Making sense of data : a practical guide to exploratory data analysis and data mining / / Glenn J. Myatt

• Record Nr.: UNINA9911019449203321
• Autore: Myatt Glenn J. <1969->
• Pubbl/distr/stampa: Hoboken, N.J., : Wiley-Interscience, c2007
• ISBN: 9786610721788; 9781280721786; 1280721782; 9780470101025; 0470101024; 9780470101018; 0470101016
• Edizione: [1st edition]
• Descrizione fisica: 1 online resource (294 p.)
• Disciplina: 006.312
• Soggetti: Data mining; Mathematical statistics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references (p. 273-274) and index.
• Nota di contenuto: Making Sense of Data; Contents; Preface; 1. Introduction; 1.1 Overview; 1.2 Problem definition; 1.3 Data preparation; 1.4 Implementation of the analysis; 1.5 Deployment of the results; 1.6 Book outline; 1.7 Summary; 1.8 Further reading; 2. Definition; 2.1 Overview; 2.2 Objectives; 2.3 Deliverables; 2.4 Roles and responsibilities; 2.5 Project plan; 2.6 Case study; 2.6.1 Overview; 2.6.2 Problem; 2.6.3 Deliverables; 2.6.4 Roles and responsibilities; 2.6.5 Current situation; 2.6.6 Timetable and budget; 2.6.7 Cost/benefit analysis; 2.7 Summary; 2.8 Further reading; 3. Preparation; 3.1 Overview; 3.2 Data sources3.3 Data understanding; 3.3.1 Data tables; 3.3.2 Continuous and discrete variables; 3.3.3 Scales of measurement; 3.3.4 Roles in analysis; 3.3.5 Frequency distribution; 3.4 Data preparation; 3.4.1 Overview; 3.4.2 Cleaning the data; 3.4.3 Removing variables; 3.4.4 Data transformations; 3.4.5 Segmentation; 3.5 Summary; 3.6 Exercises; 3.7 Further reading; 4. Tables and graphs; 4.1 Introduction; 4.2 Tables; 4.2.1 Data tables; 4.2.2 Contingency tables; 4.2.3 Summary ; tables; 4.3 Graphs; 4.3.1 Overview; 4.3.2 Frequency polygrams and histograms; 4.3.3 Scatterplots; 4.3.4 Box plots; 4.3.5 Multiple graphs4.4 Summary; 4.5 Exercises; 4.6 Further reading; 5. Statistics; 5.1 Overview; 5.2 Descriptive statistics; 5.2.1 Overview; 5.2.2 Central tendency; 5.2.3 Variation; 5.2.4 Shape; 5.2.5 Example; 5.3 Inferential statistics; 5.3.1 Overview; 5.3.2 Confidence intervals; 5.3.3 Hypothesis tests; 5.3.4 Chi-square; 5.3.5 One-way analysis of variance; 5.4 Comparative statistics; 5.4.1 Overview; 5.4.2 Visualizing relationships; 5.4.3 Correlation coefficient (r); 5.4.4 Correlation analysis for more than two variables; 5.5 Summary; 5.6 Exercises; 5.7 Further reading; 6. Grouping; 6.1 Introduction6.1.1 Overview; 6.1.2 Grouping by values or ranges; 6.1.3 Similarity measures; 6.1.4 Grouping approaches; 6.2 Clustering; 6.2.1 Overview; 6.2.2 Hierarchical agglomerative clustering; 6.2.3 K-means clustering; 6.3 Associative rules; 6.3.1 Overview; 6.3.2 Grouping by value combinations; 6.3.3 Extracting rules from groups; 6.3.4 Example; 6.4 Decision trees; 6.4.1 Overview; 6.4.2 Tree generation; 6.4.3 Splitting criteria; 6.4.4 Example; 6.5 Summary; 6.6 Exercises; 6.7 Further reading; 7. Prediction; 7.1 Introduction; 7.1.1 Overview; 7.1.2 Classification; 7.1.3 Regression; 7.1.4 Building a prediction model7.1.5 Applying a prediction model; 7.2 Simple regression models; 7.2.1 Overview; 7.2.2 Simple linear regression; 7.2.3 Simple nonlinear regression; 7.3 K-nearest neighbors; 7.3.1 Overview; 7.3.2 Learning; 7.3.3 Prediction; 7.4 Classification and regression trees; 7.4.1 Overview; 7.4.2 Predicting using decision trees; 7.4.3 Example; 7.5 Neural networks; 7.5.1 Overview; 7.5.2 Neural network layers; 7.5.3 Node calculations; 7.5.4 Neural network predictions; 7.5.5 Learning process; 7.5.6 Backpropagation; 7.5.7 Using neural networks; 7.5.8 Example; 7.6 Other methods
• Sommario/riassunto: A practical, step-by-step approach to making sense out of dataMaking Sense of Data educates readers on the steps and issues that need to be considered in order to successfully complete a data analysis or data mining project. The author provides clear explanations that guide the reader to make timely and accurate decisions from data in almost every field of study. A step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. With a comprehensive collection of methods from both data analysis and