The document discusses data visualization and its importance. It explains that data visualization goes beyond simple charts and graphs by transforming raw data into visual representations that provide insight and tell stories. Effective visualization helps people understand complex information more quickly and can lead to better decision making. The document also provides examples of different visualization tools and techniques and discusses challenges and opportunities in emerging areas like mobile devices, virtual reality, and brain interfaces.

1. alizing Data
hvanil Patel
Visualizing Data
by Dhvanil Patel

2. Isn’t it all about pretty bar charts?
Isn’t it just a fancy way of showing numbers?
Oh, Data Viz? Yeah, I can do that in Excel.
Why do we even need Data Visualization?
Yeah, I’ve worked with D3.js too.

3. Isn’t it all about pretty bar charts?
Isn’t it just a fancy way of showing numbers?
Oh, Data Viz? Yeah, I can do that in Excel.
Why do we even need Data Visualization?
Yeah, I’ve worked with D3.js too.No! No!

4. KW Pyramid? DIKW Pyramid?

5. Data Data
Information Information
Knowledge Knowledge
Wisdom Wisdom

6. Data Data
Information Information
Knowledge Knowledge
Wisdom Wisdom
It is raining.

7. Data Data
Information Information
Knowledge Knowledge
Wisdom Wisdom
The temperature dropped
15 degrees and then it
started raining.

8. Data Data
Information Information
Knowledge Knowledge
Wisdom Wisdom
If the humidity is very high
and the temperature drops
substantially the atmospheres
is often unlikely to be able to
hold the moisture so it rains.

9. Data Data
Information Information
Knowledge Knowledge
Wisdom Wisdom
I should carry an umbrella
to office today.

10. Data Visualization
rmation
Design
Information
Design

11. Why is Data Visualization important?
essential?
crucial?
neccesary?
vital?
critical?
useful?
meaningful?
decisive?
relevant?
far-reaching?

12. Sight Sight
1250Mb/s 1250Mb/s125Mb/s 125Mb/s
Touch Touch
12.5Mb/s 12.5Mb/s
mell
aring
Smell
HearingTaste

13. Sight Sight
1250Mb/s 1250Mb/s125Mb/s 125Mb/s
Touch Touch
12.5Mb/s 12.5Mb/s
mell
aring
Smell
HearingTaste
Active awareness
at any given time

14. Human beings are visual creatures.

15. alysis of Variance (ANOVA) Confounding Factor Confoundent Correlation CoefficientDegrees of Freedom (df) Degrees of Freedom (df)
χ² (Chi-Square) χ² (Chi-Square)Sum of Squares Sum of SquaresThe Statistic t The Sternative Hypothesis Statistical Significance
The Statistic Z The Statistic Ze-Tailed Test Critical Value CriticalCritical Regiontatistical Test Statistical TestStandard Error Standard Error
F Test F TestPower Powerβ (beta) β (beta)α (alpha) α (alpha) Z Score Z Scorerameter Variance VarianceDeviation Range RangeMode M
Type II Error Type ITransformation RulesNormal Distribution Normal DistributionStatistic Statisticdian Mean Histogram Histogram
Regression Related Measures Related MeasuresCentral Limit TheoremSample Mean (X Bar) Sample Mean (X Bar)pulation
Standard Deviation Standard DeviationMean MeaMean Square Mean SquareType I Error Type I ErrorTwo-Tailed Test s² s²Prediction
Null HypotUnbiased Estimate Random SampleAverage Deviationandard Deviation

16. alysis of Variance (ANOVA) Confounding Factor Confoundent Correlation CoefficientDegrees of Freedom (df) Degrees of Freedom (df)
χ² (Chi-Square) χ² (Chi-Square)Sum of Squares Sum of SquaresThe Statistic t The Sternative Hypothesis Statistical Significance
The Statistic Z The Statistic Ze-Tailed Test Critical Value CriticalCritical Regiontatistical Test Statistical TestStandard Error Standard Error
F Test F TestPower Powerβ (beta) β (beta)α (alpha) α (alpha) Z Score Z Scorerameter Variance VarianceDeviation Range RangeMode M
Type II Error Type ITransformation RulesNormal Distribution Normal DistributionStatistic Statisticdian Mean Histogram Histogram
Regression Related Measures Related MeasuresCentral Limit TheoremSample Mean (X Bar) Sample Mean (X Bar)pulation
Standard Deviation Standard DeviationMean MeaMean Square Mean SquareType I Error Type I ErrorTwo-Tailed Test s² s²Prediction
Null Hypothesis Null HypotUnbiased EstimateRandom Sample Random SampleAverage Deviation Average Deviationandard Deviation
s. People HATE Statistics.

17. Data Visualization
rmation
Design
Information
Design

18. Anatomy of a Visualization!

19. Country Military Budget ($B) % GDP
United Kingdom 55.5 2.0
Russia 66.4 5.4
South Korea 36.4 2.6
Japan 40.9 1.0
Germany 39.4 1.2
India 51.3 2.3
Saudi Arabia 87.2 13.7
France 50.9 2.1
United States 596.0 3.3
China 215.0 1.9

20. United Kingdom 55.5 2.0
Russia 66.4 5.4
South Korea 36.4 2.6
Japan 40.9 1.0
Germany 39.4 1.2
India 51.3 2.3
Saudi Arabia 87.2 13.7
France 50.9 2.1
United States 596.0 3.3
China 215.0 1.9
Country ($B) % GDP

21. United States 596.0 3.3
China 215.0 1.9
Saudi Arabia 87.2 13.7
Russia 66.4 5.4
United Kingdom 55.5 2.0
India 51.3 2.3
France 50.9 2.1
Japan 40.9 1.0
Germany 39.4 1.2
South Korea 36.4 2.6
Country ($B) % GDP

22. United States 596.0 3.3
China 215.0 1.9
Saudi Arabia 87.2 13.7
Russia 66.4 5.4
United Kingdom 55.5 2.0
India 51.3 2.3
France 50.9 2.1
Japan 40.9 1.0
Germany 39.4 1.2
South Korea 36.4 2.6
Country ($B) % GDP

23. United
States
China
Saudi
Arabia
Russia
United
Kingdom
France
Japan
Germany
South
Korea
India

24. South
Korea
Germany
Japan
France
United
Kingdom
Russia
Saudi
Arabia
China
United
States
India

25. Japan
France
United Kingdom
Russia
Saudi Arabia
China
United States
India
South Korea
Germany

26. Japan
France
United Kingdom
Russia
Saudi Arabia
China
United States
India
South Korea
Germany

27. United States
Japan
France
United Kingdom
Russia
Saudi Arabia
China
India
South Korea
Germany

28. United States NASA’s budget
Japan
France
United Kingdom
Russia
Saudi Arabia
China
India
South Korea
Germany

29. Why don’t we
ring in Maps?
Why don’t we
bring in Maps?

30. United States
Japan
France
United Kingdom
Russia
Saudi
Arabia
China
India
South
Korea
Germany
Military Budget
(in $ Billions)

31. United States Japan
France
Russia
Saudi
Arabia
India
South
Korea
Germany
United Kingdom
China
Military Budget
(% of GDP)

32. –Ronald Coase –Ronald Coase
“If you torture the data long enough, it will confess.”

33. ation Information
Dis Information
Un Information
Mis Information

34. That’s all well and good,
but How do I start?

35. Step 1
Pick a Dataset!

36. Step 2
Visualize!

37. Visualization Tools!

38. three.js

39. three.js

40. three.js

41. Mapping Tools!

43. bit.do/datavisualization

44. ization
uture!
Data Visualization
and it’s Future!

45. obile Devices Mobile Devices
Tiny screens, huge challenges!

46. rtual Reality! Virtual Reality!
Different ballgame, altogether!

47. Brain Computer Interfaces!
Different ballgame, altogether!

48. Feedback ?!
Questions ?!
Stones ?!
Tomatoes ?!
Shoes ?!
Bricks ?!
Feedback ?!
Questions ?!
Stones ?!
Tomatoes ?!
Shoes ?!
Bricks ?!

49. facebook.com/@flamenfury
@dhvanilp twitter.com/@dhvanilp
l.com mailto : me@dhvanil.com
e: dhvanil.com website: dhvanil.com
Thank you! Thank you!