Introduction to data science: practical approach with R and Python

Table of content
Chapter 1 Introduction to Data Science

1.1 Data Science

1.2 Brief History of Data Science

1.3 Increasing Attention to Data Science

1.4 Fundamental Fields of Study Related to Data Science

1.5 Data Science and Related Terminologies

1.6 Types of Analytics

1.7 Applications of Data Science

1.8 Data Science Process Model



Chapter 2 Introduction to R and Python

2.1 Introduction

2.2 R and RStudio Environment

2.3 Basics of R

2.4 Python Language and Python Environment

2.5 Basics of Python



Chapter 3 Exploratory Data Analysis

3.1 Introduction

3.2 Steps in Data Preprocessing

3.3 Understanding Data

3.3.1 Steps Involved in EDA Using R Programming

3.4 Looking at the Data

3.5 Visualizing Data

3.6 Dealing with Outliers

3.7 Dealing with Missing Values

3.8 Standardizing Data

3.9 Steps Involved in EDA Using Python Programming

3.10 Looking at the Data

3.11 Visualization the Data

3.12 Treatment of Outliers



Chapter 4 Data Visualization

4.1 Introduction

4.2 Data Visualization for Machine Learning

4.3 Data Visualization Techniques

4.4 Simple Data Visualization Using R

4.5 Data Visualization Using Ggplots in R

4.6 Data Visualization Using Python

4.7 Matplotlib Library

4.8 Seaborn Library



Chapter 5 Dimensionality Reduction Techniques

5.1 Dimensionality Reduction

5.2 Independent and Dependent Variables

5.3 Relationship between Variables: Correlation

5.5 Factor Analysis

5.5.4 Rotated Factor Matrix

5.6 Application of Factor Analysis Using Python Programming



Chapter 6 Types of Machine Learning Algorithms

6.1 Introduction

6.2 Supervised and Unsupervised Learning Algorithms

6.3 Supervised Learning Algorithm

6.4 Unsupervised Learning Algorithm



Chapter 7 Unsupervised Learning Algorithms

7.1 Introduction

7.2 Association Rule Mining

7.3 Conjoint Analysis

7.4 Clustering

7.5 K Means Clustering



Chapter 8 Text Analytics

8.1 Introduction

8.2 Unstructured Data

8.3 Word Cloud

8.4 Sentiment Analysis

8.5 Web and Social Media Analytics



Chapter 9 Supervised Learning Algorithms: Linear and Logistic Regression

9.1 Introduction

9.2 Simple Linear Regression

9.3 Multiple Linear Regression

9.4 Logistic Regression



Chapter 10 Supervised Learning Algorithms: Decision Tree and Random Forest

10.1 Decision Tree

10.2 Classification and Regression Technique

10.3 Random Forest



Chapter 11 Supervised Learning Algorithm: KNN, Naïve Bayes, and Linear Discriminant Analysis

11.1 K-Nearest Neighbors

11.2 Naïve Bayes Algorithm

11.3 Linear Discriminant Analysis



Chapter 12 Support Vector Machines and Artificial Neural Networks

12.1 Support Vector Machines

12.2 Artificial Neural Networks



Chapter 13 Time Series Forecasting

13.1 Introduction

13.2 Time Series Data

13.3 Visualizing the Time Series Data

13.4 Components of Time Series Data

13.5 Stationarity of the Data

13.6 Exponential Smoothening Model

13.7 Holt–Winters Model

13.8 ARIMA Model



Chapter 14 Ensemble Methods

14.1 Introduction

14.2 Dealing with Imbalanced Data

14.3 Ensemble Methods

14.4 Bias Variance Tradeoff

14.5 Bagging

14.6 Boosting

14.7 Synthetic Minority over Sampling Technique (SMOTE)



Chapter 15 Artificial Intelligence

15.1 Introduction

15.2 Artificial Intelligence

15.3 Deep Learning

15.4 Convolutional Neural Networks

15.5 Reinforcement Learning



Chapter 16 Applications of Analytics

16.1 Introduction

16.2 Application of Analytics in Healthcare

16.3 Application of Analytics in Agriculture

16.4 Application of Analytics in Business

16.5 Application of Analytics in Sports

16.6 Application of Analytics in Governance

Introduction to Data Science: Practical Approach with R and Python covers all the fundamental concepts of Data Science in a concise manner. It offers a mix of insights and golden rules which would be needed in analyzing data. This book serves as a practical guide for Science/Engineering/MBA students – both at the undergraduate and postgraduate level interested in Data Science domain.