Applying Analytics: A Practical Introduction

CRC Press
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Newcomers to quantitative analysis need practical guidance on how to analyze data in the real world yet most introductory books focus on lengthy derivations and justifications instead of practical techniques. Covering the technical and professional skills needed by analysts in the academic, private, and public sectors, Applying Analytics: A Practical Introduction systematically teaches novices how to apply algorithms to real data and how to recognize potential pitfalls. It offers one of the first textbooks for the emerging first course in analytics.

The text concentrates on the interpretation, strengths, and weaknesses of analytical techniques, along with challenges encountered by analysts in their daily work. The author shares various lessons learned from applying analytics in the real world. He supplements the technical material with coverage of professional skills traditionally learned through experience, such as project management, analytic communication, and using analysis to inform decisions. Example data sets used in the text are available for download online so that readers can test their own analytic routines.

Suitable for beginning analysts in the sciences, business, engineering, and government, this book provides an accessible, example-driven introduction to the emerging field of analytics. It shows how to interpret data and identify trends across a range of fields.

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About the author

Dr. Evan S. Levine is an analytics professional specializing in homeland security and counterterrorism. His work has been published in Science, Risk Analysis, Analytics magazine, and several other peer-reviewed journals. He is a member of the American Association for the Advancement of Science (AAAS), the Institute For Operations Research and Management Science (INFORMS), and the Society for Risk Analysis (SRA). He earned a Ph.D. in astrophysics from the University of California, Berkeley, and a Master of Advanced Study (MASt) in mathematics from Cambridge University.

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Additional Information

Publisher
CRC Press
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Published on
Jun 21, 2013
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Pages
290
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ISBN
9781466557192
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Best For
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Language
English
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Genres
Business & Economics / Operations Research
Business & Economics / Statistics
Design / Product
Mathematics / Probability & Statistics / General
Technology & Engineering / Operations Research
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Content Protection
This content is DRM protected.
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Eligible for Family Library

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We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we get a car loan, how much we pay for health insurance—are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated.

But as Cathy O’Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination: If a poor student can’t get a loan because a lending model deems him too risky (by virtue of his zip code), he’s then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a “toxic cocktail for democracy.” Welcome to the dark side of Big Data.

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Whenweagreedtoshareallofourpreparationofexercisesinsamplingtheory to create a book, we were not aware of the scope of the work. It was indeed necessary to compose the information, type out the compilations, standardise the notations and correct the drafts. It is fortunate that we have not yet measured the importance of this project, for this work probably would never have been attempted! In making available this collection of exercises, we hope to promote the teaching of sampling theory for which we wanted to emphasise its diversity. The exercises are at times purely theoretical while others are originally from real problems, enabling us to approach the sensitive matter of passing from theory to practice that so enriches survey statistics. The exercises that we present were used as educational material at the École Nationale de la Statistique et de l’Analyse de l’Information (ENSAI), where we had successively taught sampling theory. We are not the authors of all the exercises. In fact, some of them are due to Jean-Claude Deville and Laurent Wilms. We thank them for allowing us to reproduce their exercises. It is also possible that certain exercises had been initially conceived by an author that we have not identi?ed. Beyondthe contribution of our colleagues, and in all cases, we do not consider ourselves to be the lone authors of these exercises:they actually form part of a common heritagefrom ENSAI that has been enriched and improved due to questions from students and the work of all the demonstrators of the sampling course at ENSAI.
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