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Fundamentals of Data Science

Fundamentals of Data Science PDF

Author: Sanjeev J. Wagh, Manisha S. Bhende

Publisher: Chapman and Hall/CRC


Publish Date: September 28, 2021

ISBN-10: 1138336181

Pages: 296

File Type: PDF

Language: English

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Book Preface

The best way to teach data science is to focus on the work culture in the data  analytics business world. Data science is touted as the sexiest job of the 21st  century. Everyone – from companies to individuals – is trying to understand  it and adopt it. If you’re a programmer, you most definitely are experiencing  FOMO (fear of missing out)! Data science helps you be data-driven. Datadriven decisions help companies understand their customers better and  build great businesses. Companies collect different kinds of data depending  upon the type of business they run. For example, for a retail store, the data  could be about the kinds of products that its customers buy over time and  their spending amounts. For Netflix, it could be about what shows most of  their users watch or like and their demographics.

This book consists of 11 chapters, encompassing data science concepts to  hands-on tools to practice, which will support aspirants to the data science  domain. Chapter 1 introduces the importance of data science and its impact  on society. The process, prerequisites, components, and tools are discussed  to enhance the business strategies. Chapter 2 defines the concepts of statistics and probability, which are the basis to understand data science strategies.

To understand the background of data science, traditional SQL practices are  discussed with the recent popular tool, i.e., NoSQL, with its importance and  the techniques to handle more complex databases in Chapter 3. Chapter 4  elaborates on data science methodology with data analysis and life cycle management. The analytics for data science with some examples is discussed. The  data analytics life cycle with all phases is discussed to in line with understanding the data science techniques. Chapter 5 outlines data science methods and  machine learning approach with various techniques for regression analysis  and standard machine learning methods. Data analytics and text mining are  precisely elaborated with natural language processing in Chapter 6. Chapters  7–11 cover various platforms and tools to practice the data science techniques  with a practical approach. This section covers Python, R, MATLAB, GNU  Octave, and Tableau tools precisely with sample examples to practice and  apply for implementing desired applications in the theme domain.

This will be a textbook tool that provides students, academicians, and practitioners with a complete walk-through right from the foundation required,  outlining the database concepts and techniques required to understand data  science. This book isn’t meant to be read cover to cover or page to a chapter  that looks like something you’re trying to accomplish or that simply ignites  your interest in data analytics for implementation of real project applications.

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