Lessons from between the white lines for isolated data scientists
Program in Statistical & Data Sciences, Smith College, Northampton, Massachusetts, United States
- Published
- Accepted
- Subject Areas
- Computer Education, Data Science, Scientific Computing and Simulation, Social Computing
- Keywords
- data science, advice, sports analytics, statistical computing, applications and case studies, industry
- Copyright
- © 2017 Baumer
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Lessons from between the white lines for isolated data scientists. PeerJ Preprints 5:e3160v1 https://doi.org/10.7287/peerj.preprints.3160v1
Abstract
Many current and future data scientists will be "isolated"---working alone or in small teams within a larger organization. This isolation brings certain challenges as well as freedoms. Drawing on my considerable experience both working in the professional sports industry and teaching in academia, I discuss troubled waters likely to be encountered by newly-minted data scientists, and offer advice about how to navigate them. Neither the issues raised nor the advice given are particular to sports, and should be applicable to a wide range of knowledge domains.
Author Comment
This is part of the 'Practical Data Science for Stats' Collection.