Data Science for Higher Ed

Gloria LauManager, Data Science @ LinkedIn

LinkedIn data. LinkedIn data. For students*.For students*.

*prospective students, current students and recent graduates

WHY?WHY?We have career outcome data to We have career outcome data to

derive better insights about higher derive better insights about higher educationeducation

Common questions from user studiesCommon 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?

The Answer for the type A’sThe Answer for the type A’s

Show me the career outcome data per school / field of study / degree

The Answer for the exploratory kindThe 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

OK! Let’s start building some data OK! Let’s start building some data products for students!products for students!

type A’s and non type A’s, we have answers for you

Invest in Plumbing

Before your faucets

Data Science for Higher EdData Science for Higher EdA case studyA case study

From plumbing to fixture. From standardization to delightful data products.

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

Entity Resolution

• User types in University of California, Berkeley easy

• User types in UCB hard / ambiguous

Entity Resolution

• Name feature: fuzzy match, edit distance, prefix match, etc

• Profile feature: email, groups, etc

• Network feature: connections, invitations, etc

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

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

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

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

Plumbing? checkedPlumbing? checkedOnto building delightful* data products

*The level of delightfulness is directly correlated to how good your standardization layer is.

Similar SchoolsSimilar SchoolsSerendipitous discoveries. Sideways browse.Based on career outcome data + some more.

Similar Schools

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

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

What’s wrong?What’s wrong?Degree bucketization

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

Notable AlumniNotable AlumniAspirations. Connecting the dots.

Notable Alumni

• Who’s notable?

• Wikipedia match

• School standardization

• Name mapping

• Success stories

Who’s notable – Wikipedia stories

…

Wikipedia stories

• Lightweight school standardization

• �✓ Name feature ✕ profile feature �✕ network feature

• Name mapping

• Even when you are notable, your name isn’t unique

• Crowdsourcing for evaluation

• Profile from LinkedIn vs profile from Wikipedia

Crowdsourcing for evaluation

Are we done? Do we have notable Are we done? Do we have notable alumni for all schools?alumni for all schools?

Similar issue like similar schools – data sparseness

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

• A top company

• Senior

• An alum

• They all depend on…

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

Evaluation – I know it when I see it

INSIGHTS: unique & standardized data to describe schools.

similar schools.notable alumni.

to drive STUDENT DECISIONS