Analyzing the Analyzers
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There has been intense excitement in recent years around activities labeled "data science," "big data," and "analytics." However, the lack of clarity around these terms and, particularly, around the skill sets and capabilities of their practitioners has led to inefficient communication between "data scientists" and the organizations requiring their services. This lack of clarity has frequently led to missed opportunities. To address this issue, we surveyed several hundred practitioners via the Web to explore the varieties of skills, experiences, and viewpoints in the emerging data science community. We used dimensionality reduction techniques to divide potential data scientists into five categories based on their self-ranked skill sets (Statistics, Math/Operations Research, Business, Programming, and Machine Learning/Big Data), and four categories based on their self-identification (Data Researchers, Data Businesspeople, Data Engineers, and Data Creatives).
Further examining the respondents based on their division into these categories provided additional insights into the types of professional activities, educational background, and even scale of data used by different types of Data Scientists. In this report, we combine our results with insights and data from others to provide a better understanding of the diversity of practitioners, and to argue for the value of clearer communication around roles, teams, and careers.
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.