TY - GEN
T1 - Predicting the probability and salary to get data science job in top companies
AU - Situ, Wangming
AU - Zheng, Lei
AU - Yu, Xiaozhou
AU - Daneshmand, Mahmoud
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Data Mining
KW - Human Resource Analytics
KW - Machine learning
KW - Predictive analysis
UR - http://www.scopus.com/inward/record.url?scp=85031009805&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85031009805&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85031009805
T3 - 67th Annual Conference and Expo of the Institute of Industrial Engineers 2017
SP - 933
EP - 939
BT - 67th Annual Conference and Expo of the Institute of Industrial Engineers 2017
A2 - Nembhard, Harriet B.
A2 - Coperich, Katie
A2 - Cudney, Elizabeth
T2 - 67th Annual Conference and Expo of the Institute of Industrial Engineers 2017
Y2 - 20 May 2017 through 23 May 2017
ER -