Keywords Matching--- Resume & Job Description

Jingyang Liu,Yawei Xia, Xiayu Zeng, Hong ZhangISGB/BYGB 7978, Web Analytics

Professor Yilu Zhou12/08/14

Agenda

• Introduction

• Model Design and implementation

• Results Analysis

IntroductionObjective• In this project we try to develop a resume filtering

mechanism for companies to hire qualified candidates. • We want to show the company that which candidate is the

best fit for specific positions (“data analyst” and “data scientist”) by finding keywords between resume and job descriptions.

• Also our general keywords dictionary can be used to suggest candidates how to improve their resume writing.

Datasets• Over 300 job descriptions and resumes from

www.indeed.com and www.monster.com

MethodologyData collection data input build keywords dictionary develop keyword matching model analysis results

Tools: site content analyzer, Excel, Python

Step1 Design and ImplementationCrawl 200 Job descriptions and use content analyzer to figure

out 135 keywords with most frequencies

Step 2 Data processing

Remove the redundant and meaningless high

frequency words

Combine words of similar meaning but different forms

Remove words that are part of a phrase and set these phrases

as keywords

Step 3 Data input & implementation45 keywords in our dictionary

50 Resumes

Python Code

Results

1: the keyword appears in the resume0: the keyword don’t appear in the resume

Step 4 Further Matching• Crawl 50 companies' job descriptions and run the python

code so that we get the output of 1 and 0 in Job description• Count the number of keywords that are of same outputs (0/1)

• Create matrix of 50 resumes and 50 job descriptions.

*Number of keywords that are of the same outputs （ 0 or 1 ） in resume and job description in every matching

Results Analysis

For example, No.1 Candidate (refer to Resume#1) does not have teamwork-related keywords in the resume, which is required for No.1 Job (refer to Job description#1), thus we put an “N” to indicate an unqualification of the job requirement. If a keyword appear in both resume and job description, we put a “Y” under the keyword.

Results Analysis

 

Results AnalysisAcademic Degree

Teamwork Communication Leadership Problem Solving0

5

10

15

20

25

30

35

40

45

5041

48

39

48

Soft SkillsLeaders are becoming more and more concerned with candidates’ soft skills. Teamwork, communication, leadership and problem solving skills are our target keywords. All these four qualities share mostly equal importance in the job market, but communication and problem solving are slightly higher. • People with good communication

skills have the ability to convey information more effectively either orally or in writing, strengthening their interpersonal skills as well.

Results AnalysisAcademic Degree

Microso

ft S

QL

Data

Ware

hous

e S

tat

Data

base

Othe

rs

Pyth

on E

TL S

AS

Hado

op

Algorith

m

Ora

cle Ja

va

Mac

hine Lear

ning

Bus

iness

Intel

ligenc

e

Data

Mining

0

5

10

15

20

25

30

35

40

45

50

Technical Skills

From the chart above, we can conclude that there are mainly two classes of skills that a successful data analyst has: statistics and programming. We offer some suggestions to candidates who are eager to strengthen their programming skills. For the interest of time, we suggest to start learning those software tools and programming languages that appeared on the keyword lists, which are more likely to be recognized by recruiters.

Results AnalysisAcademic Degree

Academic Degree

Bachelor Master MBA Ph.D

20%

10%

7%

7%

7%5%3%

2%

15%

25%

Majors Management

Information System

Marketing

Computer Science

Engineering

Other Quant fields

Statstics

Econ

Math

Finance

Business

• Candidates with master degree are mostly wanted for a majority of companies. First those candidates are assumed to have more professional knowledge than undergrads. Second, masters would demand less than PhD and MBA in terms of salary, pensions.

• Most companies would like to have their employees be equipped with a business academic background. It is good for candidates to have a solid technical background, but it will greatly increase their opportunity of getting a job if they also have fundamental business studies.