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.

Table of Contents:
List of Figures       ix Preface        xvii About the Authors        xix   Chapter 1. What Is Data Science?        1   Chapter 2. Basic Statistics         3 2.1 Introductory Statistical Notions         3 2.2 Expectation         17 2.3 Variance         21 2.4 Correlation         26 2.5 Regression         28 2.6 Chapter Summary         32   Chapter 3. Types of Data         33 3.1 Tabular Data         33 3.2 Textual Data         38 3.3 Image, Video, and Audio Data         40 3.4 Time Series Data         41 3.5 Geographical Data         42 3.6 Social Network Data         44 3.7 Transforming Data         46 3.8 Chapter Summary         51   Chapter 4. Machine Learning Tools         52 4.1 What Is Machine Learning?         52 4.2 Evaluation         57 4.3 Supervised Methods         68 4.4 Unsupervised Methods         105 4.5 Semi-Supervised Methods         125 4.6 Chapter Summary         129   Chapter 5. Data Science Topics         130 5.1 Searching, Ranking, and Rating         130 5.2 Social Networks         150 5.3 Three Natural Language Processing Topics         171 5.4 Chapter Summary         183   Chapter 6. Selected Additional Topics         184 6.1 Neuro-Symbolic AI         184 6.2 Conversational AI         185 6.3 Generative Neural Networks         185 6.4 Trustworthy AI         186 6.5 Large Language Models         187 6.6 Epilogue         187   Chapter 7. Further Reading         189 7.1 Basic Statistics         189 7.2 Data Science         189 7.3 Machine Learning         190 7.4 Deep Learning         191 7.5 Research Papers         191 7.6 Python         191   Bibliography         192  Index         195