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Capstone presenation
- 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
- 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
- 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
Editor's Notes
- When I started the project I felt exactly when to try to eat more than your stomach can handle. It was very hard as there was no Data; and that’s why I decided to name it Data story. This project is based on linked in data which I is hard to get due to encryption codes etc. So I tried to screp it found some codes which were not working.
- This proposal was written by Curtis Foundation a NGO, works in education and human resources enhancement; especially with law enforcements.
- Many of us are on linkedin and feel that endorsements don’t matter. Current System of endorsements is broken. Anyone can endorse anyone and there is no credibility assigned to endorsements. That’s why this project is there.
- Major questions- How to define Success? How to Normalize the titles ? What Variables to consider? What to look for for the next steps? etc. List of publications I used to create this data -
- Finally data was ready …Major hurdles were assigning titles, - Explain How we did it 2) Assigning Skills- finding and assigning Skills, issues with Scrped data in R and then transformating it to database 3) Normalization of Titles
- Not an Ideal way to create this database.
- Neo4j graphical databases is way to go and would like to recommend.
- Data class – Character, Integers, Numeric Converted character vectors into Factors for data analysis purpose.
- This will reduce the bias due to age. My observation endorsments are more for young people, who works in IT industry as well as those who have more access to computers and internet. # for HCS I propose - CS x number of similar Skills for which endorcer has been endorsed x No of endorsers who fullfills similarity criteria# THIS MODEL DOESN'T HAVE MULTIPLE ENDORSERS :- THEREFORE I HAVE ADJUSTED THE SCORE ACCORDINGLY.# I ALSO PROPOSE THAT IN FUTURE HIGH CREDIBILITY SCORE SHOULD BE ASSIGNED TO INDIVIDULA SKILL TO BRING MORE CREDIBILITY TO ENDORSEMENT SYSTEM.