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University of California Press
Knowledge Discovery in the Social Sciences by
Publication Date: 2020-02-04
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
Beneath the China Boom Labor, Citizenship, and the Making of a Rural Land Market by
Publication Date: 2020
Price: $ 29.95
For nearly four decades, China’s manufacturing boom has been powered by the labor of 287 million rural migrant workers, who travel seasonally between villages where they farm for subsistence and cities where they work. Yet recently local governments have moved away from manufacturing and toward urban expansion and construction as a development strategy. As a result, at least 88 million rural people to date have lost rights to village land. In Beneath the China Boom, Julia Chuang follows the trajectories of rural workers, who were once supported by a village welfare state and are now landless. This book provides a view of the undertow of China’s economic success, and the periodic crises—a rural fiscal crisis, a runaway urbanization—that it first created and now must resolve.
Princeton University Press
Publication Date: 2019-12-03
Price: $ 35.00
A leading political theorist's groundbreaking defense of ideal conceptions of justice in political philosophy Throughout the history of political philosophy and politics, there has been continual debate about the roles of idealism versus realism. For contemporary political philosophy, this debate manifests in notions of ideal theory versus nonideal theory. Nonideal thinkers shift their focus from theorizing about full social justice, asking instead which feasible institutional and political changes would make a society more just. Ideal thinkers, on the other hand, question whether full justice is a standard that any society is likely ever to satisfy. And, if social justice is unrealistic, are attempts to understand it without value or importance, and merely utopian? Utopophobia argues against thinking that justice must be realistic, or that understanding justice is only valuable if it can be realized. David Estlund does not offer a particular theory of justice, nor does he assert that justice is indeed unrealizable--only that it could be, and this possibility upsets common ways of proceeding in political thought. Estlund engages critically with important strands in traditional and contemporary political philosophy that assume a sound theory of justice has the overriding, defining task of contributing practical guidance toward greater social justice. Along the way, he counters several tempting perspectives, including the view that inquiry in political philosophy could have significant value only as a guide to practical political action, and that understanding true justice would necessarily have practical value, at least as an ideal arrangement to be approximated. Demonstrating that unrealistic standards of justice can be both sound and valuable to understand, Utopophobia stands as a trenchant defense of ideal theory in political philosophy.
Statistics, Data Mining, and Machine Learning in Astronomy by
Publication Date: 2019-12-03
Price: $ 85.00
Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope. Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest. An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date. Fully revised and expanded Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from astronomical surveys Uses a freely available Python codebase throughout Ideal for graduate students, advanced undergraduates, and working astronomers