Data Science for Hire Ed


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Gloria Lau describes some of the products built for the higher education sector, the data standardization process, determining school similarity and identifying notable alumni. Filmed at

Gloria Lau leads the core data products team at Linkedin. Her team focuses on understanding and engaging members to construct the best professional identity on the web, including education and occupation, and builds interesting data products on top of said data. Previously, she was a research scientist at FindLaw, a Thomson Reuters business. She has a MS and PhD from Stanford, and BS from UCLA.

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Data Science for Hire Ed

  1. 1. Data Science for Higher Ed Gloria Lau Manager, Data Science @ LinkedIn
  2. 2. Watch the video with slide synchronization on! /data-analysis-hiring News & Community Site • 750,000 unique visitors/month • Published in 4 languages (English, Chinese, Japanese and Brazilian Portuguese) • Post content from our QCon conferences • News 15-20 / week • Articles 3-4 / week • Presentations (videos) 12-15 / week • Interviews 2-3 / week • Books 1 / month
  3. 3. Presented at QCon San Francisco Purpose of QCon - to empower software development by facilitating the spread of knowledge and innovation Strategy - practitioner-driven conference designed for YOU: influencers of change and innovation in your teams - speakers and topics driving the evolution and innovation - connecting and catalyzing the influencers and innovators Highlights - attended by more than 12,000 delegates since 2007 - held in 9 cities worldwide
  4. 4. LinkedIn data. For students*. *prospective students, current students and recent graduates
  5. 5. WHY? We have career outcome data to derive better insights about higher education
  6. 6. Common questions from user studies Prospective students: I want to be a pediatrician. Where should I go to school? I don’t know what I want but I am an A student. So? Current students: Show me the internship / job opportunities. Should I double / change major? Recent graduates: Show me the job opportunities. Should I consider further education?
  7. 7. The Answer for the type A’s Show me the career outcome data per school / field of study / degree
  8. 8. The Answer for the exploratory kind Show me the career outcome data in a form that allows for serendipitous discoveries  build me some data products to help me draw insights from aggregate data  build me some data products that are delightful
  9. 9. OK! Let’s start building some data products for students! type A’s and non type A’s, we have answers for you
  10. 10. Invest in Plumbing
  11. 11. Before your faucets
  12. 12. Data Science for Higher Ed A case study From plumbing to fixture. From standardization to delightful data products.
  13. 13. Standardization • Standardization is about understanding our data, and building the foundational layer that maps <school_name> to <school_id> so that we can build data products on top • Entity resolution • Recognizable entities • Typeahead
  14. 14. Entity Resolution • User types in University of California, Berkeley  easy • User types in UCB  hard / ambiguous
  15. 15. Entity Resolution • Name feature: fuzzy match, edit distance, prefix match, etc • Profile feature: email, groups, etc • Network feature: connections, invitations, etc
  16. 16. Recognizable entities • User types in University of California, Berkeley  easy • User types in UCB  hard / ambiguous / alias not understood • User types in 東京大学  harder / canonical name not understood
  17. 17. Recognizable entities • You don’t know what you don’t know • Your standardization is only as good as your recognized dataset • LinkedIn data is very global
  18. 18. Recognizable entities • IPEDS for US school data • Crowdsourcing for non-US school + government data • • internal and external with schema spec’ed out Alias – bootstrap from member data
  19. 19. Typeahead • Plug the hole from the front(-end) as soon as you can • Invest in a good typeahead early on so that you don’t even need to standardize • Helps standardization rate tremendously • Make sure you have aliases and localized strings in your typeahead
  20. 20. Plumbing? checked Onto building delightful* data products *The level of delightfulness is directly correlated to how good your standardization layer is.
  21. 21. Similar Schools Serendipitous discoveries. Sideways browse. Based on career outcome data + some more.
  22. 22. Similar Schools
  23. 23. Similar schools • Aggregate profile per school based on alumni data • Industry, job title, job function, company, skills, etc • Feature engineering and balancing • Dot-product of 2 aggregate profiles = school similarity
  24. 24. Similar schools – issues • Observation #1: similarity identified between tiny specialized schools and big research institutions • Observation #2: similarity identified between non-US specialized schools and big US research institutions
  25. 25. What’s wrong? Degree bucketization
  26. 26. Similar schools - issues Kyoritsu Women's University • Observation: no data • New community colleges and non-US schools have very sparse data • Solution: attribute-based similarity • From IPEDS and crowdsourced data
  27. 27. Notable Alumni Aspirations. Connecting the dots.
  28. 28. Notable Alumni • Who’s notable? • Wikipedia match • • • School standardization Name mapping Success stories
  29. 29. Who’s notable – Wikipedia stories …
  30. 30. Wikipedia stories • Lightweight school standardization • • network feature Name mapping • • ✓ Name feature ✕ profile feature ✕ Even when you are notable, your name isn’t unique Crowdsourcing for evaluation • Profile from LinkedIn vs profile from Wikipedia
  31. 31. Crowdsourcing for evaluation
  32. 32. Are we done? Do we have notable alumni for all schools? Similar issue like similar schools – data sparseness
  33. 33. Who’s notable - Success stories • Many schools don’t have notable alumni section in Wikipedia • Success stories based on LinkedIn data • Features of success • • • CXO’s at Fortune companies Generalizes to high seniority at top companies But what does it mean to be • • Senior • • A top company An alum They all depend on…
  34. 34. Standardization • Degree standardization - alumni • Company standardization • • IBM vs international brotherhood of magicians Title & seniority standardization • founder of the gloria lau franchise vs founder of LinkedIn • VP in financial sector vs VP in software engineering industry
  35. 35. Evaluation – I know it when I see it
  36. 36. INSIGHTS: unique & standardized data to describe schools. similar schools. notable alumni. to drive STUDENT DECISIONS
  37. 37. Watch the video with slide synchronization on!