Renuka Kulkarni-Kelapure created a simulated data model based on LinkedIn to analyze whether success is related to endorsements. The project involved: 1) Creating a MySQL database with simulated professional profiles including demographics, titles, skills, endorsements, and salaries. 2) Defining and calculating success and credibility scores based on titles, experience, endorsements, and other variables. 3) Analyzing the correlations between factors like endorsements, experience, job changes, and the success score; results showed endorsements are not significantly correlated with success.

1. A DATASTORY
Renuka Kulkarni-Kelapure

2. When I started the project….

3. Introduction and Expectations
Two words about Sponsor – Curtis Foundation
Assigned Tasks
1)To create a Simulated Data Model Based on Linkedin
2) Create a MYSQL DATABASE
3)Analysis-
A) Is success related to number of endorsements?
B)Classification of Endorsements- Toxonomy creation
C) Creation of Credibility Score
D)Creation of Success Score

4. Why? Why ? Why?

5. The Beginnings…….
Understanding the
proposal
• Define Success
• Normalization of
Titles
• What Variables to
Consider
Research /Literature
Review
• How do we define
variables ?
• How to create
based data model
Coding
• How to write loops
?
• How to I assign
multiple titles to
one person
• Figuring out
logistics
• How to avoid
biases

6. Assumptions and Definition
Age group – 22- 65 ( 1950- 1995)
Apply to IT industry only
Success is defined objectively , not subjectively in this model
Job Titles are normalized based on the responsibilities of jobs.
Titles are divided into 6 groups – 1 – Lowest ; 6 Highest
Assignment of job titles is based on education. As per job
requirement analysis most of the IT jobs need Undergraduate and
above.
Only US population is considered in this model.
Created 3 levels of Endorsers.

7. Creation of DataDemographics
• Generator to
create names ,
DOB
• Usmap- location
• Derived Age,
YOE, No of Jobs
changed
• No of titles – 6
• Randomly
assigned – No of
connections,
• Education,
Gender, Race
• Salary is based
on Current Titles
, and Location
Endorsers
• Randomly
Assigned
YOE,
endorsers
Titles, No of
Endorsers ID
Titles
• We have
derived up to
5 previous
titles .
Previous
titles are
based on
education
and No of
titles.
• Titles are
based in
Education,
YOE.
Skill
• Scrap data on
Skills and
Endorsements
from linked in
• Normalized the
Skills based on
Titles .
• Assigned Skilled
Randomly among
each group
based on current
titles and Years
of experience
• No. of
endorsements
randomly
assigned

8. One down two to go …DATABASE

9. Best DataBase Model

10. Descriptive Analysis
Tables Rows Columns
Demo 973 42
CurtisSkill2 8986 7
EndorSkill11 4689 8
EndorSkill21 4736 8
EndorSkill31 4648 8
Master Title 244 2
6 24276 72

11. Success Score
What Variables are highly
correlated?
Success Score – (sum(Title
Level+(Current_Salary/1000)+Fortu
ne500))/Age

12. Credibility Score(CS) VS High
Credibility Score(HCS)
CS =Title level + year of experience+(median of total
endorsements)
HCS= CS x No. of similar Skills for which endorser has been
endorsed x No. of endorsers who fullfill similar criteria

13. Do No. of endorsements Matter?
No. of titles, No. of job
Changed, and year of
experience are strongly
negatively correlated to
Success Score.
Success Score is strongly
negatively correlated.

14. Regression- Model 1 Success Score Vs Total
Endorsements
The results are nonsignificant. That proves current endorsement
system is broken

15. Multivariate Analysis

16. Future Pathways ….
Testing Simulated Model against real life data model
Creation of Neo4j graphical database
Creation and Validation of High Credibility Score per person
per skill
Measuring the impact of score system on personal success
 Use of system in future Skill-Gap Analysis

17. Thank You