- Shopping Bag ( 0 items )
Want a NOOK? Explore Now
Despite the excitement around "data science," "big data," and "analytics," the ambiguity of these terms has led to poor communication between data scientists and organizations seeking their help. In this report, authors Harlan Harris, Sean Murphy, and Marck Vaisman examine their survey of several hundred data science practitioners in mid-2012, when they asked respondents how they viewed their skills, careers, and experiences with prospective employers. The results are striking.
Based on the survey data, the authors found that data scientists today can be clustered into four subgroups, each with a different mix of skillsets. Their purpose is to identify a new, more precise vocabulary for data science roles, teams, and career paths.
This report describes:
Chapter 1: Introduction;
Chapter 2: Case Studies in Miscommunication;
2.1 Rock Stars and Gods;
2.2 Apples and Oranges;
Chapter 3: A Survey of, and About, Professionals;
3.1 Clustering Data Scientists;
3.2 The Variety of Data Scientists;
3.3 Big Data;
3.4 Related Surveys;
Chapter 4: T-Shaped Data Scientists;
4.1 Evidence for T-Shaped Data Scientists;
Chapter 5: Data Scientists and Organizations;
5.1 Where Data People Come From: Science vs. Tools Education;
5.2 From Theory to Practice: Internships and Mentoring;
5.3 Teams and Org Charts;
5.4 Career Paths;
Chapter 6: Final Thoughts;
Survey Details;
Design and Invitation;
Skills List;
Non-negative Matrix Factorization;
Acknowledgements;
Colophon;
Overview
Despite the excitement around "data science," "big data," and "analytics," the ambiguity of these terms has led to poor communication between data scientists and organizations seeking their help. In this report, authors Harlan Harris, Sean Murphy, and Marck Vaisman examine their survey of several hundred data science practitioners in mid-2012, when they asked respondents how they viewed their skills, careers, and experiences with prospective employers. The results are striking.
...