The document discusses various topics related to data science including what a data scientist does, the Maslow pyramid of data science, data science compared to nuclear energy, and whether data or information should be the goal. It also discusses data analysis techniques like time series forecasting methods, the central limit theorem, control charts, and quality metrics. Key points include discussing EWMA, ARIMA, and regression forecasting models and when each is best to use.

2. What does a data scientist do?
Alex Gilgur. Data Science & Predictive SPC

3. The Maslow Pyramid
Alex Gilgur. Data Science & Predictive SPC

4. The Maslow Pyramid of Data Science
IT Infrastructure
Software Engineering
Quantitative Analytics
Domain
Data
Alex Gilgur. Data Science & Predictive SPC

5. Data Science = Nuclear Energy
Blow up in our face
Alex Gilgur. Data Science & Predictive SPC

6. Data Science = Nuclear Energy
Blow up in our face
…or…
Alex Gilgur. Data Science & Predictive SPC

7. Data Science = Nuclear Energy
Blow up in our face
…or…
Give us Power
Alex Gilgur. Data Science & Predictive SPC

8. What’s the Team We’re Rooting For?
DATA
INFORMATION
8Alex Gilgur. Data Science & Predictive SPC

9. What’s the Team We’re Rooting For?
DATA
INFORMATION
9
INFORMATION
INFORMATION
Alex Gilgur. Data Science & Predictive SPC

10. What’s the Team We’re Rooting For?
INFORMATION
10
INFORMATION
INFORMATION
Alex Gilgur. Data Science & Predictive SPC

12. •
•
•
•

14. … … …
… … …
… … …
… … …

15. Servers = argmax (Revenue |Budget)
Revenue = f[Throughput (Servers, SW, Budget)]
Servers = argmin (Budget | Revenue)
•Throughput = t (UX)
•Revenue = r (Throughput)
•Budget = f(SW, Servers)
Constraints:
•Domain
•Budget ≤ B

16. From X to Y to X

17. Closing the Loop

20. ∆ →

22. Arithmetic means of
random samples taken
from any distribution
asymptotically
converges to a normal
distribution as the
number of such samples
tends to infinity.
CENTRAL LIMIT THEOREM

23. …

24. …

25. σ
σ
σ

26. σ
σ
σ

27. σ
σ
σ

29. ○
○
○

30. ●
○ σ
○
○
●
○ σ σ
○
○
●
●

31. http://www.isixsigma.com/

32. ❑
❑
❑
❑
❑… … …
❑
❑
❑
❑
❑
❑
❑
❑
❑… … …

33. ❑
❑
❑

34. •
•
•
•
•
•

35. KeyPerformanceMetric(KPM)
72 hrs
LSL
USL
How did HAL Know?

37. • •
•
■
■
●
●
●

38. A Few Words About Forecasting
Methods:
● EWMA
● ARIMA
● Regression
EWMA models are very specific and computationally fast, but they have to be told
trend (linear or exponential) and seasonality (additive or multiplicative).
ARIMA model will implicitly account for trends, seasonality, and stationarity of the
data. Autocorrelation of ARIMA residuals provide all the periodicities that have been
missed.
For stationary data, use ARIMA
For non-stationary data, use EWMA
EWMA and ARIMA overlap
When to use Regression:
● data are monotonic.
● seasonality is NOT statistically significant.
● EWMA and ARIMA fail.
When to use Quantile Regression:
● Upper and Lower bounds behave differently.
● Outliers are possible.
For each data set, we can run a model competition, computing forecast model
quality based on a weighted sum of model goodness of fit, model suitability for
forecasting, data stationarity and data variability, and selecting the model that works
best for each data set.
EWMA
ARIMA
Quantile Regression

39. …

40. …

41. ●
●
○
○
…

42. … …
●
●
●
●

43. … …
●
●
●
●

44. … …
●
●
●
●

45. …
… …
●
●
●
●

46. … …
…
… …

47. … …
…
… …

48. p50
R …
p50
R …
Target
(LCL…UCL)
(LSL…USL)

49. p50
R …
p50
R …
Target
(LCL…UCL)
(LSL…USL)

50. ❑
❑
❑
❑
o
❑
❑
❑

52. •
•
•
•

53. ●
○
○
○
●
○
●
○
■
■
●
●
●
●
●
●

54. www.isixsigma.com
www.amstat.org
www.cmg.org
www.linkedin.com
http://alexonsimanddata.blogspot.com/
http://josepferrandiz.blogspot.com/
“Statistical thinking will one day be as necessary for
efficient citizenship as the ability to read and write.”
- H.G.Wells (1866-1946)

55. THANK YOU

57. •

59. http://en.wikipedia.
org/wiki/Western_Electric_rules
http://en.wikipedia.
org/wiki/Nelson_rules

61. •
•
•
•
•

62. σ
σ

63. Universal
Scalability Law