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Analyzing the Analyzers: An Introspective Survey of Data Scientists and Their Work [Kindle Edition]

Harlan Harris , Sean Murphy , Marck Vaisman

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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

Über den Autor und weitere Mitwirkende

Harlan D. Harris is a Senior Data Scientist at Kaplan Test Prep, the Co-Founder and Co-Organizer of the Data Science DC Meetup, and the Co-Founder and President of Data Community DC, Inc. He has a PhD in Computer Science (Machine Learning) from the University of Illinois at Urbana-Champaign and worked as a researcher in several Psychology departments before turning to industry. Sean Patrick Murphy, with degrees in mathematics, electrical engineering, and biomedical engineering and an MBA from Oxford University, has served as a senior scientist at the Johns Hopkins Applied Physics Laboratory for the past ten years. Previously, he served as the Chief Data Scientist at WiserTogether, a series A funded health care analytics firm, and the Director of Research at Manhattan Prep, a boutique graduate educational company. He was also the co-founder and CEO of a big data-focused startup: CloudSpree. Marck Vaisman is a data scientist, consultant, entrepreneur, master munger and hacker. Marck is the Principal Data Scientist at DataXtract, LLC helping clients from start-ups to Fortune 500 firms with all kinds of data science projects. His professional experience spans the management consulting, telecommunications, Internet, and technology industries. He is the co-founder of Data Community DC, Inc. and co-organizer of the Data Science DC and R Users DC meetup groups. He has an MBA from Vanderbilt University and a B.S. in Mechanical Engineering from Boston University. Marck is also a contributing author of The Bad Data Handbook.


  • Format: Kindle Edition
  • Dateigröße: 659 KB
  • Seitenzahl der Print-Ausgabe: 40 Seiten
  • ISBN-Quelle für Seitenzahl: 1449371760
  • Gleichzeitige Verwendung von Geräten: Keine Einschränkung
  • Verlag: O'Reilly Media; Auflage: 1 (10. Juni 2013)
  • Verkauf durch: Amazon Media EU S.à r.l.
  • Sprache: Englisch
  • ASIN: B00DBHTE56
  • Text-to-Speech (Vorlesemodus): Aktiviert
  • X-Ray:
  • Amazon Bestseller-Rang: #7.638 Kostenfrei in Kindle-Shop (Siehe Top 100 - Kostenfrei in Kindle-Shop)

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Die hilfreichsten Kundenrezensionen auf (beta) 4.8 von 5 Sternen  5 Rezensionen
5.0 von 5 Sternen A Thougthful And Excellent Essay 26. August 2014
Von Nathan Albright - Veröffentlicht auf
Format:Kindle Edition|Verifizierter Kauf
This particular essay is one of pleasing personal relevance to me, although it is unlikely that there are too many other people within my acquaintance for whom this work would be particularly relevant. The subject matter of this particular essay is the desire to make more precise the popular understanding of data scientist, to add nuance to the understanding of distinct groups of people whose jobs involve the analysis of data (this would include people like me). I am aware that this particular book may not be interesting for a wide audience, but it is short and reads like a pleasant sort of business novel of the kind that I read from time to time[4]. For those readers who are involved in data analysis as part of their own work or who have an interest in the intersection of business and mathematics, this is a good, and quick, read.

The heart of this particular essay, which is a qualitative study (for those who wish to know) is the discussion and division of the rather large and vague buzzword “data scientist” into four distinct types of expertise: data businesspeople (which would be my general category, those who analyze business data with an eye towards discerning profitability in the aid of business strategy), data creatives (who are more jack-of-all-trades who are heavily involved in writing code and creating data algorithms), data developers (who are involved heavily in machine operations and who often come from a strong computer science background), and data researchers (who often come from a social science background and who are involved in the writing of academic research based on their analysis of data, something that is also not too far out of my own areas of expertise and interest). By dividing up a rather large and vague term into four more precise terms with a fair degree of structure and cohesion, the authors hope to contribute to a greater precision in understanding what sorts of people do different types of work with data. Among the more interesting qualities that the authors uncover is that those who are best at data analysis often combine an interest and aptitude in mathematics and/or programming with a rather broad and diverse background that includes hard sciences as well as social sciences. This would seem to describe me rather accurately, which provides anecdotal evidence, at least, of the soundness of the author’s conclusions.
5.0 von 5 Sternen good knowledge 1. April 2014
Von Bess - Veröffentlicht auf
Format:Kindle Edition|Verifizierter Kauf
The book give some good ideas in the book and because it was free I loved it. It's great for ideas
5.0 von 5 Sternen Loved the way the field was broken down 18. August 2013
Von NG - Veröffentlicht auf
Format:Kindle Edition
THis book was an eye-opener in how Data Science is evolving and where we can expect it to go. As someone who is going to study for a Masters in this subject, it was great to see this break down.
5.0 von 5 Sternen Great FREE booklet for Data Scientist newbies (or not) 25. Juli 2013
Von GABRIEL CAPPARELLI - Veröffentlicht auf
Format:Kindle Edition|Verifizierter Kauf
This book tells you why a complete Data Scientist probably doesn't exist. With so many skill sets required, you will get a feel under what branch you fit best if you are interested in taking the DS journey.
4.0 von 5 Sternen An insight into the diversity of roles and skills of data scientists 7. Juli 2013
Von Cerys - Veröffentlicht auf
Data science is a hot topic at the moment, especially when it comes to 'big data'. Many organisations are looking for data scientists or data specialists to help them analyse their business data or to produce software and tools to help them optimise their businesses. The problem is that data science represents a broad variety of skills, technical know-how, and practitioners from a wide variety of backgrounds. There is a lack of common terminology and understanding of the role leading to problems communicating what data scientists do, what problems they solve, and the requirements for business and within organisations. This book provides an insight into the backgrounds, skills, and activities of data scientists based upon a recent survey of people identifying themselves as data scientists. The book attempts a classification of the different types of data scientists based upon the work they do and the skills that they bring. As well as a diversity of skills across the participants, the depth and breath of the skills across the different data scientist types is discussed. The book also compares the work of the authors with surveys from other researchers. Recommendations are provided for data scientists in how they might want to develop and pitch their skills to business when looking for new opportunities, and how businesses might try to find data scientists appropriate skills for their particular requirements and support their staff in those roles.

The book is basically an extended academic paper with details of the authors approach, findings, and recommendations that is an interesting read for people who consider themselves data scientists or who are looking to bring data scientists into their teams or businesses. The book also contains links to related material and a questionnaire so you can identify the type of data scientist that most closely represents your role and skill set.
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