Predicting the probability and salary to get data science job in top companies

Wangming Situ, Lei Zheng, Xiaozhou Yu, Mahmoud Daneshmand

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Purpose: Predict the probability to get data science job in Fortune 500 companies through predictive analysis. Methodology/approach: Following an introduction of career-based social websites and Human Resource Analytics, the authors processed the common features from LinkedIn and Glassdoor which are necessary to connect two different data sources with features as company name and job title, applied the methodology of Data Mining - Cross Industry Standard Process. The major machine learning algorithms include gradient boosting decision tree and logistic regression. Findings: Predict the probability to get the job in different categories of companies with expected salary mean. Originality/value: Instead of traditional employment survey this research base on web analytics, data mining and predictive modeling which enabled low cost, high efficiency, short lead-time analytics. The methodology could be widely used to discover all kinds of career based insights for various research purposes.

Original languageEnglish
Title of host publication67th Annual Conference and Expo of the Institute of Industrial Engineers 2017
EditorsHarriet B. Nembhard, Katie Coperich, Elizabeth Cudney
Pages933-939
Number of pages7
ISBN (Electronic)9780983762461
StatePublished - 2017
Event67th Annual Conference and Expo of the Institute of Industrial Engineers 2017 - Pittsburgh, United States
Duration: 20 May 201723 May 2017

Publication series

Name67th Annual Conference and Expo of the Institute of Industrial Engineers 2017

Conference

Conference67th Annual Conference and Expo of the Institute of Industrial Engineers 2017
Country/TerritoryUnited States
CityPittsburgh
Period20/05/1723/05/17

Keywords

  • Data Mining
  • Human Resource Analytics
  • Machine learning
  • Predictive analysis

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