Table of Contents
1 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