Includes bibliographical references and index.

Table of Contents

Part-I Data Science Introduction

Importance of Data Science

Need for Data Science

What is Data Science

Data Science Process

Business Intelligence and Data Science

Prerequisite for Data Scientist

Components of Data Science

Tools and Skills Need

Summary

Exercise

References

Statistics and Probability

2.1 Data Types

2.2. Variable Types

2.3 Statistics

2.4 Sampling Techniques and Probability

2.5 Information Gain and Entropy

2.6 Probability Theory

2.7 Probability Types

2.8 Probability Distribution

2.9 Bayes Theorem

2.10 Inferential Statistics

2.11 Summary

Exercise

References

3. Databases for Data Science

3.1 SQL-Tool for Data Science

3.1.1 Basic Statistics with SQL

3.1.2 Data Munging with SQL

3.1.3 Filtering, Joins and Aggregation

3.1.4 Window Functions and Ordered Data

3.1.5 Preparing Data for Analytics Tool

3.2 NoSQL for Data Science

3.2.1 Why NoSQL

3.2.2 Document databases for Data Science

3.2.3 Wide-Column Databases for Data Science

3.2.4 Graph Databases for Data Science

3.3 Summary

Exercise

References

Part II Data Modelling and Analytics

Chapter 4: Data Science Methodology

4.1 Analytics for Data Science

4.2 Data Analytics Examples

4.3 Data Analytics Life Cycle

4.3.1 Data Discovery

4.3.2 Data preparation

4.3.3 Model Planning

4.3.4 Model Building

4.3.5 Communicate Results

4.3.6 Operationalization

4.4 Summary

Exercise

References

Chapter 5: Data Science Methods and Machine learning

5.1 Regression Analysis

5.1.1 Linear Regression

5.1.2 Logistic Regression

5.1.3 Multinomial Logistic Regression

5.1.4 Time Series Models

5.2 Machine Learning

5.2.1 Decision Trees

5.2.2 Naïve Bayes

5.2.3 Support Vector Machines

5.2.4 Nearest Neighbour learning

5.2.5 Clustering

5.2.6 Confusion Matrix

5.3 Summary

Exercise

References

Chapter 6: Data Analytics and Text Mining

6.1 Text Mining

6.1.1 Major Text Mining Areas

6.2 Text Analytics

6.2.1 Text Analysis Subtasks

6.2.2 Basic Text Analysis Steps

6.3 Natural Language Processing

6.3.1 Major Components of NLP

6.3.2 Stages of NLP

6.3.3 Statistical Processing of Natural Language

6.3.4 Applications of NLP

6.4 Summary

Exercise

References

Part III: Platforms for Data Science

Chapter 7: Data Science Tool: Python

Basics Of Python

Python libraries: Data Frame Manipulation with Pandas, Numpy

Data Analysis Exploration With Python

Time Series Data

Clustering with Python

Arch & Garch

Dimensionality Reduction

Python for Machine Learning

Algorithms: KNN, Decision Tree, Random Forest, SVM

Python IDEs for Data Science

Summary

Exercise

References

　

Chapter 8: Data Science Tool: R

8.1 Reading and Getting Data into R

8.1.1 Reading Data into R

8.1.2 Writing Data into File

8.1.3 Scan() function

8.1.4 Built-in Datasets

8.2 Ordered and Unordered Factors

8.3 Arrays and Matrices

8.3.1 Arrays

8.3.2 Matrices

8.4 Lists and Data Frames

8.4.1 Lists

8.4.2 Data Frames

8.5 Probability Distributions

8.5.1 Normal Distribution

8.6 Statistical Models in R

8.6.1 Model Fitting

8.6.2 Marginal Effects

8.7 Manipulating Objects

8.7.1 Viewing Objects

8.7.2 Modifying Objects

8.7.3 Appending Elements

8.7.4 Deleting Objects

8.8 Data Distribution

8.8.1 Visualizing Distributions

8.8.2 Statistics in Distributions

8.9 Summary

Exercise

References

　

Chapter 9: Data Science Tool: MATLAB

9.1 Data Science Workflow and MATLAB

9.2 Importing Data

9.2.1 How Data is stored

9.2.2 How MATLAB Represents Data

9.2.3 MATLAB Data Types

9.2.4 Automating the Import Process

9.3 Visualizing and Filtering Data

9.3.1 Plotting Data Contained in Tables

9.3.2 Selecting Data from Tables

9.3.3 Accessing and Creating Table Variables

9.4 Performing Calculations

9.4.1 Basic Mathematical Operations

9.4.2 Using Vectors

9.4.3 Using Functions

9.4.4 Calculating Summary Statistics

9.4.5 Correlations between Variables

9.4.6 Accessing Subsets of Data

9.4.7 Performing Calculations by Category

9.5 Summary

Exercise

References

　

Chapter 10 : GNU Octave as a Data Science Tool

10.1 Vectors and Matrices

10.2 Arithmetic Operations

10.3 Set Operations

10.4 Plotting Data
10.5 Summary

Exercise

References

Chapter 11: Data Visualization using Tableau

11.1 Introduction to Data Visualization

11.2 Tableau Basics

11.3 Dimensions, Measures and Descriptive Statistics

11.4 Basic Charts

11.5 Dashboard Design & Principles

11.6 Special Chart Types

11.7 Integrate Tableau with Google Sheets

11.8 Summary

Exercise

References

Index

Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science.

Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of　data　by the use of various scientific methods, algorithms, and processes

Readers will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue.

This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge.