emerge – the roads, the river Thames, popular areas like the Tower of London, Buckingham palace, and Westminster Abbey highlighted in red, and the popular business districts highlighted in blue. This is despite not using ANY underlying map. There is nothing more here on this image, than hundreds of thousands of data points from Twitter and Flickr.
  4. Who’s the best Indian one-daybatsman? The size represents every run ever scored. The colour represents speed. Red is slow, green is fast.Sehwag’s very fast – but so was Kapil, especially for his time.
  5. This is a drilldown, showing every single match they played.With this, you’ll be able to see who the consistent players are, and where exactly their runs came from.You can also click to see that particular match statistics.
  6. Gramener is a data analtyics and visualisation company.We have the ability to process data at a small and a large scale.We analyse the data to find non-intuitive insights that lie hidden behind it and present it as a visual story that makes those insights obvious in real time.
  7. Gramener is a data analtyics and visualisation company.We have the ability to process data at a small and a large scale.We analyse the data to find non-intuitive insights that lie hidden behind it and present it as a visual story that makes those insights obvious in real time.