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Analytics in a big data world : the essential guide to data science and its applications / / Bart Baesens
| Analytics in a big data world : the essential guide to data science and its applications / / Bart Baesens |
| Autore | Baesens Bart |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (xv, 232 pages) : illustrations |
| Disciplina | 658.4/038 |
| Collana | Wiley & SAS Business Series |
| Soggetto topico |
Big data
Management - Statistical methods Management - Data processing Decision making - Data processing |
| ISBN |
1-118-89274-7
1-118-89271-2 1-119-20418-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Big data and analytics -- 2. Data collection, sampling, and preprocessing -- 3. Predictive analytics -- 4. Descriptive analytics -- 5. Survival analysis -- 6. Social network analytics -- 7. Analytics : putting it all to work -- 8. Example applications. |
| Record Nr. | UNINA-9910141723703321 |
Baesens Bart
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| Hoboken, New Jersey : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Analytics in a big data world : the essential guide to data science and its applications / / Bart Baesens
| Analytics in a big data world : the essential guide to data science and its applications / / Bart Baesens |
| Autore | Baesens Bart |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (xv, 232 pages) : illustrations |
| Disciplina | 658.4/038 |
| Collana | Wiley & SAS Business Series |
| Soggetto topico |
Big data
Management - Statistical methods Management - Data processing Decision making - Data processing |
| ISBN |
1-118-89274-7
1-118-89271-2 1-119-20418-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Big data and analytics -- 2. Data collection, sampling, and preprocessing -- 3. Predictive analytics -- 4. Descriptive analytics -- 5. Survival analysis -- 6. Social network analytics -- 7. Analytics : putting it all to work -- 8. Example applications. |
| Record Nr. | UNINA-9910813918803321 |
|
Baesens Bart
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| Hoboken, New Jersey : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Beginning Java programming : the object-oriented approach / / Bart Baesens, Aimée Backiel, Seppe vanden Broucke
| Beginning Java programming : the object-oriented approach / / Bart Baesens, Aimée Backiel, Seppe vanden Broucke |
| Autore | Baesens Bart |
| Pubbl/distr/stampa | Indianapolis, [Indiana] : , : Wrox, , 2015 |
| Descrizione fisica | 1 online resource (669 p.) |
| Disciplina |
005.13
005.13/3 005.133 |
| Collana | Wrox Programmer to Programmer |
| Soggetto topico | Java (Computer program language) |
| ISBN |
1-118-73935-3
1-119-20941-2 1-118-73951-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Beginning Java® Programming; About the Authors; Credits; Acknowledgments; Contents; Introduction; Chapter 1: A General Introduction to Programming ; The Programming Process; Object-Oriented Programming: A Sneak Preview; Programming Errors; Syntax/Compilation Errors; Runtime Errors; Logic/Semantic Errors; Principles of Software Testing; Software Maintenance; Adaptive Maintenance; Perfective Maintenance; Corrective Maintenance; Preventive Maintenance; Principles of Structured Programming; Chapter 2: Getting to Know Java; A Short Java History; Features of Java; Looking Under the Hood
BytecodeJava Runtime Environment (JRE); Java Application Programming Interface (API); Class Loader; Bytecode Verifier; Java Virtual Machine (JVM); Java Platforms; Java Applications; Standalone Applications; Java Applets; Java Servlets; Java Beans; Java Language Structure; Classes; Identifiers; Java Keywords; Variables; Methods; Comments; Naming Conventions; Java Data Types; Primitive Data Types; Literals; Operators; Arithmetic Operators; Assignment Operators; Bitwise Operators; Logical Operators; Relational Operators; Arrays; Type Casting; Summary Chapter 3: Setting Up Your Development EnvironmentIntegrated Development Environments; Coding in Text Editors; Choosing an IDE; Eclipse; NetBeans; IntelliJ IDEA; Continuing with One IDE; Installing Eclipse on Your Computer; Downloading and Installing Eclipse; Using Eclipse; Chapter 4: Moving Toward Object-Oriented Programming ; Basic Concepts of Object-Oriented Programming; Classes and Objects in Java; Defining Classes in Java; Creating Objects; Storing Data: Variables; Instance Variables; Class Variables; Final Variables; Variable Scope; Defining Behavior: Methods; Instance Methods Class MethodsConstructors; The Main Method; Method Argument Passing; Java SE Built-in Classes; Classes in the java.lang Package; Classes in the java.io and java.nio Packages; Classes in the java.math Package; Classes in the java.net, java.rmi, javax.rmi, and org.omg.CORBA Packages; Classes in the java.awt and javax.swing Packages; Classes in the java.util Package; Collections; Other Utility Classes; Other Classes and Custom Libraries; Chapter 5: Controlling the Flow of Your Program; Comparisons Using Operators and Methods; Comparing Primitive Data Types with Comparison Operators Comparing Composite Data Types with Comparison MethodsUnderstanding Language Control; Creating if-then Statements; Nesting if-then Statements; Creating for Loops; What Is an Enhanced for Loop?; Nesting for Loops; Creating while Loops; What Is a do while Loop?; Comparing for and while Loops; Creating Switches; Comparing Switches and if-then Statements; Reviewing Keywords for Control; Controlling with the return Keyword; Controlling with the break Keyword; Controlling with the continue Keyword; Specifying a Label for break or continue Control; Reviewing Control Structures Chapter 6: Handling Exceptions and Debugging |
| Record Nr. | UNINA-9910132254203321 |
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Baesens Bart
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| Indianapolis, [Indiana] : , : Wrox, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Credit risk analytics : measurement techniques, applications, and examples in SAS / / Bart Baesens, Daniel Rösch, Harald Scheule
| Credit risk analytics : measurement techniques, applications, and examples in SAS / / Bart Baesens, Daniel Rösch, Harald Scheule |
| Autore | Baesens Bart |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2016 |
| Descrizione fisica | 1 online resource (583 pages) |
| Disciplina | 332.10285/555 |
| Collana | Wiley & SAS Business Series |
| Soggetto topico |
Credit - Management - Data processing
Risk management - Data processing Bank loans - Data processing |
| ISBN |
1-119-44956-1
1-119-27834-1 1-119-27828-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910270943003321 |
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Baesens Bart
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| Hoboken, New Jersey : , : Wiley, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Credit risk analytics : measurement techniques, applications, and examples in SAS / / Bart Baesens, Daniel Rösch, Harald Scheule
| Credit risk analytics : measurement techniques, applications, and examples in SAS / / Bart Baesens, Daniel Rösch, Harald Scheule |
| Autore | Baesens Bart |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2016 |
| Descrizione fisica | 1 online resource (583 pages) |
| Disciplina | 332.10285/555 |
| Collana | Wiley & SAS Business Series |
| Soggetto topico |
Credit - Management - Data processing
Risk management - Data processing Bank loans - Data processing |
| ISBN |
1-119-44956-1
1-119-27834-1 1-119-27828-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910826919103321 |
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Baesens Bart
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| Hoboken, New Jersey : , : Wiley, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Fraud analytics using descriptive, predictive, and social network techniques : a guide to data science for fraud detection / / Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke
| Fraud analytics using descriptive, predictive, and social network techniques : a guide to data science for fraud detection / / Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke |
| Autore | Baesens Bart |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2015 |
| Descrizione fisica | 1 online resource (402 p.) |
| Disciplina | 364.16/3015195 |
| Collana | Wiley and SAS Business Series |
| Soggetto topico |
Fraud - Statistical methods
Fraud - Prevention Commercial crimes - Prevention |
| ISBN |
1-119-14683-6
1-119-14684-4 1-119-14682-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; List of Figures; Foreword; Preface; Acknowledgments; Chapter 1 Fraud: Detection, Prevention, and Analytics!; Introduction; Fraud!; Fraud Detection and Prevention; Big Data for Fraud Detection; Data-Driven Fraud Detection; Fraud-Detection Techniques; Fraud Cycle; The Fraud Analytics Process Model; Fraud Data Scientists; A Fraud Data Scientist Should Have Solid Quantitative Skills; A Fraud Data Scientist Should Be a Good Programmer; A Fraud Data Scientist Should Excel in Communication and Visualization Skills
A Fraud Data Scientist Should Have a Solid Business Understanding A Fraud Data Scientist Should Be Creative; A Scientific Perspective on Fraud; References; Chapter 2 Data Collection, Sampling, and Preprocessing; Introduction; Types of Data Sources; Merging Data Sources; Sampling; Types of Data Elements; Visual Data Exploration and Exploratory Statistical Analysis; Benford's Law; Descriptive Statistics; Missing Values; Outlier Detection and Treatment; Red Flags; Standardizing Data; Categorization; Weights of Evidence Coding; Variable Selection; Principal Components Analysis; RIDITs PRIDIT Analysis Segmentation; References; Chapter 3 Descriptive Analytics for Fraud Detection; Introduction; Graphical Outlier Detection Procedures; Statistical Outlier Detection Procedures; Break-Point Analysis; Peer-Group Analysis; Association Rule Analysis; Clustering; Introduction; Distance Metrics; Hierarchical Clustering; Example of Hierarchical Clustering Procedures; k-Means Clustering; Self-Organizing Maps; Clustering with Constraints; Evaluating and Interpreting Clustering Solutions; One-Class SVMs; References; Chapter 4 Predictive Analytics for Fraud Detection; Introduction Target Definition Linear Regression; Logistic Regression; Basic Concepts; Logistic Regression Properties; Building a Logistic Regression Scorecard; Variable Selection for Linear and Logistic Regression; Decision Trees; Basic Concepts; Splitting Decision; Stopping Decision; Decision Tree Properties; Regression Trees; Using Decision Trees in Fraud Analytics; Neural Networks; Basic Concepts; Weight Learning; Opening the Neural Network Black Box; Support Vector Machines; Linear Programming; The Linear Separable Case; The Linear Nonseparable Case; The Nonlinear SVM Classifier; SVMs for Regression Opening the SVM Black Box Ensemble Methods; Bagging; Boosting; Random Forests; Evaluating Ensemble Methods; Multiclass Classification Techniques; Multiclass Logistic Regression; Multiclass Decision Trees; Multiclass Neural Networks; Multiclass Support Vector Machines; Evaluating Predictive Models; Splitting Up the Data Set; Performance Measures for Classification Models; Performance Measures for Regression Models; Other Performance Measures for Predictive Analytical Models; Developing Predictive Models for Skewed Data Sets; Varying the Sample Window; Undersampling and Oversampling Synthetic Minority Oversampling Technique (SMOTE) |
| Record Nr. | UNINA-9910131489503321 |
|
Baesens Bart
|
||
| Hoboken, New Jersey : , : Wiley, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Fraud analytics using descriptive, predictive, and social network techniques : a guide to data science for fraud detection / / Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke
| Fraud analytics using descriptive, predictive, and social network techniques : a guide to data science for fraud detection / / Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke |
| Autore | Baesens Bart |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2015 |
| Descrizione fisica | 1 online resource (402 p.) |
| Disciplina | 364.16/3015195 |
| Collana | Wiley and SAS Business Series |
| Soggetto topico |
Fraud - Statistical methods
Fraud - Prevention Commercial crimes - Prevention |
| ISBN |
1-119-14683-6
1-119-14684-4 1-119-14682-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; List of Figures; Foreword; Preface; Acknowledgments; Chapter 1 Fraud: Detection, Prevention, and Analytics!; Introduction; Fraud!; Fraud Detection and Prevention; Big Data for Fraud Detection; Data-Driven Fraud Detection; Fraud-Detection Techniques; Fraud Cycle; The Fraud Analytics Process Model; Fraud Data Scientists; A Fraud Data Scientist Should Have Solid Quantitative Skills; A Fraud Data Scientist Should Be a Good Programmer; A Fraud Data Scientist Should Excel in Communication and Visualization Skills
A Fraud Data Scientist Should Have a Solid Business Understanding A Fraud Data Scientist Should Be Creative; A Scientific Perspective on Fraud; References; Chapter 2 Data Collection, Sampling, and Preprocessing; Introduction; Types of Data Sources; Merging Data Sources; Sampling; Types of Data Elements; Visual Data Exploration and Exploratory Statistical Analysis; Benford's Law; Descriptive Statistics; Missing Values; Outlier Detection and Treatment; Red Flags; Standardizing Data; Categorization; Weights of Evidence Coding; Variable Selection; Principal Components Analysis; RIDITs PRIDIT Analysis Segmentation; References; Chapter 3 Descriptive Analytics for Fraud Detection; Introduction; Graphical Outlier Detection Procedures; Statistical Outlier Detection Procedures; Break-Point Analysis; Peer-Group Analysis; Association Rule Analysis; Clustering; Introduction; Distance Metrics; Hierarchical Clustering; Example of Hierarchical Clustering Procedures; k-Means Clustering; Self-Organizing Maps; Clustering with Constraints; Evaluating and Interpreting Clustering Solutions; One-Class SVMs; References; Chapter 4 Predictive Analytics for Fraud Detection; Introduction Target Definition Linear Regression; Logistic Regression; Basic Concepts; Logistic Regression Properties; Building a Logistic Regression Scorecard; Variable Selection for Linear and Logistic Regression; Decision Trees; Basic Concepts; Splitting Decision; Stopping Decision; Decision Tree Properties; Regression Trees; Using Decision Trees in Fraud Analytics; Neural Networks; Basic Concepts; Weight Learning; Opening the Neural Network Black Box; Support Vector Machines; Linear Programming; The Linear Separable Case; The Linear Nonseparable Case; The Nonlinear SVM Classifier; SVMs for Regression Opening the SVM Black Box Ensemble Methods; Bagging; Boosting; Random Forests; Evaluating Ensemble Methods; Multiclass Classification Techniques; Multiclass Logistic Regression; Multiclass Decision Trees; Multiclass Neural Networks; Multiclass Support Vector Machines; Evaluating Predictive Models; Splitting Up the Data Set; Performance Measures for Classification Models; Performance Measures for Regression Models; Other Performance Measures for Predictive Analytical Models; Developing Predictive Models for Skewed Data Sets; Varying the Sample Window; Undersampling and Oversampling Synthetic Minority Oversampling Technique (SMOTE) |
| Record Nr. | UNINA-9910824829403321 |
|
Baesens Bart
|
||
| Hoboken, New Jersey : , : Wiley, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||