Story telling is a key to susccessfull presentation or to sell an idea, telling a story with simple words wont be useful to create a visual picture. If we can tell stories with proper data and visual that will bring a great meaning.

1. Data Visualization
Story telling
XD Knowledge Share
By
Sreenivas Ravi
Session 1

2. We humans always look for visuals to perceive information, we grasp information
very quickly if we look at any visuals rather than plain text.

3. Our brain stores information in form of visuals. Our brain has special part “visual
cortex” to process the visual information. It is located in back of the head.

4. Brain from back of the head
Source: Wikipedia

5. We live in a world full of data. And making sense of it can be a challenge.

6. The Rules of Effective Data Visualization
• Why visualize data?
• What kind of visualization should you make?
Comparing Data Sets
• Visualize comparisons in the data
• Bar charts
• Line Charts
• Spark Lines
• Gant charts
• Tree Maps

8. Why Visualizing Data?

9. Everybody's used to summary statistics, things like what's the average height of a class, what's the
average sales per customer. These numbers can be a useful summary of a data set but they can
also hide detail within the data. There is a danger that relying on summaries can lead to
misleading or even incorrect answers.

10. Anscombe's quartet
I II III IV
x y x y x y x y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89
Source: Wikipedia

11. Anscombe's quartet
Property Value Accuracy
Mean of x 9 exact
Sample variance of x 11 exact
Mean of y 7.50 to 2 decimal places
Sample variance of y 4.125 plus/minus 0.003
Correlation between x and y 0.816 to 3 decimal places
Linear regression line y = 3.00 + 0.500x
to 2 and 3 decimal places,
respectively
Coefficient of determination of the
linear regression
0.67 to 2 decimal places
Source: Wikipedia

12. Source: Wikipedia
Rough linear relationship with little variation to the line of best fit.

13. Source: Wikipedia
The summary statistics were identical, the data points create a very neat curve that doesn't fit
that linear relationship.

14. Source: Wikipedia
Tight linear relationship, and the angle of straight line is pretty much bang on to the
regression line.

15. Source: Wikipedia
x remains constant except for one exception up here.

16. Source: Wikipedia

17. The start of any data visualization depends on the data set and what you want to know.

18. Five main questions you can ask
What type of information do I need to show from my data?1
How does my one thing compare to another?2
How is this data related to that data?3
How is this data distributed?4
How is this data made up, and how does this data look on a
map?
5

19. What kind of visualization should you make?

20. There are many ways to visualize your data, and the choice of it is going to largely depend on
your data and what you're trying to achieve and what visualization you decide to use, there's
some general guidelines that will stand you in good stead.

21. Two kinds of data visualization
1. exploratory dashboards.
2. Info graphics

22. Exploratory dashboards.

23. Exploratory dashboards.

24. Exploratory dashboards.

25. Info Graphics

26. Info Graphics

27. Info Graphics

28. Info Graphics

29. Info Graphics

30. Info Graphics

31. Info Graphics

32. Info Graphics

33. A good visualization will convey the answer clearly and concisely. But that might not be possible
with just a single chart. You might need to have more.

34. Info Graphics

35. There is no single visualization method that shows everything.
Use as many charts as you need. That doesn’t mean to use too many visualization.
Stick to no more than say four.
But have several dashboards linked together in a flowing narrative so you can take the
viewer through the data in a logical fashion.

36. Comparing Data sets

37. Visualize comparisons in data

38. There are many ways to compare data, depending on the kind of data we have and
the type of questions we are asked

39. Three great ways to compare data sets
Bar charts Line charts Highlight tables

40. The amount of profit each one of a product categories has made.

41. • Product
• Profit
Dimensions:
-$10,000 -$5,000 $0 $5,000 $10,000 $15,000 $20,000 $25,000 $30,000 $35,000 $40,000
Electronics
Home, Kitchen
Car, Motor Bikes
Men's Fashion
Women's Fashion
Beauty, Health
Movies
Games
Mobile, Computer
Baby Products
Mobile Recharges

42. • Product
• Profit
Dimensions:
-$10,000 -$5,000 $0 $5,000 $10,000 $15,000 $20,000 $25,000 $30,000 $35,000 $40,000
Electronics
Home, Kitchen
Car, Motor Bikes
Men's Fashion
Women's Fashion
Beauty, Health
Movies
Games
Mobile, Computer
Baby Products
Mobile Recharges

43. • Product
• Profit
Dimensions:
-$10,000 -$5,000 $0 $5,000 $10,000 $15,000 $20,000 $25,000 $30,000 $35,000 $40,000
Electronics
Home, Kitchen
Car, Motor Bikes
Men's Fashion
Women's Fashion
Beauty, Health
Movies
Games
Mobile, Computer
Baby Products
Mobile Recharges

44. • Product
• Profit
Dimensions:
-$10,000 -$5,000 $0 $5,000 $10,000 $15,000 $20,000 $25,000 $30,000 $35,000 $40,000
Electronics
Home, Kitchen
Car, Motor Bikes
Men's Fashion
Women's Fashion
Beauty, Health
Movies
Games
Mobile, Computer
Baby Products
Mobile Recharges

45. -$10,000 $10,000$0 -$10,000 $10,000$0 -$10,000 $10,000$0 -$10,000 $10,000$0
Electronics
Home, Kitchen
Car, Motor Bikes
Men's Fashion
Women's Fashion
Beauty, Health
Movies
Games
Mobile, Computer
Baby Products
Mobile Recharges
Hyderabad Mumbai Bangalore Delhi

46. -$10,000
-$5,000
$0
$5,000
$10,000
$15,000
$20,000
$25,000
$30,000
$35,000
$40,000
January February March April May June July August September October November December
Profit vs. Time
Hyderabad Mumbai Bangalore Delhi

47. -$10,000
-$5,000
$0
$5,000
$10,000
$15,000
$20,000
$25,000
$30,000
$35,000
$40,000
January February March April May June July August September October November December
Profit vs. Time
Hyderabad Mumbai Bangalore Delhi

48. -$10,000
-$5,000
$0
$5,000
$10,000
$15,000
$20,000
$25,000
$30,000
$35,000
$40,000
January February March April May June July August September October November December
Profit vs. Time
Hyderabad Mumbai Bangalore Delhi

49. To see the detail straightaway
Smaller Data Set

50. Hyderabad Mumbai Bangalore Delhi
Mobile Recharges $33,956 $45,033 $27,277 $61,114
Baby Products $23,582 $34,188 $19,525 $30,236
Mobile, Computer $5,762 $7,486 $4,656 $9,212
Games $56,923 $53,498 $37,030 $55,961
Movies $24,157 $43,819 $10,899 $36,004
Beauty, Health $85,231 $96,261 $45,176 $101,781
Women's Fashion $37,260 $53,219 $9,300 $49,749
Men's Fashion $4,637 $4,376 $3,346 $4,118
Car, Motor Bike $778 $820 $503 $923
Home Kitchen $15,254 $29,071 $17,307 $30,321
Electronics $2,451 $2,603 $2,353 $5,006

51. Hyderabad Mumbai Bangalore Delhi
Mobile Recharges $33,956 $45,033 $27,277 $61,114
Baby Products $23,582 $34,188 $19,525 $30,236
Mobile, Computer $5,762 $7,486 $4,656 $9,212
Games $56,923 $53,498 $37,030 $55,961
Movies $24,157 $43,819 $10,899 $36,004
Beauty, Health $85,231 $96,261 $45,176 $101,781
Women's Fashion $37,260 $53,219 $9,300 $49,749
Men's Fashion $4,637 $4,376 $3,346 $4,118
Car, Motor Bike $778 $820 $503 $923
Home Kitchen $15,254 $29,071 $17,307 $30,321
Electronics $2,451 $2,603 $2,353 $5,006

52. Hyderabad Mumbai Bangalore Delhi
Corporate Individual Corporate Individual Corporate Individual Corporate Individual
Mobile
Recharges
Baby Products
Mobile,
Computer
Games
Movies
Beauty, Health
Women's
Fashion
Men's Fashion
Car, Motor Bike
Home Kitchen
Electronics

53. Sales we had in each day of the year
or
when that day was profitable or not.

54. 2018 Monday Tuesday Wednesday Thursday Friday Saturday
Week1
Week2
Week3
Week4
Week5
Week6
Week7
Week8
Week9
Week10
Week11
Week12
Week13
Week14
Week15
Week17
Week18
Week19
Week20
Week21
Week22
Week23
Week24
Highest Profits
Average Profits
Losses

55. What if the data's very noisy?

57. Spark lines for important events

61. Tree maps for long-tail data

62. A tree map's a very compact visualization that allows a very large amount of data to be viewed
in a small space. By using shape, size and color, a tree map can also show all of the small
values' contributions to the overall data set.

65. long tail data

66. Tree map
Very compact visualization that allows a very large amount of data to be viewed in a small
space.

72. Slope charts for change between dates

75. A slope chart works by just looking at the difference between the first and the last value in a
data set.
Slope chart