Data Science in Theory and Practice - Techniques for Big Data Analytics and Complex Data Sets

Informationen zum Autor 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¿. Klappentext DATA SCIENCE IN THEORY AND PRACTICEEXPLORE THE FOUNDATIONS OF DATA SCIENCE WITH THIS INSIGHTFUL NEW RESOURCEData 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 setsPerfect 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. Zusammenfassung DATA SCIENCE IN THEORY AND PRACTICEEXPLORE THE FOUNDATIONS OF DATA SCIENCE WITH THIS INSIGHTFUL NEW RESOURCEData 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 dat...

List of contents

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 5
 
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 69
 
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

Product details

• Authors: BECCA, Beccar-Varel, Maria Pia Beccar-Varela, Maria Mariani, Maria C. Mariani, Maria C. Tweneboah Mariani, Maria Cristin Mariani, Maria Cristina Mariani, Maria Cristina (University of Texas At El Mariani, Maria Cristina Tweneboah Mariani, MC Mariani, Osei Kof Tweneboah, Osei Kofi Tweneboah
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 31.05.2021
• EAN: 9781119674689
• ISBN: 978-1-119-67468-9
• No. of pages: 400
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; Statistik, Informatik, Datenanalyse, Big Data, Statistics, computer science, Zeitreihen, data analysis, Time Series, Data Mining & Knowledge Discovery, Data Mining u. Knowledge Discovery