Introduction to Data Science: Practical Approach with R and Python

Description

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.

About the Author

Dr. B. Uma Maheswari is an Associate Professor of the Decision Sciences stream at PSG Institute of Management, Tamil Nadu, India. An academician with around two decades of experience, she specializes in the field of strategic management, business analytics, design thinking, and entrepreneurship. She is also an adjunct faculty of University of Toledo, USA. Dr. Maheswari has undergone trainings with various prestigious institutions across the globe.

Dr. R. Sujatha is an Associate Professor at PSG Institute of Management, PSG College of Technology, where she has been teaching since 2000. She is also an adjunct faculty of University of Toledo, USA. She has more than two decades of wide-ranging experience in teaching, research, and consulting at PSG Institute of Management. She has undergone an extensive 4 months’ Faculty Development Program at Indian Institute of Management, Ahmedabad. She has been certified by Wharton Business School, University of Pennsylvania, for the completion of their online course on Customer Analytics, People Analytics, Operation Analytics and Accounting Analytics.

Table of Contents

Book Review

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

Summary

Terms to Know

Review Questions

Exercise Problems

Internet Exercises

Mini Project

References

Answers to Multiple-Choice Questions

Interview Questions and Answers

Index