Data Mining and Statistics for Decision Making, 2nd Edition

Data mining and data science are more and more widespread in companies and organizations seeking to extract relevant information from their databases, which they can use to explain and predict the phenomena that concern them (risks, consumption, loyalty).

Data Mining and Statistics for Decision Making: Second Edition is an overview of data mining, its theoretical foundations, its methods, tools and applications, from scoring to text mining, which is the subject of a completely revised chapter. Many of its tools belong to "classical" data analysis and statistics (factor analysis, clustering, discriminant analysis, logistic regression, generalized linear models, penalized regression, clusterwise regression...) but some are more specific to data mining, such as neural networks, genetic algorithms, SVMs, decision trees, random forests, boosting and association rule detection. The most recent advances of the learning machine and the most current Big Data applications are presented, going from image recognition algorithms to text mining word embedding methods. The chapters on neural networks and SVMs are illustrated by the example of handwriting recognition.

These tools are available in more and more powerful and complete software, starting with the open source software R, which we compare in detail with SAS and IBM SPSS software in a specific chapter. These tools are used to illustrate the theoretical explanations given with precise examples.

Methodological aspects range from project management to success factors and pitfalls to avoid, through evaluation, comparison of models, and their integration into operational processes. A chapter is devoted to a complete credit scoring case study, from exploratory data analysis to scorecard development.

Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.