Guide to Intelligent Data Analysis: How to Intelligently Make Sense of Real Data (Texts in Computer Science) - Tapa blanda

Críticas

From the reviews:

“The authors, leading scholars in the field based in Germany and Spain, seek to offer a hands-on instructional approach to basic data analysis techniques and consider their use in solving problems. The reader is taken through the process, following the interlinked steps of project understanding, data understanding, data preparation, modelling, and deployment and monitoring. The text reviews the basics of classical statistics that support and justify many data analysis methods, and includes a glossary of statistical terms.” (Times Higher Education, 26 May 2011)

“The clear and complete exposition of arguments, along with the attention to formalization and the balanced number of bibliographic references, make this book a bright introduction to intelligent data analysis. It is an excellent choice for graduate or advanced undergraduate courses, as well as for researchers and professionals who want get acquainted with this field of study. ... Overall, the authors hit their target producing a textbook that aids in understanding the basic processes, methods, and issues for intelligent data analysis.” (Corrado Mencar, ACM Computing Reviews, April, 2011)

“The book provides a thorough introduction to data mining that covers theoretical background as well as the use of tools (KNIME and R). The book is intended as a textbook for a broad audience from graduate and advanced undergraduate students to professional data analysts. ... each chapter ends with a list of references to identify relevant research. Hence, I recommend this book as an introductory text on data analysis for the intended target audience.” (Gottfried Vossen, Zentralblatt MATH, Vol. 1210, 2011)

Reseña del editor

This is a systematic overview and classification of tasks in data analysis, methods to solve them and typical problems encountered. The book combines views from classical and non-classical statistics like Bayesian inference and robust statistics.