Maria Cristina Mariani - Data Science in Theory and Practice

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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: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets Perfect for advanced undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs,
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.

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List of Illustrations

1 Chapter 4 Figure 4.1 Time series data of phase arrival times of an earthquake. Figure 4.2 Time series data of financial returns corresponding to Bank of Am... Figure 4.3 Seasonal trend component. Figure 4.4 Linear trend component. The horizontal axis is time картинка 7, and the ve... Figure 4.5 Nonlinear trend component. The horizontal axis is time картинка 8and the ... Figure 4.6 Cyclical component (imposed on the underlying trend). The horizon...

2 Chapter 7Figure 7.1 The big O notation.Figure 7.2 The картинка 9notation.Figure 7.3 The Data Science in Theory and Practice - изображение 10notation.Figure 7.4 Symbols used in flowchart.Figure 7.5 Flowchart to add two numbers entered by user.Figure 7.6 Flowchart to find all roots of a quadratic equation Data Science in Theory and Practice - изображение 11.Figure 7.7 Flowchart.

3 Chapter 8Figure 8.1 The box plot.Figure 8.2 Box plot example.

4 Chapter 9Figure 9.1 Scatter plot of temperature versus ice cream sales.Figure 9.2 Heatmap of handwritten digit data.Figure 9.3 Map of earthquake magnitudes recorded in Chile.Figure 9.4 Spatial distribution of earthquake magnitudes (Mariani et al. 201...Figure 9.5 Number of text messages sent.Figure 9.6 Normal Q–Q plot.Figure 9.7 Risk of loan default. Source: Tableau Viz Gallery.Figure 9.8 Top five publishing markets. Source: Modified from International ...Figure 9.9 High yield defaulted issuer and volume trends. Source: Based on F...Figure 9.10 Statistics page for popular movies and cinema locations. Source:...

5 Chapter 10Figure 10.1 One‐step binomial tree for the return process.

6 Chapter 11Figure 11.1 Height versus weight.Figure 11.2 Visualizing low‐dimensional data.Figure 11.3 2D data set.Figure 11.4 First PCA axis.Figure 11.5 Second PCA axis.Figure 11.6 New axis.Figure 11.7 Scatterplot of Royal Dutch Shell stock versus Exxon Mobil stock....

7 Chapter 12Figure 12.1 Classification (by quadrant) of earthquakes and explosions using...Figure 12.2 Classification (by quadrant) of Lehman Brothers collapse and Fla...Figure 12.3 Clustering results for the earthquake and explosion series based...Figure 12.4 Clustering results for the Lehman Brothers collapse, Flash crash...

8 Chapter 13Figure 13.1 Scatter plot of data in Table 13.1

9 Chapter 16Figure 16.1 The картинка 12‐plane and several other horizontal planes.Figure 16.2 The Data Science in Theory and Practice - изображение 13‐plane and several parallel planes.Figure 16.3 The plane Data Science in Theory and Practice - изображение 14.Figure 16.4 Two class problem when data is linearly separable.Figure 16.5 Two class problem when data is not linearly separable.Figure 16.6 ROC curve for linear SVM.Figure 16.7 ROC curve for nonlinear SVM.

10 Chapter 17Figure 17.1 Single hidden layer feed‐forward neural networks.Figure 17.2 Simple recurrent neural network.Figure 17.3 Long short‐term memory unit.Figure 17.4 Philippines (PSI). (a) Basic RNN. (b) LTSM.Figure 17.5 Thailand (SETI). (a) Basic RNN. (b) LTSM.Figure 17.6 United States (NASDAQ). (a) Basic RNN. (b) LTSM.Figure 17.7 JPMorgan Chase & Co. (JPM). (a) Basic RNN. (b) LTSM.Figure 17.8 Walmart (WMT). (a) Basic RNN. (b) LTSM.

11 Chapter 18Figure 18.1 3D power spectra of the daily returns from the four analyzed sto...Figure 18.2 3D power spectra of the returns (generated per minute) from the ...

12 Chapter 19Figure 19.1 Time‐frequency image of explosion 1 recorded by ANMO (Table 19.2...Figure 19.2 Time‐frequency image of earthquake 1 recorded by ANMO (Table 19....Figure 19.3 Three‐dimensional graphic information of explosion 1 recorded by...Figure 19.4 Three‐dimensional graphic information of earthquake 1 recorded b...Figure 19.5 Time‐frequency image of explosion 2 recorded by TUC (Table 19.3)...Figure 19.6 Time‐frequency image of earthquake 2 recorded by TUC (Table 19.3...Figure 19.7 Three‐dimensional graphic information of explosion 2 recorded by...Figure 19.8 Three‐dimensional graphic information of earthquake 2 recorded b...

13 Chapter 21Figure 21.1 картинка 15for volcanic eruptions 1 and 2.Figure 21.2 DFA for volcanic eruptions 1 and 2.Figure 21.3 DEA for volcanic eruptions 1 and 2.

Guide

1 Cover Page

2 Table of Contents

3 Title Page Data Science in Theory and Practice Techniques for Big Data Analytics and Complex Data Sets Maria Cristina Mariani University of Texas, El Paso El Paso, United States Osei Kofi Tweneboah Ramapo College of New Jersey Mahwah, United States Maria Pia Beccar-Varela University of Texas, El Paso El Paso, United States

4 Copyright

5 List of Figures

6 List of Tables

7 Preface

8 Begin Reading

9 Bibliography

10 Index

11 WILEY END USER LICENSE AGREEMENT

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