This document discusses using LinkedIn data to build data products that provide insights about higher education outcomes to help students with decisions. It describes developing standardized representations of schools, degrees, jobs, and companies to power features like similar schools comparisons and notable alumni lists. The goal is to help prospective, current, and recent students answer common questions about choosing a school or major, finding internships and jobs, or pursuing further education. The challenges include data sparsity for some schools and standardizing diverse information. Crowdsourcing helps address these issues to generate insightful and useful data products for students.

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

3. LinkedIn data.
For students*.
*prospective students, current students and recent graduates

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

5. 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?

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

7. 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

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

10. Invest in Plumbing

11. Before your faucets

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

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

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

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

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

22. Similar Schools

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. 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. What’s wrong?
Degree bucketization

26. Similar schools - issues
•
Observation: no data
•
New community colleges and non-US
schools have very sparse data
•
Solution: attribute-based similarity
•
From IPEDS and crowdsourced data
Kyoritsu Women's University

27. Notable Alumni
Aspirations. Connecting the dots.

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

30. Who’s notable – Wikipedia stories
…

31. Wikipedia stories
•
Lightweight school standardization
•
•
Name mapping
•
•
✓ Name feature ✕ profile feature ✕ network feature
Even when you are notable, your name isn’t unique
Crowdsourcing for evaluation
•
Profile from LinkedIn vs profile from Wikipedia

32. Crowdsourcing for evaluation

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

34. 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…

35. 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

36. Evaluation – I know it when I see it

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