Читать книгу Data Science in Theory and Practice - Maria Cristina Mariani - Страница 2
Table of Contents
Оглавление1 Cover
6 Preface
7 1 Background of Data Science 1.1 Introduction 1.2 Origin of Data Science 1.3 Who is a Data Scientist? 1.4 Big Data
8 2 Matrix Algebra and Random Vectors 2.1 Introduction 2.2 Some Basics of Matrix Algebra 2.3 Random Variables and Distribution Functions 2.4 Problems
9 3 Multivariate Analysis 3.1 Introduction 3.2 Multivariate Analysis: Overview 3.3 Mean Vectors 3.4 Variance–Covariance Matrices 3.5 Correlation Matrices 3.6 Linear Combinations of Variables 3.7 Problems
10 4 Time Series Forecasting 4.1 Introduction 4.2 Terminologies 4.3 Components of Time Series 4.4 Transformations to Achieve Stationarity 4.5 Elimination of Seasonality via Differencing 4.6 Additive and Multiplicative Models 4.7 Measuring Accuracy of Different Time Series Techniques 4.8 Averaging and Exponential Smoothing Forecasting Methods 4.9 Problems
11 5 Introduction to R 5.1 Introduction 5.2 Basic Data Types 5.3 Simple Manipulations – Numbers and Vectors 5.4 Problems
12 6 Introduction to Python 6.1 Introduction 6.2 Basic Data Types 6.3 Number Type Conversion 6.4 Python Conditions 6.5 Python File Handling: Open, Read, and Close 6.6 Python Functions 6.7 Problems
13 7 Algorithms 7.1 Introduction 7.2 Algorithm – Definition 7.3 How to Write an Algorithm 7.4 Asymptotic Analysis of an Algorithm 7.5 Examples of Algorithms 7.6 Flowchart 7.7 Problems
14 8 Data Preprocessing and Data Validations 8.1 Introduction 8.2 Definition – Data Preprocessing 8.3 Data Cleaning 8.4 Data Transformations 8.5 Data Reduction 8.6 Data Validations 8.7 Problems
15 9 Data Visualizations 9.1 Introduction 9.2 Definition – Data Visualization 9.3 Data Visualization Techniques 9.4 Data Visualization Tools 9.5 Problems
16 10 Binomial and Trinomial Trees 10.1 Introduction 10.2 The Binomial Tree Method 10.3 Binomial Discrete Model 10.4 Trinomial Tree Method 10.5 Problems
17 11 Principal Component Analysis 11.1 Introduction 11.2 Background of Principal Component Analysis 11.3 Motivation 11.4 The Mathematics of PCA 11.5 How PCA Works 11.6 Application 11.7 Problems
18 12 Discriminant and Cluster Analysis 12.1 Introduction 12.2 Distance 12.3 Discriminant Analysis 12.4 Cluster Analysis 12.5 Problems
19 13 Multidimensional Scaling 13.1 Introduction 13.2 Motivation 13.3 Number of Dimensions and Goodness of Fit 13.4 Proximity Measures 13.5 Metric Multidimensional Scaling 13.6 Nonmetric Multidimensional Scaling 13.7 Problems
20 14 Classification and Tree‐Based Methods 14.1 Introduction 14.2 An Overview of Classification 14.3 Linear Discriminant Analysis 14.4 Tree‐Based Methods 14.5 Applications 14.6 Problems
21 15 Association Rules 15.1 Introduction 15.2 Market Basket Analysis 15.3 Terminologies 15.4 The Apriori Algorithm 15.5 Applications 15.6 Problems
22 16 Support Vector Machines 16.1 Introduction 16.2 The Maximal Margin Classifier 16.3 Classification Using a Separating Hyperplane 16.4 Kernel Functions 16.5 Applications 16.6 Problems
23 17 Neural Networks 17.1 Introduction 17.2 Perceptrons 17.3 Feed Forward Neural Network 17.4 Recurrent Neural Networks 17.5 Long Short‐Term Memory 17.6 Application 17.7 Significance of Study 17.8 Problems
24 18 Fourier Analysis 18.1 Introduction 18.2 Definition 18.3 Discrete Fourier Transform 18.4 The Fast Fourier Transform (FFT) Method 18.5 Dynamic Fourier Analysis 18.6 Applications of the Fourier Transform 18.7 Problems
25 19 Wavelets Analysis 19.1 Introduction 19.2 Discrete Wavelets Transforms 19.3 Applications of the Wavelets Transform 19.4 Problems
26 20 Stochastic Analysis 20.1 Introduction 20.2 Necessary Definitions from Probability Theory 20.3 Stochastic Processes 20.4 Examples of Stochastic Processes 20.5 Measurable Functions and Expectations 20.6 Problems
27 21 Fractal Analysis – Lévy, Hurst, DFA, DEA 21.1 Introduction and Definitions 21.2 Lévy Processes 21.3 Lévy Flight Models 21.4 Rescaled Range Analysis (Hurst Analysis) 21.5 Detrended Fluctuation Analysis (DFA) 21.6 Diffusion Entropy Analysis (DEA) 21.7 Application – Characterization of Volcanic Time Series 21.8 Problems
28 22 Stochastic Differential Equations 22.1 Introduction 22.2 Stochastic Differential Equations 22.3 Examples 22.4 Multidimensional Stochastic Differential Equations 22.5 Simulation of Stochastic Differential Equations 22.6 Problems
29 23 Ethics: With Great Power Comes Great Responsibility 23.1 Introduction 23.2 Data Science Ethical Principles 23.3 Data Science Code of Professional Conduct 23.4 Application 23.5 Problems
30 Bibliography
31 Index
