Available From UC Press

Knowledge Discovery in the Social Sciences helps readers find valid, meaningful, and useful information. It is written for researchers and data analysts as well as students who have no prior experience in statistics or computer science. Suitable for a variety of classes—including upper-division courses for undergraduates, introductory courses for graduate students, and courses in data management and advanced statistical methods—the book guides readers in the application of data mining techniques and illustrates the significance of newly discovered knowledge. 

Readers will learn to: 
• appreciate the role of data mining in scientific research 
• develop an understanding of fundamental concepts of data mining and knowledge discovery
• use software to carry out data mining tasks
• select and assess appropriate models to ensure findings are valid and meaningful
• develop basic skills in data preparation, data mining, model selection, and validation
• apply concepts with end-of-chapter exercises and review summaries
 

Xiaoling Shu is Professor of Sociology at the University of California, Davis. 

"Big data, data mining, machine learning, pattern recognition, neural networks, computational social science—all these terms in vogue today imply two facts: the first is the exponential increase in data creation; the second is the need for viable methods for making sense of all these data. Knowledge Discovery in the Social Sciences provides an accessible, timely introduction to methods old and new for social scientists and others to find insights in such data."—Tim Liao, Professor of Sociology and Statistics, University of Illinois at Urbana-Champaign

"This book introduces students to an alternative research approach that is becoming more and more important for social sciences. Students, especially those who have an interest in doing applied work, should be exposed to it."—Yang Cao, Department of Sociology, University of North Carolina Charlotte