Quantifying the reputation and success of data scientists
Success has always been a source of obsession for humans. For decades, researchers and scientists have studied the issue of success in the hopes of replicating it. The career patterns that characterise the emergence of scientific achievements still remain unclear. The goal of this research is to ide...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/154364 |
| Summary: | Success has always been a source of obsession for humans. For decades, researchers and scientists have studied the issue of success in the hopes of replicating it. The career patterns that characterise the emergence of scientific achievements still remain unclear. The goal of this research is to identify success indicators and tactics for aspiring young data scientists who want to improve their chances of becoming reputable and successful. People can work more meaningfully towards their goals if they can forecast and be aware of the aspects that contribute to success. The rank and prestige of the venue where data scientists' work is published are used to measure their success and reputation. A coauthorship network is mapped out using data management conferences publications from the dblp database to find patterns and explore the association between predictors and success. We investigate the career trajectory of data scientists and assess the impact of various factors on success and reputation by identifying data scientists with a central influence. High initial reputation was the strongest correlate of success and reputation. Collaboration and networking with success and reputable data scientists also improves one’s chances at success and reputation. We contend that aspiring data scientists should be well-aware of the important factors and strive to publish at prominent conferences early in their careers. |
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