Why Jupyter is data scientists’ computational notebook of choice
Sign Up to like & getrecommendations! Published in 2018 at "Nature"
DOI: 10.1038/d41586-018-07196-1
Abstract: An improved architecture and enthusiastic user base are driving uptake of the open-source web tool.An improved architecture and enthusiastic user base are driving uptake of the open-source web tool. read more here.
Keywords: jupyter data; data scientists; notebook choice; scientists computational ... See more keywords
Adjusting to the GDPR: The Impact on Data Scientists and Behavioral Researchers
Sign Up to like & getrecommendations! Published in 2019 at "Big data"
DOI: 10.1089/big.2018.0176
Abstract: Rapid growth in the availability of behavioral big data (BBD) has outpaced the speed of updates to ethical research codes and regulation of data privacy and human subjects' data collection, storage, and use. The introduction… read more here.
Keywords: behavioral researchers; data scientists; gdpr impact; adjusting gdpr ... See more keywords
How to Find a Perfect Data Scientist: A Distance-Metric Learning Approach
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Access"
DOI: 10.1109/access.2018.2870535
Abstract: The title of data scientist has been described as one of the sexiest jobs of the 21st century. Numerous efforts have been made to define the job of a data scientist in a qualitative manner… read more here.
Keywords: data scientist; data scientists; distance metric; scientist ... See more keywords
For AI rollouts, hazards reported ahead: Getting machine learning to work is made a lot harder by HR and IT issues - [Spectral Lines]
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Spectrum"
DOI: 10.1109/mspec.2019.8889896
Abstract: Implementing machine learning in the real world isn't easy. The tools are available and the road is well marked—but the speed bumps are many. ¶ That was the conclusion of panelists at the IEEE AI… read more here.
Keywords: machine; work; machine learning; rollouts hazards ... See more keywords
Academia's failure to retain data scientists
Sign Up to like & getrecommendations! Published in 2017 at "Science"
DOI: 10.1126/science.aam6116
Abstract: WE APPLAUD THE recommendations of V. Stodden et al. for “Enhancing reproducibility for computational methods” (Policy Forum, 9 December 2016, p. [1240][1]). Many of their recommendations could be fulfilled if researchers embraced modern statistical and… read more here.
Keywords: failure retain; data scientists; retain data; academia failure ... See more keywords
The Training of Next Generation Data Scientists in Biomedicine
Sign Up to like & getrecommendations! Published in 2017 at "Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing"
DOI: 10.1142/9789813207813_0059
Abstract: With the booming of new technologies, biomedical science has transformed into digitalized, data intensive science. Massive amount of data need to be analyzed and interpreted, demand a complete pipeline to train next generation data scientists.… read more here.
Keywords: data scientists; scientists biomedicine; training next; generation data ... See more keywords
Replace, absorb, serve: Data scientists talk about their aspired jurisdiction
Sign Up to like & getrecommendations! Published in 2023 at "Current Sociology"
DOI: 10.1177/00113921231166147
Abstract: How do data scientists frame their relations with domain experts? This study focuses on data scientists’ aspired professional jurisdiction and their multiple narratives regarding data science’s relations to other fields of expertise. Based on the… read more here.
Keywords: jurisdiction; replace absorb; scientists talk; absorb serve ... See more keywords