Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1dGIrxX. 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 qconsf.com. 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.

1. Data Science for Higher Ed
Gloria Lau
Manager, Data Science @ LinkedIn

2. Watch the video with slide
synchronization on InfoQ.com!
http://www.infoq.com/presentations
/data-analysis-hiring
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5. LinkedIn data.
For students*.
*prospective students, current students and recent graduates

6. WHY?
We have career outcome data to derive
better insights about higher education

7. 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?

8. The Answer for the type A’s
Show me the career outcome data per school / field of study / degree

9. 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

10. OK! Let’s start building some data
products for students!
type A’s and non type A’s, we have answers for you

12. Invest in Plumbing

13. Before your faucets

14. Data Science for Higher Ed
A case study
From plumbing to fixture.
From standardization to delightful data products.

15. 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

16. Entity Resolution
•
User types in University of California, Berkeley  easy
•
User types in UCB  hard / ambiguous

17. Entity Resolution
•
Name feature: fuzzy match, edit distance, prefix match,
etc
•
Profile feature: email, groups, etc
•
Network feature: connections, invitations, etc

18. 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

19. 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

20. 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

21. 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

22. Plumbing? checked
Onto building delightful* data products
*The level of delightfulness is directly correlated to
how good your standardization layer is.

23. Similar Schools
Serendipitous discoveries. Sideways browse.
Based on career outcome data + some more.

24. Similar Schools

25. 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

26. 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

27. What’s wrong?
Degree bucketization

28. 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

29. Notable Alumni
Aspirations. Connecting the dots.

31. Notable Alumni
•
Who’s notable?
•
Wikipedia match
•
•
•
School standardization
Name mapping
Success stories

32. Who’s notable – Wikipedia
stories
…

33. 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

34. Crowdsourcing for evaluation

35. Are we done? Do we have notable
alumni for all schools?
Similar issue like similar schools – data sparseness

36. 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…

37. 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

38. Evaluation – I know it when I
see it

39. INSIGHTS:
unique & standardized data to describe schools.
similar schools.
notable alumni.
to drive STUDENT DECISIONS

40. Watch the video with slide synchronization on
InfoQ.com!
http://www.infoq.com/presentations/dataanalysis-hiring