Quantifying reputation and success of data scientists

Abstract

In today’s hyper-competitive world, people are obsessed with success. For decades, researchers and scientist have studied the issue of success and quantifying them. However, not all success can be easily quantified, such as the success of researchers or artists. Unlike sports, where there are many objective criteria used to measure the success of teams, such as the best football club is the one that wins the Champions League, these criteria are missing for some subjects. This project exploits network science techniques to understand and predict success of data scientist. We utilize Database Systems and Logic Programming (DBLP) datasets to track the career of data scientists in data management conferences over time and perform network analytics to understand and quantify their career success. Data scientists from 13 data management conferences are used to create a co-authorship network to find relationships and correlations.