The document discusses LinkedIn Learning's efforts to expand its data science and business analytics course offerings. It outlines LinkedIn's strategy to develop a priority list of 100 new courses covering key concepts, tools, and industries. LinkedIn analyzes data on skills supply and demand from its 500 million users to determine high-growth areas and inform new course development. Some top skills identified include Excel, SQL, Java, Python, and business analysis. The analysis helps LinkedIn prioritize courses to address skills gaps and emerging trends in data science.

2. ​ Steve Weiss
​ Content Manager, Data Science & Business Analytics
​ LinkedIn Learning
The Sprint for Teaching Data Science:
LinkedIn Learning, Analytics, and the New Era of
Just-In-Time Skills Training

3. 3

4. 4
LinkedIn: Quick Data Points

5. 141 million+
workers in the U.S. have LinkedIn profiles
5

6. 20,000+
companies in the U.S. use LinkedIn to recruit
6

7. 3 million+
Jobs posted on LinkedIn US monthly
7

8. 11 million+
Open jobs posted on LinkedIn Jobs Avg
8

9. 50,000+
skills on LinkedIn that members can add to
their profiles
9

10. That gives us unique
and valuable insight
into U.S. workforce
trends…
10
LinkedIn: Quick Data Points

11. … and to the
learning resources
we can create to
help workers in the
U.S. and around the
world.
11
LinkedIn: Quick Data Points

12. LINKEDIN GLOBAL DATA:
• 70% of LinkedIn members
are OUTSIDE the US
• LinkedIn is available in 24
languages
• And in 200 countries and
territories
12
LinkedIn: Quick Data Points

13. How We Got Here:
The Rise of Lynda.com and Online Learning
13

14. • Started out in technical
publishing.
• Formed in the late 1990s, by
Lynda Weinman and her
husband/partner Bruce
Heavin, building on the
success of her books on
web design.
14
Lynda.com Grows

15. • By 2014 LDC had established itself as one of
the top companies in the rapidly growing online
training market.
• They’d done this by staying focused on their
content model: Non-MOOC, emphasis on
extremely high-quality production values, plus
expanding from just covering “creative-tech”
topics, into Business/Tech and IT/Tech topics.
15
Lynda.com Succeeds

16. All this time LinkedIn was growing, too, with
the mission of connecting the world’s
professionals to make them more productive
and successful.
It probably surprised no one when LinkedIn
announced the acquisition of Lynda.com:
LinkedIn wanted to be more than an online
resume/networking service. Skills training!
LDC presented a turn-key, best-of-breed
solution.
16
Enter LinkedIn

17. • LinkedIn is fundamentally a data
company. And we can use supply-demand
data to determine what our members need in
order to improve their career prospects.
• The idea of being able to measure our 500
million members’ skills—growth, velocity—
and also take into account what skills
recruiters using LinkedIn need is a huge
plus.
17
LinkedIn Integrates Lynda.com’s Operations

18. Lynda.com and LinkedIn
Learning are the same
content. No need to make
Lynda.com disappear for the
legions of LDC subscribers,
but also a natural fit to begin
presenting learning-content
options to LinkedIn members
who aren’t aware of LDC as
“LinkedIn Learning”.
18
And thus… LinkedIn Learning

19. The Opportunity
Skills Training for Working Professionals
19

20. • >10,000 courses across tech, business,
and creative topics categories
• LinkedIn Learning and Lynda.com
provide coursework to over 10,000
organizations and over 4 million
professionals.
• LinkedIn Learning is available in
English, Spanish, German, French and
Japanese.
20
LinkedIn Learning

21. • More than 25 new courses added
each week.
• Data-driven curation: Personalized
course recommendations based on
job role, skillset, and experience
level.
• Learning paths: Use ours or build
your own.
21
LinkedIn Learning

22. 2015… Time for a:
• Dedicated Data Science course
library
• Dedicated Business Analytics
course library
22
Dedicated Library: Data Science & Business Analytics

23. • Within Tech topic areas,
Data Science is our
fastest-growing library in
terms of demand.
23
Growth of Data Science as a Category

24. • Business Analytics is the
bridge category between
“Tech/Data Science” and
“Business” topics.
24
Growth of Business Analytics as a Category

25. Mostly courses on:
• Excel (LOTS of Excel)
• Information design/visualization
(dashboards, beginning
Tableau, etc)
• “Big data” generalist
overviews
25
Existing Courses: Where We Started (2015)

26. A handful of courses
on Python, Hadoop
and SQL that didn’t
especially focus on the
DS aspects of those
tools.
26
Existing Courses: Where We Started (2015)

27. A few terrific courses
from Barton Poulson
covering R, SPSS,
conceptual intros.
(Barton rocks, BTW)
27
Existing Courses: Where We Started (2015)

28. The Strategy
Go-To Market for Data Science Training
28

29. 29
The List of 100 Priority Courses

30. Ranged from
baseline concepts
(statistics courses
needed!)…
30
The List of 100 Priority Courses

31. …to key tools (more
Hadoop, more Python,
more SQL and NoSQL,
etc)
31
The List of 100 Priority Courses

32. …to acknowledging
emerging leaders
Apache Spark; R
eclipsing SAS; etc.
32
The List of 100 Priority Courses

33. …to covering
applied use of
data science:
finance vs
marketing vs
sports science vs
healthcare, etc.
33
The List of 100 Priority Courses

34. -to coverage of
soft skills, such as
building and
managing data
science teams.
34
The List of 100 Priority Courses

35. Based from LI data and from other industry sources. In-process for rolling 17 of these out:
• Become a Data Scientist
• What's Involved with Data Science & Big Data Careers?
• Become an AWS Data & DevOps Specialist
• Become a Data Visualization Specialist: Concepts (b/w “Tools”)
• Learn AI/Machine Learning, Level 1: Introduction to Machine Learning
• Learn AI/Machine Learning, Level 2: Deep Learning and Computer Vision
• Become a Data Engineer: Mastering the Concepts
35
Building Foundational Learning Paths

36. Based from LI data and from other industry sources. In-process for rolling 17 of these out:
• Advance Your Python Data Science Skills
• Master: SQL for Data Science
• Master: Excel for Data Science
• Master: R for Data Science
• Become a Business Intelligence Specialist
• Become a Data Analytics Specialist
• Advance Your Data Science Skills in: Health Sciences
36
Building Foundational Learning Paths

37. Using LinkedIn Data
for Building Courses & Learning Paths
37

38. • We look at the
number of members
who list a particular
skill or area of
interest. (Supply)
38
Market Assessments: Supply and Demand

39. • Then we measure
that supply in YoY
growth, L90D
YoY growth, L30D
YoY growth.
(Velocity)
39
Market Assessments: Supply and Demand

40. • Next we look at
demand, i.e. how
these skills stack up
in recruiter activity:
LTM (last 12 months)
• -…as well as
demand velocity:
L90D YoY, L30D YoY.
40
Market Assessments: Supply and Demand

41. • Finally, we use a KPI algorithm to measure scarcity of supply vs
strength of demand.
41
Market Assessments: Supply and Demand

42. We developed an algorithm that parsed everything into one of four categories on a grid:
1 = Highest growth velocity, largest vector of supply/demand
42
Stack-ranking Data Results

43. 43
Member Supply: Top 30 Data Science-related Skills
Avg LinkedIn member count: 3.1 million
1. Microsoft Excel
2. Business Analysis
3. SQL
4. Financial Analysis
5. Healthcare
6. Java
7. Data Analysis
8. Inventory Management
9. JavaScript
10. Risk Management
11. C++
12. Microsoft SQL Server
13. Requirements Analysis
14. MySQL
15. Business Intelligence
16. Matlab
17. Databases
18. Six Sigma
19. Data Entry
20. Healthcare Management
21. Oracle
22. Python
23. Competitive Analysis
24. Financial Modeling
25. Statistics
26. Data Center
27. Analytics
28. SPSS
29. Google Analytics
30. Analytical Skills

44. 44
Recruiter Demand: Top 30 Data Science-related Skills
Avg LinkedIn member count: 2.9 million
1. Microsoft Excel
2. SQL
3. Java
4. JavaScript
5. MySQL
6. Business Analysis
7. C++
8. Python
9. Microsoft SQL Server
10. Databases
11. Data Analysis
12. Financial Analysis
13. Requirements Analysis
14. Business Intelligence
15. Oracle
16. Risk Management
17. Healthcare
18. Matlab
19. Analytics
20. Data Center
21. Six Sigma
22. Inventory Management
23. Financial Modeling
24. Competitive Analysis
25. Amazon Web Services
(AWS)
26. PostgreSQL
27. MongoDB
28. Data Warehousing
29. Machine Learning
30. Hadoop

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51. 51

52. Excel
… is still enormous as a tool of choice in this general
space.
Don’t miss the forest for the trees.
52
Top 30: Always Interesting Insights

53. MATLAB, SPSS, Amazon Web Services
…are healthier categories than we initially suspected.
53
Top 30: Always Interesting Insights

54. Tools are only part of the picture
…crucial, yes (see Excel, SQL, Java, Python, Hadoop)
54
Top 30: Always Interesting Insights

55. …but only a part of the much bigger
skills picture.
What you do with those tools—and what you need to
know in general about specific tasks—is just as important.
55
Top 30: Always Interesting Insights

56. Confirmation of trending tools:
See Hadoop ecosystem & open source in general.
56
Top 30: Always Interesting Insights

57. Questions:
Where is R?
Trajectory for proprietary tools (e.g. SAS, Tableau, SPSS,
Qlik) better or worse?
57
Top 30: Always Interesting Insights

58. 58
Top 30: Always Interesting Insights

59. 59
Top 30: Always Interesting Insights

60. Industry verticals:
Healthcare and data science. See any trends there?
60
Top 30: Always Interesting Insights

61. 61

62. Humble truths:
Our data only goes so far… it won’t tell us what’s
happening right now or in the very recent past:
-Apache Spark (two years ago)
-Domo, Julia (a year ago)
-Blockchain (this year)
62
Top 30: Always Interesting Insights

63. The big latitude:
-Our data is just a part of the puzzle for helping us
connect the dots between career opps and LI members.
-We still rely on knowledge of the market, which includes
having a robust network of community leaders
and influencers to check our work.
63
Top 30: Always Interesting Insights

64. Data Informs the Rest of Our Workflow, Too
64

65. • Most watched content:
instructional demo (mostly on tools
and practical skills)
• Followed by live-action,
conceptual training
• Influencer courses: Industry
leaders.
65
Course Type: Multiple Options

66. • Seeing increased demand
for serial content: Weekly,
bi-weekly, monthly.
• Increased demand for
short courses (30 minutes
or less), micro-bursts of
learning.
66
Course Type: Multiple Options

67. • Historically, Lynda.com
courses varied from 1 hours
to 8+ hours. Avg course
length was over three
hours.
• By 2015, usage data began
indicating most users
disengage after three hours.
67
Course Length: What the Data Showed Us

68. • Solution? Break longer courses
into more than one course.
• Enterprise customers: Give our
people shorter, more focused
course treatments. It can be a big
ask to require more than an hour
at a time to vertical-topic skills
training.
68
Course Length: What the Data Showed Us

69. • Customer feedback
shared with instructors
regularly.
• QA team will follow up on
actionable issues
immediately with
instructors and
content/production team
members.
69
Customer Feedback Loop

70. • Tracking traffic on which
specific video “lessons” within a
course were most accessed,
least accessed, etc.
• Helps guide course revision
decisions as well as ideas for
new areas of topic focus.
70
Customer Feedback Loop

71. Using Data Insights…
…To Serve Your Customers, Everyday
71

72. • LI has accounts with the entire
Fortune 500; LiL corporate sales
is surpassing individual account
sales (even though single
subscriber numbers are still
rising).
• Enterprise clients tend to be
practitioners, looking for very
focused (and often advanced-
level) solutions.
72
Enterprise Client Needs

73. • Curated Learning Paths
• "Time is money, so get to the point
and teach us what we need to learn.”
• Adding soft-skills courses, on myriad
issues working professionals—esp in
team environments &
communications—need to succeed.
73
Enterprise Client Needs

74. • Tend to be lower-level: the base of
the pyramid.
• Students, IT workers—and in other
business roles—looking to refocus
their career paths.
• Survey-level, foundational learning.
• Starting on the path; important to
show related courses that can help
viewers continue their learning
journeys.
74
Individual Learner Needs

75. • Employment trends in US
workforce
• National trends on
-Hiring
-Skills Gaps
-Migration Trends
• Also features localized reports for
20 largest US metro areas.
75
Monthly LinkedIn Workforce Reports

76. Feb 2017 launch; covers U.S.
market
• While ostensibly aimed at job-
seekers, the reports also offer
insights galore for recruiters—which
is why we’ve teased out the most
important trends, skill gaps, and
stats with an eye towards hiring.
76
Monthly LinkedIn Workforce Reports

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Monthly LinkedIn Workforce Reports

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Monthly LinkedIn Workforce Reports

79. • Created by LinkedIn data
researchers.
• Salaries by job title,
education level and field
of study, location,
company size, and
industry.
79
LinkedIn State of Salary Reports

80. • Created by LinkedIn data
researchers.
• Spurs more-informed,
positive decision making
from employers, e.g.
“Data on How Candidates
Want to be Recruited”.
80
LinkedIn Global Talent Trends Reports

81. • Created by LinkedIn
data researchers.
• Help others to better
understand the
dynamics of the
employment markets
and to participate in
the global
conversation.
81
LinkedIn Global Talent Trends Reports

82. Great example:
This LinkedIn blog post and infographic
inspired by Global Talent Trends Report
"The Gap Between Women and Men in
STEM and What You Can Do About It"
82
LinkedIn Global Talent Trends Reports

83. So what are WE learning (at LinkedIn
Learning)?
• Change is a constant, but all the moving
parts don’t change at the same rate.
• Curation makes a difference: Help them
find the content that’s really important.
• Embrace smart scaling: Demand for
online learning continues to climb.
83
LinkedIn Learning Data: Skills, Jobs, Careers, Futures

84. Where is this taking us?
• Shorter-term: Shorter to-market
development times for high-quality learning
content.
• Medium-term: Development of just-in-time,
short-form learning content.
• Longer-term: AI is on a trajectory to
obviate some—if not many—of the job
skills we cover
now… including data science topics.
84
LinkedIn Learning Data: Skills, Jobs, Careers, Futures

85. Data = Power.
85

86. Show What You Know.
86

87. Share That Power and
Help Others.
87

88. 88
Jeff Weiner
Our mission is to connect the world’s
professionals to make them more productive
and successful.

89. 89
Jeff Weiner
Our vision is to create economic
opportunity for every member of
the global workforce.

90. ©2014 LinkedIn Corporation. All Rights Reserved.©2014 LinkedIn Corporation. All Rights Reserved.
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