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EXPLORE THE FOUNDATIONS OF DATA SCIENCE WITH THIS INSIGHTFUL NEW RESOURCE
Data Science in Theory and Practice delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling.
The book offers readers a multitude of topics all relevant to the analysis of complex data sets. Along with a robust exploration of the theory underpinning data science, it contains numerous applications to specific and practical problems. The book also provides examples of code algorithms in R and Python and provides pseudo-algorithms to port the code to any other language.
Ideal for students and practitioners without a strong background in data science, readers will also learn from topics like:
Perfect for advanced undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs, Data Science in Theory and Practice will also earn a place in the libraries of practicing data scientists, data and business analysts, and statisticians in the private sector, government, and academia.
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.
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.
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.
List of Figures xvii
List of Tables xxi
Preface xxiii
1 Background of Data Science 1
1.1 Introduction 1
1.2 Origin of Data Science 2
1.3 Who is a Data Scientist? 2
1.4 Big Data 3
1.4.1 Characteristics of Big Data 4
1.4.2 Big Data Architectures 4
2 Matrix Algebra and Random Vectors 7
2.1 Introduction 7
2.2 Some Basics of Matrix Algebra 7
2.2.1 Vectors 7
2.2.2 Matrices 8
2.3 Random Variables and Distribution Functions 12
2.3.1 The Dirichlet Distribution 15
2.3.2 Multinomial Distribution 17
2.3.3 Multivariate Normal Distribution 18
2.4 Problems 19
3 Multivariate Analysis 21
3.1 Introduction 21
3.2 Multivariate Analysis: Overview 21
3.3 Mean Vectors 22
3.4 Variance–Covariance Matrices 24
3.5 Correlation Matrices 26
3.6 Linear Combinations of Variables 28
3.6.1 Linear Combinations of Sample Means 29
3.6.2 Linear Combinations of Sample Variance and Covariance 29
3.6.3 Linear Combinations of Sample Correlation 30
3.7 Problems 31
4 Time Series Forecasting 35
4.1 Introduction 35
4.2 Terminologies 36
4.3 Components of Time Series 39
4.3.1 Seasonal 39
4.3.2 Trend 40
4.3.3 Cyclical 41
4.3.4 Random 42
4.4 Transformations to Achieve Stationarity 42
4.5 Elimination of Seasonality via Differencing 44
4.6 Additive and Multiplicative Models 44
4.7 Measuring Accuracy of Different Time Series Techniques 45
4.7.1 Mean Absolute Deviation 46
4.7.2 Mean Absolute Percent Error 46
4.7.3 Mean Square Error 47
4.7.4 Root Mean Square Error 48
4.8 Averaging and Exponential Smoothing Forecasting Methods 48
4.8.1 Averaging Methods 49
4.8.1.1 Simple Moving Averages 49
4.8.1.2 Weighted Moving Averages 51
4.8.2 Exponential Smoothing Methods 54
4.8.2.1 Simple Exponential Smoothing 54
4.8.2.2 Adjusted Exponential Smoothing 55
4.9 Problems 57
5 Introduction to R 61
5.1 Introduction 61
5.2 Basic Data Types 62
5.2.1 Numeric Data Type 62
5.2.2 Integer Data Type 62
5.2.3 Character 63
5.2.4 Complex Data Types 63
5.2.5 Logical Data Types 64
5.3 Simple Manipulations – Numbers and Vectors 64
5.3.1 Vectors and Assignment 64
5.3.2 Vector Arithmetic 65
5.3.3 Vector Index 66
5.3.4 Logical Vectors 67
5.3.5 Missing Values 68
5.3.6 Index Vectors 69
5.3.6.1 Indexing with Logicals 69
5.3.6.2 A Vector of Positive Integral Quantities 69
5.3.6.3 A Vector of Negative Integral Quantities 69
5.3.6.4 Named Indexing 70
5.3.7 Other Types of Objects 70
5.3.7.1 Matrices 70
5.3.7.2 List 72
5.3.7.3 Factor 73
5.3.7.4 Data Frames 75
5.3.8 Data Import 76
5.3.8.1 Excel File 76
5.3.8.2 CSV File 76
5.3.8.3 Table File 77
5.3.8.4 Minitab File 77
5.3.8.5 SPSS File 77
5.4 Problems 78
6 Introduction to Python 81
6.1 Introduction 81
6.2 Basic Data Types 82
6.2.1 Number Data Type 82
6.2.1.1 Integer 82
6.2.1.2 Floating-Point Numbers 83
6.2.1.3 Complex Numbers 84
6.2.2 Strings 84
6.2.3 Lists 85
6.2.4 Tuples 86
6.2.5 Dictionaries 86
6.3 Number Type Conversion 87
6.4 Python Conditions 87
6.4.1 If Statements 88
6.4.2 The Else and Elif Clauses 89
6.4.3 The While Loop 90
6.4.3.1 The Break Statement 91
6.4.3.2 The Continue Statement 91
6.4.4 For Loops 91
6.4.4.1 Nested Loops 92
6.5 Python File Handling: Open, Read, and Close 93
6.6 Python Functions 93
6.6.1 Calling a Function in Python 94
6.6.2 Scope and Lifetime of Variables 94
6.7 Problems 95
7 Algorithms 97
7.1 Introduction 97
7.2 Algorithm – Definition 97
7.3 How toWrite an Algorithm 98
7.3.1 Algorithm Analysis 99
7.3.2 Algorithm Complexity 99
7.3.3 Space Complexity 100
7.3.4 Time Complexity 100
7.4 Asymptotic Analysis of an Algorithm 101
7.4.1 Asymptotic Notations 102
7.4.1.1 Big O Notation 102
7.4.1.2 The Omega Notation, Ω 102
7.4.1.3 The Θ Notation 102
7.5 Examples of Algorithms 104
7.6 Flowchart 104
7.7 Problems 105
8 Data Preprocessing and Data Validations 109
8.1 Introduction 109
8.2 Definition – Data Preprocessing 109
8.3 Data Cleaning 110
8.3.1 Handle Missing Data 110
8.3.2 Types of Missing Data 110
8.3.2.1 Missing Completely at Random 110
8.3.2.2 Missing at Random 110
8.3.2.3 Missing Not at Random 111
8.3.3 Techniques for Handling the Missing Data 111
8.3.3.1 Listwise Deletion 111
8.3.3.2 Pairwise Deletion 111
8.3.3.3 Mean Substitution 112
8.3.3.4 Regression Imputation 112
8.3.3.5 Multiple Imputation 112
8.3.4 Identify Outliers and Noisy Data 113
8.3.4.1 Binning 113
8.3.4.2 Box Plot 113
8.4 Data Transformations 115
8.4.1 Min–Max Normalization 115
8.4.2 Z-score Normalization 115
8.5 Data Reduction 116
8.6 Data Validations 117
8.6.1 Methods for Data Validation 117
8.6.1.1 Simple Statistical Criterion 117
8.6.1.2 Fourier Series Modeling and SSC 118
8.6.1.3 Principal Component Analysis and SSC 118
8.7 Problems 119
9 Data Visualizations 121
9.1 Introduction 121
9.2 Definition – Data Visualization 121
9.2.1 Scientific Visualization 123
9.2.2 Information Visualization 123
9.2.3 Visual Analytics 124
9.3 Data Visualization Techniques 126
9.3.1 Time Series Data 126
9.3.2 Statistical Distributions 127
9.3.2.1 Stem-and-Leaf Plots 127
9.3.2.2 Q–Q Plots 127
9.4 Data Visualization Tools 129
9.4.1 Tableau 129
9.4.2 Infogram 130
9.4.3 Google Charts 132
9.5 Problems 133
10 Binomial and Trinomial Trees 135
10.1 Introduction 135
10.2 The Binomial Tree Method 135
10.2.1 One Step Binomial Tree 136
10.2.2 Using the Tree to Price a European Option 139
10.2.3 Using the Tree to Price an American Option 140
10.2.4 Using the Tree to Price Any Path Dependent Option 141
10.3 Binomial Discrete Model 141
10.3.1 One-Step Method 141
10.3.2 Multi-step Method 144
10.3.2.1 Example: European Call Option 145
10.4 Trinomial Tree Method 146
10.4.1 What is the Meaning of Little o and Big O? 147
10.5 Problems 147
11 Principal Component Analysis 151
11.1 Introduction 151
11.2 Background of Principal Component Analysis 151
11.3 Motivation 152
11.3.1 Correlation and Redundancy 152
11.3.2 Visualization 153
11.4 The Mathematics of PCA 153
11.4.1 The Eigenvalues and Eigenvectors 156
11.5 How PCAWorks 159
11.5.1 Algorithm 160
11.6 Application 161
11.7 Problems 162
12 Discriminant and Cluster Analysis 165
12.1 Introduction 165
12.2 Distance 165
12.3 Discriminant Analysis 166
12.3.1 Kullback–Leibler Divergence 167
12.3.2 Chernoff Distance 167
12.3.3 Application – Seismic Time Series 169
12.3.4 Application – Financial Time Series 171
12.3.4.1 Background of Data and Analysis 171
12.4 Cluster Analysis 173
12.4.1 Partitioning Algorithms 174
12.4.2 k-Means Algorithm 174
12.4.3 k-Medoids Algorithm 175
12.4.4 Application – Seismic Time Series 176
12.4.5 Application – Financial Time Series 176
12.5 Problems 177
13 Multidimensional Scaling 179
13.1 Introduction 179
13.2 Motivation 180
13.3 Number of Dimensions and Goodness of Fit 182
13.4 Proximity Measures 183
13.5 Metric Multidimensional Scaling 183
13.5.1 The Classical Solution 184
13.6 Nonmetric Multidimensional Scaling 186
13.6.1 Shepard–Kruskal Algorithm 186
13.7 Problem 187
14 Classification and Tree-Based Methods 191
14.1 Introduction 191
14.2 An Overview of Classification 191
14.2.1 The Classification Problem 192
14.2.2 Logistic Regression Model 192
14.2.2.1 l1 Regularization 193
14.2.2.2 l2 Regularization 194
14.3 Linear Discriminant Analysis 194
14.3.1 Optimal Classification and Estimation of Gaussian Distribution 195
14.4 Tree-Based Methods 197
14.4.1 One Single Decision Tree 197
14.4.2 Random Forest 198
14.5 Applications 200
14.6 Problems 202
15 Association Rules 205
15.1 Introduction 205
15.2 Market Basket Analysis 205
15.3 Terminologies 207
15.3.1 Itemset and Support Count 207
15.3.2 Frequent Itemset 207
15.3.3 Closed Frequent Itemset 207
15.3.4 Maximal Frequent Itemset 207
15.3.5 Association Rule 208
15.3.6 Rule Evaluation Metrics 208
15.4 The Apriori Algorithm 209
15.4.1 An example of the Apriori Algorithm 211
15.5 Applications 213
15.5.1 Confidence 214
15.5.2 Lift 215
15.5.3 Conviction 215
15.6 Problems 215
16 Support Vector Machines 219
16.1 Introduction 219
16.2 The Maximal Margin Classifier 219
16.3 Classification Using a Separating Hyperplane 223
16.4 Kernel Functions 225
16.5 Applications 225
16.6 Problems 227
17 Neural Networks 231
17.1 Introduction 231
17.2 Perceptrons 231
17.3 Feed Forward Neural Network 231
17.4 Recurrent Neural Networks 233
17.5 Long Short-Term Memory 234
17.5.1 Residual Connections 235
17.5.2 Loss Functions 236
17.5.3 Stochastic Gradient Descent 236
17.5.4 Regularization – Ensemble Learning 237
17.6 Application 237
17.6.1 Emergent and Developed Market 237
17.6.2 The Lehman Brothers Collapse 237
17.6.3 Methodology 238
17.6.4 Analyses of Data 238
17.6.4.1 Results of the Emergent Market Index 238
17.6.4.2 Results of the Developed Market Index 238
17.7 Significance of Study 239
17.8 Problems 240
18 Fourier Analysis 245
18.1 Introduction 245
18.2 Definition 245
18.3 Discrete Fourier Transform 246
18.4 The Fast Fourier Transform (FFT) Method 247
18.5 Dynamic Fourier Analysis 250
18.5.1 Tapering 251
18.5.2 Daniell Kernel Estimation 252
18.6 Applications of the Fourier Transform 253
18.6.1 Modeling Power Spectrum of Financial Returns Using Fourier
Transforms 253
18.6.1.1 Background of Data 255
18.6.2 Application of Dynamic Fourier Analysis 255
18.6.3 Image Compression 259
18.7 Problems 259
19 Wavelets Analysis 261
19.1 Introduction 261
19.1.1 Wavelets Transform 262
19.2 DiscreteWavelets Transforms (DWT) 264
19.2.1 HaarWavelets 265
19.2.1.1 Haar Functions 265
19.2.1.2 Haar Transform Matrix 266
19.2.2 DaubechiesWavelets 267
19.3 Applications of theWavelets Transform 268
19.3.1 Discriminating Between Mining Explosions and Cluster of
Earthquakes 269
19.3.1.1 Background of Data 269
19.3.1.2 Results 269
19.3.2 Finance 271
19.3.3 Damage Detection in Frame Structures 275
19.3.4 Image Compression 275
19.3.5 Seismic Signals 275
19.4 Problems 276
20 Stochastic Analysis 279
20.1 Introduction 279
20.2 Necessary Definitions from Probability Theory 279
20.3 Stochastic Processes 280
20.3.1 The Index Set ; 281
20.3.2 The State Space ; 281
20.3.3 Stationary and Independent Components 281
20.3.4 Stationary and Independent Increments 282
20.3.5 Filtration and Standard Filtration 283
20.4 Examples of Stochastic Processes 284
20.4.1 Markov Chains 284
20.4.1.1 Examples of Markov Processes 286
20.4.1.2 The Chapman–Kolmogorov Equation 287
20.4.1.3 Classification of States 288
20.4.1.4 Limiting Probabilities 289
20.4.1.5 Branching Processes 291
20.4.1.6 Time Homogeneous Chains 293
20.4.2 Martingales 293
20.4.3 Simple RandomWalk 294
20.4.4 The Brownian Motion (Wiener Process) 294
20.5 Measurable Functions and Expectations 294
20.5.1 Radon–Nikodym Theorem and Conditional Expectation 296
20.6 Problems 299
21 Fractal Analysis – Lévy, Hurst, DFA, DEA 301
21.1 Introduction and Definitions 301
21.2 Lévy Processes 301
21.2.1 Examples of Lévy Processes 304
21.2.1.1 The Poisson Process (Jumps) 305
21.2.1.2 The Compound Poisson Process 305
21.2.1.3 Inverse Gaussian (IG) Process 306
21.2.1.4 The Gamma Process 307
21.2.2 Exponential Lévy Models 307
21.2.3 Subordination of Lévy Processes 308
21.2.4 Stable Distributions 309
21.3 Lévy Flight Models 311
21.4 Rescaled Range Analysis (Hurst Analysis) 312
21.5 Detrended Fluctuation Analysis (DFA) 315
21.6 Diffusion Entropy Analysis (DEA) 316
21.6.1 Estimation Procedure 317
21.6.1.1 The Shannon Entropy 317
21.6.2 The H–𝛼 Relationship for the Truncated Lévy Flight 319
21.7 Application – Characterization of Volcanic Time Series 321
21.7.1 Background of Volcanic Data 321
21.7.2 Results 321
21.8 Problems 323
22 Stochastic Differential Equations 325
22.1 Introduction 325
22.2 Stochastic Differential Equations (SDEs) 325
22.2.1 Solution Methods of SDEs 326
22.3 Examples 335
22.3.1 Modeling Asset Prices 335
22.3.2 Modeling the Magnitude of Earthquake Series 336
22.4 Multidimensional Stochastic Differential Equations 337
22.4.1 The multidimensional Ornstein–Uhlenbeck Processes 337
22.4.2 Solution of the Ornstein–Uhlenbeck Process 338
22.5 Simulation of Stochastic Differential Equations 340
22.5.1 Euler–Maruyama Scheme for Approximating Stochastic Differential
Equations 340
22.5.2 Euler–Milstein Scheme for Approximating Stochastic Differential
Equations 341
22.6 Problems 343
23 Ethics: With Great Power Comes Great Responsibility 345
23.1 Introduction 345
23.2 Data Science Ethical Principles 346
23.2.1 Enhance Value in Society 346
23.2.2 Avoiding Harm 346
23.2.3 Professional Competence 347
23.2.4 Increasing Trustworthiness 348
23.2.5 Maintaining Accountability and Oversight 348
23.3 Data Science Code of Professional Conduct 348
23.4 Application 350
23.4.1 Project Planning 350
23.4.2 Data Preprocessing 350
23.4.3 Data Management 350
23.4.4 Analysis and Development 351
23.5 Problems 351
Bibliography 353
Index 359
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