Fundamentals of Data Science
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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