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1140 track 1 weiss_using his mac

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​ Steve Weiss
​ Content Manager, Data Science & Business Analytics
​ LinkedIn Learning
The Sprint for Teaching Data Scienc...

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LinkedIn: Quick Data Points

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1140 track 1 weiss_using his mac

  1. 1. ​ 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
  2. 2. 3
  3. 3. 4 LinkedIn: Quick Data Points
  4. 4. 141 million+ workers in the U.S. have LinkedIn profiles 5
  5. 5. 20,000+ companies in the U.S. use LinkedIn to recruit 6
  6. 6. 3 million+ Jobs posted on LinkedIn US monthly 7
  7. 7. 11 million+ Open jobs posted on LinkedIn Jobs Avg 8
  8. 8. 50,000+ skills on LinkedIn that members can add to their profiles 9
  9. 9. That gives us unique and valuable insight into U.S. workforce trends… 10 LinkedIn: Quick Data Points
  10. 10. … and to the learning resources we can create to help workers in the U.S. and around the world. 11 LinkedIn: Quick Data Points
  11. 11. 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
  12. 12. How We Got Here: The Rise of Lynda.com and Online Learning 13
  13. 13. • 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
  14. 14. • 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
  15. 15. 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
  16. 16. • 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
  17. 17. 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
  18. 18. The Opportunity Skills Training for Working Professionals 19
  19. 19. • >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
  20. 20. • 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
  21. 21. 2015… Time for a: • Dedicated Data Science course library • Dedicated Business Analytics course library 22 Dedicated Library: Data Science & Business Analytics
  22. 22. • Within Tech topic areas, Data Science is our fastest-growing library in terms of demand. 23 Growth of Data Science as a Category
  23. 23. • Business Analytics is the bridge category between “Tech/Data Science” and “Business” topics. 24 Growth of Business Analytics as a Category
  24. 24. 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)
  25. 25. 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)
  26. 26. A few terrific courses from Barton Poulson covering R, SPSS, conceptual intros. (Barton rocks, BTW) 27 Existing Courses: Where We Started (2015)
  27. 27. The Strategy Go-To Market for Data Science Training 28
  28. 28. 29 The List of 100 Priority Courses
  29. 29. Ranged from baseline concepts (statistics courses needed!)… 30 The List of 100 Priority Courses
  30. 30. …to key tools (more Hadoop, more Python, more SQL and NoSQL, etc) 31 The List of 100 Priority Courses
  31. 31. …to acknowledging emerging leaders Apache Spark; R eclipsing SAS; etc. 32 The List of 100 Priority Courses
  32. 32. …to covering applied use of data science: finance vs marketing vs sports science vs healthcare, etc. 33 The List of 100 Priority Courses
  33. 33. -to coverage of soft skills, such as building and managing data science teams. 34 The List of 100 Priority Courses
  34. 34. 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
  35. 35. 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
  36. 36. Using LinkedIn Data for Building Courses & Learning Paths 37
  37. 37. • We look at the number of members who list a particular skill or area of interest. (Supply) 38 Market Assessments: Supply and Demand
  38. 38. • Then we measure that supply in YoY growth, L90D YoY growth, L30D YoY growth. (Velocity) 39 Market Assessments: Supply and Demand
  39. 39. • 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
  40. 40. • Finally, we use a KPI algorithm to measure scarcity of supply vs strength of demand. 41 Market Assessments: Supply and Demand
  41. 41. 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
  42. 42. 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
  43. 43. 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
  44. 44. 45
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  48. 48. 49
  49. 49. 50
  50. 50. 51
  51. 51. 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
  52. 52. MATLAB, SPSS, Amazon Web Services …are healthier categories than we initially suspected. 53 Top 30: Always Interesting Insights
  53. 53. Tools are only part of the picture …crucial, yes (see Excel, SQL, Java, Python, Hadoop) 54 Top 30: Always Interesting Insights
  54. 54. …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
  55. 55. Confirmation of trending tools: See Hadoop ecosystem & open source in general. 56 Top 30: Always Interesting Insights
  56. 56. Questions: Where is R? Trajectory for proprietary tools (e.g. SAS, Tableau, SPSS, Qlik) better or worse? 57 Top 30: Always Interesting Insights
  57. 57. 58 Top 30: Always Interesting Insights
  58. 58. 59 Top 30: Always Interesting Insights
  59. 59. Industry verticals: Healthcare and data science. See any trends there? 60 Top 30: Always Interesting Insights
  60. 60. 61
  61. 61. 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
  62. 62. 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
  63. 63. Data Informs the Rest of Our Workflow, Too 64
  64. 64. • 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
  65. 65. • 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
  66. 66. • 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
  67. 67. • 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
  68. 68. • 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
  69. 69. • 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
  70. 70. Using Data Insights… …To Serve Your Customers, Everyday 71
  71. 71. • 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
  72. 72. • 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
  73. 73. • 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
  74. 74. • 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
  75. 75. 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
  76. 76. 77 Monthly LinkedIn Workforce Reports
  77. 77. 78 Monthly LinkedIn Workforce Reports
  78. 78. • 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
  79. 79. • 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
  80. 80. • 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
  81. 81. 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
  82. 82. 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
  83. 83. 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
  84. 84. Data = Power. 85
  85. 85. Show What You Know. 86
  86. 86. Share That Power and Help Others. 87
  87. 87. 88 Jeff Weiner Our mission is to connect the world’s professionals to make them more productive and successful.
  88. 88. 89 Jeff Weiner Our vision is to create economic opportunity for every member of the global workforce.
  89. 89. ©2014 LinkedIn Corporation. All Rights Reserved.©2014 LinkedIn Corporation. All Rights Reserved. Thanks! Let’s stay in touch... sweiss@linkedin.com

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