ABSTRACT

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.

 

Features :

  • Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets.
  • Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools.
  • Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice.
  • Information is presented in an accessible way for students, researchers and academicians and professionals.

part I|84 pages

Introduction to Data Science

chapter 1|13 pages

Importance of Data Science

chapter 2|28 pages

Statistics and Probability

chapter 3|40 pages

Databases for Data Science

part II|59 pages

Data Modeling and Analytics

chapter 4|15 pages

Data Science Methodology

chapter 5|25 pages

Data Science Methods and Machine Learning

chapter 6|15 pages

Data Analytics and Text Mining

part III|123 pages

Platforms for Data Science

chapter 7|39 pages

Data Science Tool

Python

chapter 8|22 pages

Data Science Tool

R

chapter 9|23 pages

Data Science Tool: MATLAB

chapter 10|16 pages

GNU Octave as a Data Science Tool

chapter 11|19 pages

Data Visualization Using Tableau