Название: Just Enough Data Science and Machine Learning: Essential Tools and Techniques
Автор: Mark Levene, Martyn Harris
Издательство: Addison-Wesley Professional/Pearson Education
Год: 2025
Страниц: 224
Язык: английский
Формат: epub
Размер: 10.1 MB

An accessible introduction to applied Data Science and Machine Learning, with minimal math and code required to master the foundational and technical aspects of Data Science.

In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris present a comprehensive and accessible introduction to Data Science. It allows the readers to develop an intuition behind the methods adopted in both Data Science and Machine Learning, which is the algorithmic component of Data Science involving the discovery of patterns from input data. This book looks at Data Science from an applied perspective, where emphasis is placed on the algorithmic aspects of Data Science and on the fundamental statistical concepts necessary to understand the subject.

The book begins by exploring the nature of Data Science and its origins in basic statistics. The authors then guide readers through the essential steps of Data Science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand Machine Learning, which is central to the discipline of Data Science.

The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless.

Data Science is an interdisciplinary field that has evolved from a synergy between computer science and statistics. While Data Science focuses on the analysis and interpretation of data, Machine Learning is its algorithmic part enabling the discovery of patterns from a statistical model formed from the data.

In recent years, the field of Data Science and its subfield Machine Learning have emerged as indispensable tools for extracting valuable insights and making predictions from potentially vast amounts of data. As these disciplines continue to shape our world, it becomes increasingly important, not only to a wide range of information technology (IT) professionals and researchers, but also to enthusiasts who wish to grasp their fundamental principles and techniques.

Our aim in this book is that it will serve as an introductory guide to Data Science and Machine Learning. In our journey to explore the fundamentals of these fields, we focus on their applied side, giving practical examples of the concepts and methods. However, we do not shy away from presenting the fundamental theory of these subjects.

Notable features of this book:

Clear explanations of fundamental statistical notions and concepts
Coverage of various types of data and techniques for analysis
In-depth exploration of popular Machine Learning tools and methods
Insight into specific data science topics, such as social networks and sentiment analysis
Practical examples and case studies for real-world application
Recommended further reading for deeper exploration of specific topics.

This book is tailored for anyone seeking a comprehensive, yet accessible, introduction to Data Science and Machine Learning. Whether you are a student venturing into these fields for the first time an IT professional or researcher looking to broaden your skill set, or an enthusiast eager to explore the potential of data-driven decision making, this book is designed to cater to your needs.