Incluye referencias bibliográficas e índice

Introduction -- Motivation -- The analysis process -- Methods, tasks, and tools -- How to read this book -- Practical data analysis : an example -- The setup -- Data understanding and pattern finding -- Explanation finding -- Predicting the future -- Concluding remarks -- Project understanding -- Determine the project objective -- Assess the situation -- Determine analysis goals -- Further reading -- Data understanding -- Attribute understanding -- Data quality -- Data visualization -- Correlation analysis -- Outlier detection -- Missing values -- A checklist for data understanding -- Data understanding in practice -- Principles of modeling -- Model classes -- Fitting criteria and score functions -- Algorithms for model fitting -- Types of errors -- Model validation -- Model errors and validation in practice -- Further reading -- Data preparation -- Select data -- Clean data -- Construct data -- Complex data types -- Data integration -- Data preparation in practice -- Finding patterns -- Hierarchical clustering -- Notion of (Dis-) Similarity -- Prototype- and model-based clustering -- Density-based clustering -- Self-organizing maps -- Frequent pattern mining and association rules -- Deviation analysis -- Finding patterns in practice -- Further reading -- Finding explanations -- Decision trees -- Bayes classifiers -- Regression -- Rule learning -- Finding explanations in practice -- Further reading -- Finding predictors -- Nearest-neighbor predictors -- Artifical neural networks -- Support vector machines -- Ensemble methods -- Finding predictors in practice -- Evaluation and deployment -- Evaluation -- Deployment and monitoring -- Statistics -- Terms and notation -- Descriptive statistics -- Probability theory -- Inferential statistics -- The R project -- KNIME

PIT

NUEVOSTELEMAT