The Accidental Data Scientist is pretty much a how-to guide for information professionals and students interested in the world of Big Data and potential employment in the field. As the media continues to incite businesses to get on the Big Data bandwagon, Amy Affelt explains that human resource managers continue to look toward computer programmers and IT professionals to fill data scientist positions. This book argues that there is an inherent connection between traditional library skills and the professional knowledge needed to deal with Big Data. Removing the “Big” from Big Data, she demonstrates how the ability of library and information professionals (LIP) “to curate, evaluate, analyze, and transform data and information into insightful
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To complete this Big Data tutorial, Affelt summarizes several examples of easy-to-use data visualization and predictive algorithm tools. The terms are reassembled into a “Big Data Glossary”—found within Chapter 2—which is a very helpful quick guide. Affelt does not assert that all LIPs need to know how to install and run IT infrastructure, but it is critical to have a working knowledge of the systems. In addition, common language will encourage opportunities to join Big Data teams. As technology advances, however, I question how fast the software and tools specifically described here will become obsolete, thus quickly making this extremely timely book …show more content…
A practicing LIP already has expertise in search and discovery, analyzing relevancy and assuring validity, as well as, delivering projects and communicating results. Affelt suggests ways to translate existing LIS job functions to work in data science. In Chapter 7, she takes the reference interview—a widely accepted LIS technique to ascertain a user’s information needs—and provides a step-by-step protocol to repurpose the method to assist in solving data challenges. Walking the reader through real world scenarios very much illustrates the process. Another compelling section of the book details the value of a law LIP’s knowledge over pre-programmed software which can be summarized as, Big Data plus a LIP equals better data. The book’s emphasis on human factors in data science is strong and it also presents Big Data projects that failed as a way to stress the problems of false interpretation in analysis as
To complete this Big Data tutorial, Affelt summarizes several examples of easy-to-use data visualization and predictive algorithm tools. The terms are reassembled into a “Big Data Glossary”—found within Chapter 2—which is a very helpful quick guide. Affelt does not assert that all LIPs need to know how to install and run IT infrastructure, but it is critical to have a working knowledge of the systems. In addition, common language will encourage opportunities to join Big Data teams. As technology advances, however, I question how fast the software and tools specifically described here will become obsolete, thus quickly making this extremely timely book …show more content…
A practicing LIP already has expertise in search and discovery, analyzing relevancy and assuring validity, as well as, delivering projects and communicating results. Affelt suggests ways to translate existing LIS job functions to work in data science. In Chapter 7, she takes the reference interview—a widely accepted LIS technique to ascertain a user’s information needs—and provides a step-by-step protocol to repurpose the method to assist in solving data challenges. Walking the reader through real world scenarios very much illustrates the process. Another compelling section of the book details the value of a law LIP’s knowledge over pre-programmed software which can be summarized as, Big Data plus a LIP equals better data. The book’s emphasis on human factors in data science is strong and it also presents Big Data projects that failed as a way to stress the problems of false interpretation in analysis as