Data mining for the social sciences : an introduction /

Data mining for the social sciences : an introduction /

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Bibliographic Details
Author / Creator:	Attewell, Paul A., 1949- author.
Imprint:	Oakland, California : University of California Press, [2015]
Description:	xi, 252 pages ; 26 cm
Language:	English
Subject:	Social sciences -- Data processing. Social sciences -- Statistical methods. Data mining. Data mining. Social sciences -- Data processing. Social sciences -- Statistical methods.
Format:	Print Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/10393791

Hidden Bibliographic Details
Other authors / contributors:	Monaghan, David B., 1988- author. Kwong, Darren, author.
ISBN:	9780520280977 0520280970 9780520280984 0520280989 9780520960596 0520960599
Notes:	Includes bibliographical references and index.
Summary:	"We live, today, in world of big data. The amount of information collected on human behavior every day is staggering, and exponentially greater than at any time in the past. At the same time, we are inundated by stories of powerful algorithms capable of churning through this sea of data and uncovering patterns. These techniques go by many names - data mining, predictive analytics, machine learning - and they are being used by governments as they spy on citizens and by huge corporations are they fine-tune their advertising strategies. And yet social scientists continue mainly to employ a set of analytical tools developed in an earlier era when data was sparse and difficult to come by. In this timely book, Paul Attewell and David Monaghan provide a simple and accessible introduction to Data Mining geared towards social scientists. They discuss how the data mining approach differs substantially, and in some ways radically, from that of conventional statistical modeling familiar to most social scientists. They demystify data mining, describing the diverse set of techniques that the term covers and discussing the strengths and weaknesses of the various approaches. Finally they give practical demonstrations of how to carry out analyses using data mining tools in a number of statistical software packages. It is the hope of the authors that this book will empower social scientists to consider incorporating data mining methodologies in their analytical toolkits"--Provided by publisher.

Table of Contents:

What is data mining?
Contrasts with the conventional statistical approach
Some general strategies used in data mining
Important stages in a data mining project
Preparing training and test datasets
Variable selection tools
Creating new variables using binning and trees
Extracting variables
Classifiers
Classification trees
Neural networks
Clustering
Latent class analysis and mixture models
Association rules.