Pablo Cepeda Oct 26, 2024

Nice book

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Amazon Customer Sep 22, 2024

The book is structured around 15 crucial concepts that form the bedrock of data science. From the very beginning, readers are introduced to foundational topics like random variables and probability distributions. Hoyle's clear explanations help readers grasp why data varies, setting a solid groundwork for the more complex ideas that follow. The transition into matrices and linear algebra is seamless, making complex mathematical concepts accessible even to those who may not have a strong math background.The progression from essential to advanced topics — such as kernel methods, information theory, and Bayesian non-parametric methods — is methodical and well-paced. The inclusion of topics like time series forecasting and network analysis further enriches the book, catering to a diverse range of interests within the data science community.Overall, this book is a significant contribution to the field of data science literature. It not only elucidates the mathematical principles that underpin various algorithms but also empowers readers to become more proficient in applying these concepts to real-world scenarios. For anyone serious about mastering data science, Hoyle’s work is a must-read, offering a blend of theory and practice that is sure to enhance your analytical capabilities.

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Gabriel Preda Sep 27, 2024

If you got here, you are planning probably to buy and read this book. Which will be an excellent idea, because is giving you the best value for money for this type of books. Let's see why the book structure, its content quality, the quality of printing, all are providing good reasons to do it.Structure: The book is structured in three parts, covering concepts gradually increasing in difficulty, from basic and introductory notions to intermediate and advanced. In the first part the basic principles in random numbers and probability distribution, matrices and linear algebra, loss functions, optimisation, probabilistic modeling are covered. In the second part, the book will tackle time series and forecasting, hypothesis testing, and model complexity, followed by function decomposition, and network analysis. In the last part, the more advanced topics of dynamical systems, kernel methods, information theory, non-parametric Bayesian methods and random matrices are covered.Content quality: The author is a former university professor and you can see that in the consistency of the material he included, as well as in the clear explanations of the notions, so that you can keep pace not only with the introductory chapters but also with the most advanced ones. The book is well documented, the notations are carefully edited, the narative is clear and easy to follow. This easiness to follow the content is supported by the author’s choice to add summaries, example, and further reading recommendations, depending on what is adequate in the specific case, to the end of each chapter. This helps the reader to crystallise his recently learned topics.Typography: When you read this book, you enjoy not only the quality of the content and the way that the author explains rather complex notions so that you can follow, but also the quality of the typography. A beautiful font (I read it on a paperback copy), with clear tables and beautifully rendered images, in color (which for a technical book, especially when graph are presented, is a very useful feature).Summary: An easy to read, well documented, highly recommended reference for those of you that takes seriously the Data Science specialisation. And an excellent introduction in the basic tools for the Data Scientist.

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Steven Fernandes Sep 12, 2024

Title: Elevate Your Data Science Skills: A Comprehensive GuideReview:This book is a comprehensive toolkit for anyone looking to deepen their data science expertise. It covers foundational concepts and advanced techniques, using real-world challenges to illustrate the application of theories. Practical implementations using Python libraries like NumPy, SciPy, and sci-kit-learn are detailed, alongside guidance on building predictive models and mastering Bayesian methods. It’s especially valuable for those interested in time series and network data. A must-have for aspiring and seasoned data scientists alike.

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Sai Kumar Bysani Oct 18, 2024

The book encompasses a wide array of mathematical concepts essential for data science, including:1. 𝐑𝐚𝐧𝐝𝐨𝐦 𝐕𝐚𝐫𝐢𝐚𝐛𝐥𝐞𝐬 & 𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧𝐬: Understanding the foundations of randomness and how different distributions impact data interpretation.2. 𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚: Key concepts like matrices and vectors that form the backbone of many algorithms used in machine learning and data analysis.3. 𝐋𝐨𝐬𝐬 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬 & 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Insight into how these elements are crucial for training models and making predictions.4. 𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐬𝐭𝐢𝐜 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠: Techniques for making inferences and predictions based on observed data.5. 𝐇𝐲𝐩𝐨𝐭𝐡𝐞𝐬𝐢𝐬 𝐓𝐞𝐬𝐭𝐢𝐧𝐠: A fundamental aspect of data science that helps in validating assumptions and claims based on data.6. 𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Methods for analyzing data points collected or recorded at specific time intervals, essential for forecasting....and many more important math concepts!I like how the book combines theory with practical application, providing Python code snippets that help readers see how these concepts are applied in real-world scenarios. This practical focus makes the book particularly beneficial for those who learn best by doing.Whether you’re new to data science or looking to sharpen your skills, this book serves as a solid reference guide and a stepping stone to deeper understanding. It encourages readers to not only grasp the math but to apply it effectively in their data science projects.In conclusion, if you're looking to enhance your mathematical toolkit and elevate your data science skills, I highly recommend picking up this book. It’s a great addition to any data scientist’s library and a fantastic resource for professional growth.

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15 Math Concepts Every Data Scientist Should Know: Understand and learn how to apply the math behind data science algorithms

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