Just Enough Data Science and Machine Learning: Essential Tools and Techniques (剛好足夠的資料科學與機器學習：基本工具與技術)

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.

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.