Guide to Intelligent Data Analysis

Klappentext:

This book provides a systematic overview and classification of tasks in data analysis, methods to solve them and typical problems encountered.

Different views from classical and non-classical statistics like Bayesian inference and robust statistics, exploratory data analysis, data mining and machine learning are combined together to provide a better understanding of the methods, their potentials and limitations.

Features: Focuses on validation and pitfalls related to real world applications of these techniques Presents different approaches, analysing their advantages and disadvantages for certain types of tasks including exploratory data analysis, data mining, classical statistics and robust statistics Contains case studies and examples to enhance understanding A supplementary website provides numerous hands-on examples

This collective view of data analysis problems and methods, their potentials and limitations is an indispensable learning tool for graduate and advanced undergraduate students.

Inhaltsverzeichnis:

Introduction.- Practical Data Analysis: An Overview.- What is Intelligent Data Analysis?.- Business Understanding.- Data Understanding.- Data Preparation.- Objectives, Algorithms and Validation.- Finding Structures in Data.- Finding Explanations.- Finding Predictors.- Advanced Topics.- Appendix: Statistical Background

Rezension:

From the reviews: 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)

Themen:

• Datenanalyse

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