The document discusses whether digital analysts should become more data science-focused. It notes the role of digital analysts is evolving as data science techniques like statistics, computer programming, and data visualization become more important. The document advocates learning R, an open-source programming language well-suited for data science tasks. It provides examples of how R can be used to extract and analyze digital analytics data. Overall, it argues digital analysts should expand their skills to include basic data science approaches.

1. Should Digital Analysts
Become More Data Science-y?
Tim Wilson
#SPWK / @tgwilson
@tgwilson / #SPWK
Source: Flickr / KamiPhuc

2. October 10, 1993
@tgwilson / #SPWK

3. Mt. Katahdin
@tgwilson / #SPWK
Source: Google Maps

4. The end of a
5-month journey
@tgwilson / #SPWK
Source: Google Maps

5. …which was a
journey on foot.
@tgwilson / #SPWK
Source: Google Maps

6. @tgwilson / #SPWK

7. Which is like walking
from Barcelona to Moscow.
@tgwilson / #SPWK

8. Last year, I was on a
different journey.
@tgwilson / #SPWK
Source: Google Maps
Digital Analyst
Fall 2015
Intermediate R User
End of 2016

9. These journeys were
similar in many ways.
@tgwilson / #SPWK
Flickr / Ines Hegedus-Garcia

10. They were both challenging!
@tgwilson / #SPWK

11. They were both rewarding!
@tgwilson / #SPWK

12. They both turned out to have destinations
that weren’t as clear as I thought.
@tgwilson / #SPWK

13. Last year’s journey
@tgwilson / #SPWK
Source: Google Maps
Digital Analyst
Fall 2015
Intermediate R User
End of 2016

14. Last year’s journey
@tgwilson / #SPWK
Source: Google Maps
Digital Analyst
Fall 2015
Data Science-y Analyst
End of 2016

15. @tgwilson / #SPWK
But…
WHY?!

16. Because the Role of the
Digital Analyst Is Evolving
@tgwilson / #SPWK

17. The Spectrum of “Analytics”
Digital Analyst
Marketer
Basic Metrics Segmentation Data Science
@tgwilson / #SPWK

18. Data Science?!
Data Science
@tgwilson / #SPWK

19. Data Science
Wikipedia sayeth
employs techniques and theories drawn from many fields within the broad areas of:
@tgwilson / #SPWK

20. @tgwilson / #SPWK

21. @tgwilson / #SPWK

22. @tgwilson / #SPWK
is like boiling water.

23. PEAK1
Flickr / Revolution_Ferg
MSR Whisperlite
vs.
@tgwilson / #SPWK

24. @tgwilson / #SPWK
vs.

25. vs.
Programming Languages
Open Source (Free*)
Supported By Large Communities
Connectable to Digital Analytics
(and Other!) Data Sources
* TANSTAAFL
@tgwilson / #SPWK

26. Clingmans Dome
The highest point on the Appalachian Trail…
Flickr / David Fulmer
…and where I was during my undergraduate
commencement.
@tgwilson / #SPWK

27. Just as education arms us with
the power of knowledge…
...“programming” with data is powerful.
Flickr / David Fulmer
@tgwilson / #SPWK

28. Data Extraction
Flickr / Scott Swigart
@tgwilson / #SPWK

29. @tgwilson / #SPWK
One GA Account
Many Properties!
Many Views!

30. @tgwilson / #SPWK
22
lines of code

31. @tgwilson / #SPWK
22
lines of terribly written code

32. @tgwilson / #SPWK
17
lines of terribly okay written code
38Google Analytics views queried
IN
19
seconds

33. @tgwilson / #SPWK

34. @tgwilson / #SPWK
Pull all of the
views I can access
Filter to the one
account I care
about
Filter to only the
“production”
views
Pull the data for
each of those
views
Add that data to
my (filtered) list of
views
Export a .csv
Pretty up the
column names

35. @tgwilson / #SPWK
Yes!
Is this just an
alternative to Adobe’s
Data Warehouse?
…with more flexibility
…without the mystery
delivery time

36. Reusability
and
Extensibility
Flickr / Scott Swigart
@tgwilson / #SPWK

37. @tgwilson / #SPWK
Pull all of the
views I can access
Filter to the one
account I care
about
Filter to only the
“production”
views
Pull the data for
each of those
views
Add that data to
my (filtered) list of
views
Export a .csv
Pretty up the
column names

38. @tgwilson / #SPWK

39. Comparisons: (Top) 18 Web Sites
@tgwilson / #SPWK

40. Totals: Channel vs. Customer Segment
@tgwilson / #SPWK

41. Anomalies: Channels vs. Segments
@tgwilson / #SPWK

42. github.com/gilliganondata
@tgwilson / #SPWK

43. Data Science
@tgwilson / #SPWK

44. @tgwilson / #SPWK
“The clearest way into the universe is through a forest wilderness.”
- John Muir

45. “Numbers have an important story to tell. They rely on you to give them
a clear and convincing voice.”
- Stephen Few
0
20
40
60
80
100
120
140
160
180
200
1/3 1/10 1/17 1/24 1/31 2/7 2/14 2/21 2/28 3/6 3/13 3/20 3/27 4/3 4/10 4/17 4/24 5/1 5/8 5/15 5/22 5/29 6/5 6/12
@tgwilson / #SPWK

46. Where does R really stand out?
Flickr / Marina del Castell @tgwilson / #SPWK
VS.

47. Variety!
r-graph-gallery.com
@tgwilson / #SPWK

48. X X
Variety!
r-graph-gallery.com
@tgwilson / #SPWK

49. Variety!
r-graph-gallery.com
@tgwilson / #SPWK

50. How else does R stand out?
Flickr / Marina del Castell @tgwilson / #SPWK

51. My Perspective: Fall 2015
@tgwilson / #SPWK
Do the Analysis Communicate Results
@analyticshero
DATA VISUALIZATION

52. My Data Science-y Perspective
@tgwilson / #SPWK
Do the Analysis Communicate Results
@analyticshero
DATA VISUALIZATION

53. To be clear…
@tgwilson / #SPWK
…many tools offer
powerful data visualization.

54. Data scientists build visualizations that
are interactive and reactive.
@tgwilson / #SPWK
bit.ly/ga-explore

55. bit.ly/ga-explore
@tgwilson / #SPWK

56. bit.ly/ga-explore
@tgwilson / #SPWK

57. bit.ly/ga-explore
@tgwilson / #SPWK

58. bit.ly/ga-explore
@tgwilson / #SPWK

59. Data Science
@tgwilson / #SPWK

60. What is a p-value?
@tgwilson / #SPWK

61. “Informally, a p-value is the probability under a
specified statistical model that a statistical
summary of the data (for example, the sample
mean difference between two compared groups)
would be equal to or more extreme than its
observed value.”
– American Statistical Association
@tgwilson / #SPWK

62. @tgwilson / #SPWK
p-value:
Statistically significant pregnancy test.

63. This topic is…
Flickr / Howard Lake @tgwilson / #SPWK

64. As web analysts, how
often do we consider:
Correlations?
Regressions?
Confidence Levels?
Confidence Intervals?
Outlier Detection/Removal?
Type 1 vs. Type 2 Errors?
Bayesian vs. Frequentist
Approaches?
Flickr / Neil Piddock @tgwilson / #SPWK

65. @tgwilson / #SPWK
This is hard!
Flickr / Casey Fleser

66. Which Channels Have a Traffic Correlation?
@tgwilson / #SPWK

67. TODAY
The metric went down. Why?!

68. TODAY
Did the metric move enough for me to care?

69. Forecasts = The Future?
Actual Forecast
TODAY
(NOT FOR THIS, AT LEAST)

70. Let’s pretend “today” is in the past!
Actual Forecast
TODAY
PRETEND
“TODAY”

71. Let’s pretend ”today” is in the past!
Actual Forecast
PRETEND
“TODAY”

72. Actual Forecast
And let’s build a forecast from that point
PRETEND
“TODAY”

73. We know a forecast won’t be perfect.
Prediction Interval Actual Forecast
PRETEND
“TODAY”

74. Now we have meaningful context!
Prediction Interval Actual Forecast TODAY

75. What this can look like in the real world.
@tgwilson / #SPWK

76. And…drilling down and summarizing
@tgwilson / #SPWK

77. Mark Edmondson’s
Pre-/Post- Analysis Using a Bayesian Structural Time-Series Method
@tgwilson / #SPWKbit.ly/ga-effect

78. So, are YOU ready to put data science(-y-ness)
on your career roadmap?
STATISTICS
@tgwilson / #SPWK
Source: Flickr / Xavi
DATA
VISUALIZATION
COMPUTER
PROGRAMMING

79. The Measure Slack Team
@tgwilson / #SPWK
http://join.measure.chat

80. dartistics.com
@tgwilson / #SPWK

81. R and Statistics for the Digital Analyst
Columbus, Ohio, US – June 13-15, 2017
bit.ly/r-stats-training
Introduction to RDAY 1
The Basics
Taught by Tim Wilson and Mark Edmondson
Statistics for the
Digital Analyst
DAY 2
Putting R into
Practice
DAY 3
Real-World Digital Analytics Examples
Taught by Dr. Michael Levin, Otterbein University
Building Those Real-World Examples (and Advanced Topics)
Taught by Mark Edmondson and Tim Wilson

82. Flickr / Jeffrey Stylos
Slack/Twitter: @tgwilson
linkedin.com/in/tgwilson
tim@analyticsdemystified.com
Podcast: analyticshour.io
@tgwilson / #SPWK
2-D Tool: bit.ly/ga-explore
Website: dartistics.com
Slack: join.measure.chat
Training: bit.ly/r-stats-training