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We live in a world of big data: the amount of information collected on human behavior each day is staggering, and exponentially greater than at any time in the past. Additionally, powerful algorithms are capable of churning through seas of data to uncover patterns. Providing a simple and accessible introduction to data mining, Paul Attewell and David B. Monaghan discuss how data mining substantially differs from conventional statistical modeling familiar to most social scientists. The authors also empower social scientists to tap into these new resources and incorporate data mining methodologies in their analytical toolkits. Data Mining for the Social Sciences demystifies the process by describing the diverse set of techniques available, discussing the strengths and weaknesses of various approaches, and giving practical demonstrations of how to carry out analyses using tools in various statistical software packages.

Social sciences --- Data mining. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Data processing. --- Statistical methods. --- analyzing data. --- bayesian networks. --- big data. --- bootstrapping. --- business analytics. --- chaid. --- classification and regression trees. --- classification trees. --- confusion matrix. --- data analysis. --- data mining. --- data processing. --- data scholarship. --- data science. --- hardware for data mining. --- heteroscedasticity. --- naive bayes. --- partition trees. --- permutation tests. --- scholarly data. --- social science. --- social scientists. --- software for data mining. --- statistical methods. --- statistical modeling. --- studying data. --- text mining. --- vif regression. --- weka.