Guide to Intelligent Data Analysis: How to Intelligently Make Sense of Real Data

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.