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18183cam a2200469 i 4500 |
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20250812110247.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION |
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250812t20222022njum ob u001 0 eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781119674689 |
| Qualifying information |
hardcover |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781119674757 |
| Qualifying information |
electronic book |
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1119674751 |
| Qualifying information |
electronic book |
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| International Standard Book Number |
1119674700 |
| Qualifying information |
electronic book |
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| International Standard Book Number |
9781119674733 |
| Qualifying information |
electronic book |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
1119674735 |
| Qualifying information |
electronic book |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781119674702 |
| Qualifying information |
(electronic bk.) |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(OCoLC)1268543819 |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
eng |
| 042 ## - AUTHENTICATION CODE |
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| Authentication code |
pcc |
| 050 04 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA76.9.B45 |
| Item number |
M36 2022 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
005.7 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Preferred name for the person |
Mariani, Maria C., |
| Authority record control number |
http://id.loc.gov/authorities/names/n2011065874 |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Data science in theory and practice : |
| Remainder of title |
techniques for big data analytics and complex data sets / |
| Statement of responsibility, etc |
Maria Cristina Mariani, Osei Kofi Tweneboah, Maria Pia Beccar-Varela. |
| 264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Hoboken, NJ : |
| Name of publisher, distributor, etc |
John Wiley & Sons, Inc., |
| Date of publication, distribution, etc |
2022. |
| 264 #4 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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©2022. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
1 online resource (xxiv, 370 pages) : |
| Other physical details |
illustrations (some color) |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
| Content type code |
txt |
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rdacontent. |
| 337 ## - MEDIA TYPE |
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computer |
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c |
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rdamedia. |
| 338 ## - CARRIER TYPE |
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online resource |
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cr |
| Source |
rdacarrier. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references and index. |
| 505 0# - CONTENTS |
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| Formatted contents note |
Table of Contents<br/>List of Figures xvii<br/><br/>List of Tables xxi<br/><br/>Preface xxiii<br/><br/>1 Background of Data Science 1<br/><br/>1.1 Introduction 1<br/><br/>1.2 Origin of Data Science 2<br/><br/>1.3 Who is a Data Scientist? 2<br/><br/>1.4 Big Data 3<br/><br/>1.4.1 Characteristics of Big Data 4<br/><br/>1.4.2 Big Data Architectures 4<br/><br/>2 Matrix Algebra and Random Vectors 7<br/><br/>2.1 Introduction 7<br/><br/>2.2 Some Basics of Matrix Algebra 7<br/><br/>2.2.1 Vectors 7<br/><br/>2.2.2 Matrices 8<br/><br/>2.3 Random Variables and Distribution Functions 12<br/><br/>2.3.1 The Dirichlet Distribution 15<br/><br/>2.3.2 Multinomial Distribution 17<br/><br/>2.3.3 Multivariate Normal Distribution 18<br/><br/>2.4 Problems 19<br/><br/>3 Multivariate Analysis 21<br/><br/>3.1 Introduction 21<br/><br/>3.2 Multivariate Analysis: Overview 21<br/><br/>3.3 Mean Vectors 22<br/><br/>3.4 Variance–Covariance Matrices 24<br/><br/>3.5 Correlation Matrices 26<br/><br/>3.6 Linear Combinations of Variables 28<br/><br/>3.6.1 Linear Combinations of Sample Means 29<br/><br/>3.6.2 Linear Combinations of Sample Variance and Covariance 29<br/><br/>3.6.3 Linear Combinations of Sample Correlation 30<br/><br/>3.7 Problems 31<br/><br/>4 Time Series Forecasting 35<br/><br/>4.1 Introduction 35<br/><br/>4.2 Terminologies 36<br/><br/>4.3 Components of Time Series 39<br/><br/>4.3.1 Seasonal 39<br/><br/>4.3.2 Trend 40<br/><br/>4.3.3 Cyclical 41<br/><br/>4.3.4 Random 42<br/><br/>4.4 Transformations to Achieve Stationarity 42<br/><br/>4.5 Elimination of Seasonality via Differencing 44<br/><br/>4.6 Additive and Multiplicative Models 44<br/><br/>4.7 Measuring Accuracy of Different Time Series Techniques 45<br/><br/>4.7.1 Mean Absolute Deviation 46<br/><br/>4.7.2 Mean Absolute Percent Error 46<br/><br/>4.7.3 Mean Square Error 47<br/><br/>4.7.4 Root Mean Square Error 48<br/><br/>4.8 Averaging and Exponential Smoothing Forecasting Methods 48<br/><br/>4.8.1 Averaging Methods 49<br/><br/>4.8.1.1 Simple Moving Averages 49<br/><br/>4.8.1.2 Weighted Moving Averages 51<br/><br/>4.8.2 Exponential Smoothing Methods 54<br/><br/>4.8.2.1 Simple Exponential Smoothing 54<br/><br/>4.8.2.2 Adjusted Exponential Smoothing 55<br/><br/>4.9 Problems 57<br/><br/>5 Introduction to R 61<br/><br/>5.1 Introduction 61<br/><br/>5.2 Basic Data Types 62<br/><br/>5.2.1 Numeric Data Type 62<br/><br/>5.2.2 Integer Data Type 62<br/><br/>5.2.3 Character 63<br/><br/>5.2.4 Complex Data Types 63<br/><br/>5.2.5 Logical Data Types 64<br/><br/>5.3 Simple Manipulations – Numbers and Vectors 64<br/><br/>5.3.1 Vectors and Assignment 64<br/><br/>5.3.2 Vector Arithmetic 65<br/><br/>5.3.3 Vector Index 66<br/><br/>5.3.4 Logical Vectors 67<br/><br/>5.3.5 Missing Values 68<br/><br/>5.3.6 Index Vectors 69<br/><br/>5.3.6.1 Indexing with Logicals 69<br/><br/>5.3.6.2 A Vector of Positive Integral Quantities 69<br/><br/>5.3.6.3 A Vector of Negative Integral Quantities 69<br/><br/>5.3.6.4 Named Indexing 70<br/><br/>5.3.7 Other Types of Objects 70<br/><br/>5.3.7.1 Matrices 70<br/><br/>5.3.7.2 List 72<br/><br/>5.3.7.3 Factor 73<br/><br/>5.3.7.4 Data Frames 75<br/><br/>5.3.8 Data Import 76<br/><br/>5.3.8.1 Excel File 76<br/><br/>5.3.8.2 CSV File 76<br/><br/>5.3.8.3 Table File 77<br/><br/>5.3.8.4 Minitab File 77<br/><br/>5.3.8.5 SPSS File 77<br/><br/>5.4 Problems 78<br/><br/>6 Introduction to Python 81<br/><br/>6.1 Introduction 81<br/><br/>6.2 Basic Data Types 82<br/><br/>6.2.1 Number Data Type 82<br/><br/>6.2.1.1 Integer 82<br/><br/>6.2.1.2 Floating-Point Numbers 83<br/><br/>6.2.1.3 Complex Numbers 84<br/><br/>6.2.2 Strings 84<br/><br/>6.2.3 Lists 85<br/><br/>6.2.4 Tuples 86<br/><br/>6.2.5 Dictionaries 86<br/><br/>6.3 Number Type Conversion 87<br/><br/>6.4 Python Conditions 87<br/><br/>6.4.1 If Statements 88<br/><br/>6.4.2 The Else and Elif Clauses 89<br/><br/>6.4.3 The While Loop 90<br/><br/>6.4.3.1 The Break Statement 91<br/><br/>6.4.3.2 The Continue Statement 91<br/><br/>6.4.4 For Loops 91<br/><br/>6.4.4.1 Nested Loops 92<br/><br/>6.5 Python File Handling: Open, Read, and Close 93<br/><br/>6.6 Python Functions 93<br/><br/>6.6.1 Calling a Function in Python 94<br/><br/>6.6.2 Scope and Lifetime of Variables 94<br/><br/>6.7 Problems 95<br/><br/>7 Algorithms 97<br/><br/>7.1 Introduction 97<br/><br/>7.2 Algorithm – Definition 97<br/><br/>7.3 How toWrite an Algorithm 98<br/><br/>7.3.1 Algorithm Analysis 99<br/><br/>7.3.2 Algorithm Complexity 99<br/><br/>7.3.3 Space Complexity 100<br/><br/>7.3.4 Time Complexity 100<br/><br/>7.4 Asymptotic Analysis of an Algorithm 101<br/><br/>7.4.1 Asymptotic Notations 102<br/><br/>7.4.1.1 Big O Notation 102<br/><br/>7.4.1.2 The Omega Notation, Ω 102<br/><br/>7.4.1.3 The Θ Notation 102<br/><br/>7.5 Examples of Algorithms 104<br/><br/>7.6 Flowchart 104<br/><br/>7.7 Problems 105<br/><br/>8 Data Preprocessing and Data Validations 109<br/><br/>8.1 Introduction 109<br/><br/>8.2 Definition – Data Preprocessing 109<br/><br/>8.3 Data Cleaning 110<br/><br/>8.3.1 Handle Missing Data 110<br/><br/>8.3.2 Types of Missing Data 110<br/><br/>8.3.2.1 Missing Completely at Random 110<br/><br/>8.3.2.2 Missing at Random 110<br/><br/>8.3.2.3 Missing Not at Random 111<br/><br/>8.3.3 Techniques for Handling the Missing Data 111<br/><br/>8.3.3.1 Listwise Deletion 111<br/><br/>8.3.3.2 Pairwise Deletion 111<br/><br/>8.3.3.3 Mean Substitution 112<br/><br/>8.3.3.4 Regression Imputation 112<br/><br/>8.3.3.5 Multiple Imputation 112<br/><br/>8.3.4 Identify Outliers and Noisy Data 113<br/><br/>8.3.4.1 Binning 113<br/><br/>8.3.4.2 Box Plot 113<br/><br/>8.4 Data Transformations 115<br/><br/>8.4.1 Min–Max Normalization 115<br/><br/>8.4.2 Z-score Normalization 115<br/><br/>8.5 Data Reduction 116<br/><br/>8.6 Data Validations 117<br/><br/>8.6.1 Methods for Data Validation 117<br/><br/>8.6.1.1 Simple Statistical Criterion 117<br/><br/>8.6.1.2 Fourier Series Modeling and SSC 118<br/><br/>8.6.1.3 Principal Component Analysis and SSC 118<br/><br/>8.7 Problems 119<br/><br/>9 Data Visualizations 121<br/><br/>9.1 Introduction 121<br/><br/>9.2 Definition – Data Visualization 121<br/><br/>9.2.1 Scientific Visualization 123<br/><br/>9.2.2 Information Visualization 123<br/><br/>9.2.3 Visual Analytics 124<br/><br/>9.3 Data Visualization Techniques 126<br/><br/>9.3.1 Time Series Data 126<br/><br/>9.3.2 Statistical Distributions 127<br/><br/>9.3.2.1 Stem-and-Leaf Plots 127<br/><br/>9.3.2.2 Q–Q Plots 127<br/><br/>9.4 Data Visualization Tools 129<br/><br/>9.4.1 Tableau 129<br/><br/>9.4.2 Infogram 130<br/><br/>9.4.3 Google Charts 132<br/><br/>9.5 Problems 133<br/><br/>10 Binomial and Trinomial Trees 135<br/><br/>10.1 Introduction 135<br/><br/>10.2 The Binomial Tree Method 135<br/><br/>10.2.1 One Step Binomial Tree 136<br/><br/>10.2.2 Using the Tree to Price a European Option 139<br/><br/>10.2.3 Using the Tree to Price an American Option 140<br/><br/>10.2.4 Using the Tree to Price Any Path Dependent Option 141<br/><br/>10.3 Binomial Discrete Model 141<br/><br/>10.3.1 One-Step Method 141<br/><br/>10.3.2 Multi-step Method 144<br/><br/>10.3.2.1 Example: European Call Option 145<br/><br/>10.4 Trinomial Tree Method 146<br/><br/>10.4.1 What is the Meaning of Little o and Big O? 147<br/><br/>10.5 Problems 147<br/><br/>11 Principal Component Analysis 151<br/><br/>11.1 Introduction 151<br/><br/>11.2 Background of Principal Component Analysis 151<br/><br/>11.3 Motivation 152<br/><br/>11.3.1 Correlation and Redundancy 152<br/><br/>11.3.2 Visualization 153<br/><br/>11.4 The Mathematics of PCA 153<br/><br/>11.4.1 The Eigenvalues and Eigenvectors 156<br/><br/>11.5 How PCAWorks 159<br/><br/>11.5.1 Algorithm 160<br/><br/>11.6 Application 161<br/><br/>11.7 Problems 162<br/><br/>12 Discriminant and Cluster Analysis 165<br/><br/>12.1 Introduction 165<br/><br/>12.2 Distance 165<br/><br/>12.3 Discriminant Analysis 166<br/><br/>12.3.1 Kullback–Leibler Divergence 167<br/><br/>12.3.2 Chernoff Distance 167<br/><br/>12.3.3 Application – Seismic Time Series 169<br/><br/>12.3.4 Application – Financial Time Series 171<br/><br/>12.3.4.1 Background of Data and Analysis 171<br/><br/>12.4 Cluster Analysis 173<br/><br/>12.4.1 Partitioning Algorithms 174<br/><br/>12.4.2 k-Means Algorithm 174<br/><br/>12.4.3 k-Medoids Algorithm 175<br/><br/>12.4.4 Application – Seismic Time Series 176<br/><br/>12.4.5 Application – Financial Time Series 176<br/><br/>12.5 Problems 177<br/><br/>13 Multidimensional Scaling 179<br/><br/>13.1 Introduction 179<br/><br/>13.2 Motivation 180<br/><br/>13.3 Number of Dimensions and Goodness of Fit 182<br/><br/>13.4 Proximity Measures 183<br/><br/>13.5 Metric Multidimensional Scaling 183<br/><br/>13.5.1 The Classical Solution 184<br/><br/>13.6 Nonmetric Multidimensional Scaling 186<br/><br/>13.6.1 Shepard–Kruskal Algorithm 186<br/><br/>13.7 Problem 187<br/><br/>14 Classification and Tree-Based Methods 191<br/><br/>14.1 Introduction 191<br/><br/>14.2 An Overview of Classification 191<br/><br/>14.2.1 The Classification Problem 192<br/><br/>14.2.2 Logistic Regression Model 192<br/><br/>14.2.2.1 l1 Regularization 193<br/><br/>14.2.2.2 l2 Regularization 194<br/><br/>14.3 Linear Discriminant Analysis 194<br/><br/>14.3.1 Optimal Classification and Estimation of Gaussian Distribution 195<br/><br/>14.4 Tree-Based Methods 197<br/><br/>14.4.1 One Single Decision Tree 197<br/><br/>14.4.2 Random Forest 198<br/><br/>14.5 Applications 200<br/><br/>14.6 Problems 202<br/><br/>15 Association Rules 205<br/><br/>15.1 Introduction 205<br/><br/>15.2 Market Basket Analysis 205<br/><br/>15.3 Terminologies 207<br/><br/>15.3.1 Itemset and Support Count 207<br/><br/>15.3.2 Frequent Itemset 207<br/><br/>15.3.3 Closed Frequent Itemset 207<br/><br/>15.3.4 Maximal Frequent Itemset 207<br/><br/>15.3.5 Association Rule 208<br/><br/>15.3.6 Rule Evaluation Metrics 208<br/><br/>15.4 The Apriori Algorithm 209<br/><br/>15.4.1 An example of the Apriori Algorithm 211<br/><br/>15.5 Applications 213<br/><br/>15.5.1 Confidence 214<br/><br/>15.5.2 Lift 215<br/><br/>15.5.3 Conviction 215<br/><br/>15.6 Problems 215<br/><br/>16 Support Vector Machines 219<br/><br/>16.1 Introduction 219<br/><br/>16.2 The Maximal Margin Classifier 219<br/><br/>16.3 Classification Using a Separating Hyperplane 223<br/><br/>16.4 Kernel Functions 225<br/><br/>16.5 Applications 225<br/><br/>16.6 Problems 227<br/><br/>17 Neural Networks 231<br/><br/>17.1 Introduction 231<br/><br/>17.2 Perceptrons 231<br/><br/>17.3 Feed Forward Neural Network 231<br/><br/>17.4 Recurrent Neural Networks 233<br/><br/>17.5 Long Short-Term Memory 234<br/><br/>17.5.1 Residual Connections 235<br/><br/>17.5.2 Loss Functions 236<br/><br/>17.5.3 Stochastic Gradient Descent 236<br/><br/>17.5.4 Regularization – Ensemble Learning 237<br/><br/>17.6 Application 237<br/><br/>17.6.1 Emergent and Developed Market 237<br/><br/>17.6.2 The Lehman Brothers Collapse 237<br/><br/>17.6.3 Methodology 238<br/><br/>17.6.4 Analyses of Data 238<br/><br/>17.6.4.1 Results of the Emergent Market Index 238<br/><br/>17.6.4.2 Results of the Developed Market Index 238<br/><br/>17.7 Significance of Study 239<br/><br/>17.8 Problems 240<br/><br/>18 Fourier Analysis 245<br/><br/>18.1 Introduction 245<br/><br/>18.2 Definition 245<br/><br/>18.3 Discrete Fourier Transform 246<br/><br/>18.4 The Fast Fourier Transform (FFT) Method 247<br/><br/>18.5 Dynamic Fourier Analysis 250<br/><br/>18.5.1 Tapering 251<br/><br/>18.5.2 Daniell Kernel Estimation 252<br/><br/>18.6 Applications of the Fourier Transform 253<br/><br/>18.6.1 Modeling Power Spectrum of Financial Returns Using Fourier<br/><br/>Transforms 253<br/><br/>18.6.1.1 Background of Data 255<br/><br/>18.6.2 Application of Dynamic Fourier Analysis 255<br/><br/>18.6.3 Image Compression 259<br/><br/>18.7 Problems 259<br/><br/>19 Wavelets Analysis 261<br/><br/>19.1 Introduction 261<br/><br/>19.1.1 Wavelets Transform 262<br/><br/>19.2 DiscreteWavelets Transforms (DWT) 264<br/><br/>19.2.1 HaarWavelets 265<br/><br/>19.2.1.1 Haar Functions 265<br/><br/>19.2.1.2 Haar Transform Matrix 266<br/><br/>19.2.2 DaubechiesWavelets 267<br/><br/>19.3 Applications of theWavelets Transform 268<br/><br/>19.3.1 Discriminating Between Mining Explosions and Cluster of<br/><br/>Earthquakes 269<br/><br/>19.3.1.1 Background of Data 269<br/><br/>19.3.1.2 Results 269<br/><br/>19.3.2 Finance 271<br/><br/>19.3.3 Damage Detection in Frame Structures 275<br/><br/>19.3.4 Image Compression 275<br/><br/>19.3.5 Seismic Signals 275<br/><br/>19.4 Problems 276<br/><br/>20 Stochastic Analysis 279<br/><br/>20.1 Introduction 279<br/><br/>20.2 Necessary Definitions from Probability Theory 279<br/><br/>20.3 Stochastic Processes 280<br/><br/>20.3.1 The Index Set 281<br/><br/>20.3.2 The State Space 281<br/><br/>20.3.3 Stationary and Independent Components 281<br/><br/>20.3.4 Stationary and Independent Increments 282<br/><br/>20.3.5 Filtration and Standard Filtration 283<br/><br/>20.4 Examples of Stochastic Processes 284<br/><br/>20.4.1 Markov Chains 284<br/><br/>20.4.1.1 Examples of Markov Processes 286<br/><br/>20.4.1.2 The Chapman–Kolmogorov Equation 287<br/><br/>20.4.1.3 Classification of States 288<br/><br/>20.4.1.4 Limiting Probabilities 289<br/><br/>20.4.1.5 Branching Processes 291<br/><br/>20.4.1.6 Time Homogeneous Chains 293<br/><br/>20.4.2 Martingales 293<br/><br/>20.4.3 Simple RandomWalk 294<br/><br/>20.4.4 The Brownian Motion (Wiener Process) 294<br/><br/>20.5 Measurable Functions and Expectations 294<br/><br/>20.5.1 Radon–Nikodym Theorem and Conditional Expectation 296<br/><br/>20.6 Problems 299<br/><br/>21 Fractal Analysis – Lévy, Hurst, DFA, DEA 301<br/><br/>21.1 Introduction and Definitions 301<br/><br/>21.2 Lévy Processes 301<br/><br/>21.2.1 Examples of Lévy Processes 304<br/><br/>21.2.1.1 The Poisson Process (Jumps) 305<br/><br/>21.2.1.2 The Compound Poisson Process 305<br/><br/>21.2.1.3 Inverse Gaussian (IG) Process 306<br/><br/>21.2.1.4 The Gamma Process 307<br/><br/>21.2.2 Exponential Lévy Models 307<br/><br/>21.2.3 Subordination of Lévy Processes 308<br/><br/>21.2.4 Stable Distributions 309<br/><br/>21.3 Lévy Flight Models 311<br/><br/>21.4 Rescaled Range Analysis (Hurst Analysis) 312<br/><br/>21.5 Detrended Fluctuation Analysis (DFA) 315<br/><br/>21.6 Diffusion Entropy Analysis (DEA) 316<br/><br/>21.6.1 Estimation Procedure 317<br/><br/>21.6.1.1 The Shannon Entropy 317<br/><br/>21.6.2 The H–𝛼 Relationship for the Truncated Lévy Flight 319<br/><br/>21.7 Application – Characterization of Volcanic Time Series 321<br/><br/>21.7.1 Background of Volcanic Data 321<br/><br/>21.7.2 Results 321<br/><br/>21.8 Problems 323<br/><br/>22 Stochastic Differential Equations 325<br/><br/>22.1 Introduction 325<br/><br/>22.2 Stochastic Differential Equations (SDEs) 325<br/><br/>22.2.1 Solution Methods of SDEs 326<br/><br/>22.3 Examples 335<br/><br/>22.3.1 Modeling Asset Prices 335<br/><br/>22.3.2 Modeling the Magnitude of Earthquake Series 336<br/><br/>22.4 Multidimensional Stochastic Differential Equations 337<br/><br/>22.4.1 The multidimensional Ornstein–Uhlenbeck Processes 337<br/><br/>22.4.2 Solution of the Ornstein–Uhlenbeck Process 338<br/><br/>22.5 Simulation of Stochastic Differential Equations 340<br/><br/>22.5.1 Euler–Maruyama Scheme for Approximating Stochastic Differential<br/><br/>Equations 340<br/><br/>22.5.2 Euler–Milstein Scheme for Approximating Stochastic Differential<br/><br/>Equations 341<br/><br/>22.6 Problems 343<br/><br/>23 Ethics: With Great Power Comes Great Responsibility 345<br/><br/>23.1 Introduction 345<br/><br/>23.2 Data Science Ethical Principles 346<br/><br/>23.2.1 Enhance Value in Society 346<br/><br/>23.2.2 Avoiding Harm 346<br/><br/>23.2.3 Professional Competence 347<br/><br/>23.2.4 Increasing Trustworthiness 348<br/><br/>23.2.5 Maintaining Accountability and Oversight 348<br/><br/>23.3 Data Science Code of Professional Conduct 348<br/><br/>23.4 Application 350<br/><br/>23.4.1 Project Planning 350<br/><br/>23.4.2 Data Preprocessing 350<br/><br/>23.4.3 Data Management 350<br/><br/>23.4.4 Analysis and Development 351<br/><br/>23.5 Problems 351<br/><br/>Bibliography 353<br/><br/>Index 359 |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
"This textbook is dedicated to practitioners, graduate, and advanced undergraduate students who have interest in Data Science, Business analytics, and Statistical and Mathematical Modeling in different disciplines such as Finance, Geophysics, and Engineering. This book is designed to serve as a textbook for several courses in the aforementioned areas and a reference guide for practitioners in the industry. The book has a strong theoretical background and several applications to specific practical problems. It contains numerous techniques applicable to modern data science and other disciplines. In today's world, many fields are confronted with increasingly large amounts of complex data. Financial, healthcare, and geophysical data sampled with high frequency is no exception. These staggering amounts of data pose special challenges to the world of finance and other disciplines such as healthcare and geophysics, as traditional models and information technology tools can be poorly suited to grapple with their size and complexity. Probabilistic modeling, mathematical modeling, and statistical data analysis attempt to discover order from apparent disorder; this textbook may serve as a guide to various new systematic approaches on how to implement these quantitative activities with complex data sets."-- |
| Assigning source |
Provided by publisher. |
| 545 0# - BIOGRAPHICAL OR HISTORICAL DATA |
|---|
| Biographical or historical note |
About the Authors<br/>MARIA CRISTINA MARIANI, PHD, is Shigeko K. Chan Distinguished Professor and Chair in the Department of Mathematical Sciences at The University of Texas at El Paso. She currently focuses her research on Stochastic Analysis, Differential Equations and Machine Learning with applications to Big Data and Complex Data sets arising in Public Health, Geophysics, Finance and others. Dr. Mariani is co-author of other Wiley books including Quantitative Finance.<br/><br/>OSEI KOFI TWENEBOAH, PHD, is Assistant Professor of Data Science at Ramapo College of New Jersey. His main research is Stochastic Analysis, Machine Learning and Scientific Computing with applications to Finance, Health Sciences, and Geophysics.<br/><br/>MARIA PIA BECCAR-VARELA, PHD, is Associate Professor of Instruction in the Department of Mathematical Sciences at the University of Texas at El Paso. Her research interests include Differential Equations, Stochastic Differential Equations, Wavelet Analysis and Discriminant Analysis applied to Finance, Health Sciences, and Earthquake Studies. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Big data. |
| Authority record control number |
http://id.loc.gov/authorities/subjects/sh2012003227. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data mining. |
| Authority record control number |
http://id.loc.gov/authorities/subjects/sh97002073. |
| 655 #4 - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
Electronic books. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Tweneboah, Osei Kofi, |
| Dates associated with a name |
1988- |
| Authority record control number |
http://id.loc.gov/authorities/names/no2016067506 |
| Relator term |
author. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Beccar-Varela, Maria Pia, |
| Authority record control number |
http://id.loc.gov/authorities/names/no2021106674 |
| Relator term |
author. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
https://onlinelibrary.wiley.com/doi/book/10.1002/9781119674757 |
| Link text |
Full text is available at Wiley Online Library Click here to view |
| 942 ## - ADDED ENTRY ELEMENTS |
|---|
| Source of classification or shelving scheme |
|
| Item type |
EBOOK |