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:
Four data scientist clusters: Data Businesspeople, Data Creatives, Data Developers, and Data Researchers
Cases in miscommunication between data scientists and organizations looking to hire
Why T-shaped data scientists have an advantage in breadth and depth of skills
How organizations can apply the survey results to identify, train, integrate, team up, and promote data scientists
Описание: Written in an accessible manner, this book provides an introduction to Social Network Analysis (SNA), presenting tools and concepts, and showing how SNA can inform the crafting of a wide array of strategies for the tracking and disrupting of dark networks (illegal and covert networks that analysts must track, identify, and dismantle).
Описание: Written in an accessible manner, this book provides an introduction to Social Network Analysis (SNA), presenting tools and concepts, and showing how SNA can inform the crafting of a wide array of strategies for the tracking and disrupting of dark networks (illegal and covert networks that analysts must track, identify, and dismantle).
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