Amudhan Jul 09, 2024

I recently purchased "Building Statistical Models in Python" by Dr. Jane Smith, and I have to say, it’s one of the best investments I’ve made in my journey to becoming a data scientist.Content and StructureThe book is exceptionally well-organized, beginning with fundamental concepts and gradually progressing to more complex topics. Dr. Smith does an excellent job of explaining the theory behind statistical models before diving into practical implementation using Python. This approach ensures that readers not only know how to use the tools but also understand the reasoning behind them.Practical Examples and ExercisesOne of the standout features of this book is the plethora of practical examples and exercises. Each chapter includes detailed code snippets and real-world datasets, allowing readers to apply what they've learned immediately. The exercises at the end of each chapter reinforce key concepts and challenge readers to think critically and creatively.Clarity and AccessibilityDr. Smith’s writing style is clear and engaging. She has a knack for breaking down complex concepts into digestible pieces. Even if you're new to statistics or Python, you’ll find her explanations easy to follow. The book strikes a perfect balance between being thorough and accessible, making it suitable for both beginners and those looking to deepen their knowledge.Comprehensive CoverageThe book covers a wide range of topics, including:Linear and logistic regressionTime series analysisBayesian statisticsMachine learning modelsModel evaluation and validationEach topic is explored in depth, with plenty of visual aids such as graphs and charts to illustrate key points. The inclusion of advanced topics like Bayesian statistics and machine learning makes this book a valuable resource even for seasoned data scientists.Supportive ResourcesAnother great aspect of this book is the supplementary resources. Dr. Smith provides access to an online repository containing all the datasets and Python scripts used in the book. This is incredibly helpful for readers who want to experiment with the code or apply the models to their own data.Final ThoughtsOverall, "Building Statistical Models in Python" is a must-have for anyone serious about data science. Whether you’re a student, a professional looking to switch careers, or a data enthusiast, this book offers invaluable insights and practical skills. Dr. Jane Smith has created a comprehensive, accessible, and engaging guide that will undoubtedly become a go-to reference for many aspiring data scientists.Highly recommended!

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John Montalbo Oct 02, 2023

This book is a nice addition to your professional library.From basic descriptive statistics, to machine learning algorithms, this text covers a great deal of topics. And the authors cover each topic first at a foundational level while pairing most examples and discussion with accompanied Python code and visualizations.This is a great reference guide and is well structured and presented in an engaging manner.

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Sangita Mahala Oct 16, 2023

Building Statistical Models in Python is a comprehensive and practical guide to build statistical models in Python. From the fundamentals of probability and statistics to more complex subjects like machine learning and time series analysis, the book covers a wide range of subjects. If you are looking for a book that will teach you how to build powerful statistical models to solve real-world problems, then Building Statistical Models in Python is the book for you.

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Alisha Pillay Oct 25, 2023

The book has a very detailed and must have techniques that any data scientist would need. It builds from ground up providing the statistical theory behind the algorithms. covers a wide range of algorithms for supervised, unsupervised and time series.

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Dror Oct 01, 2023

Statistics is a fundamental discipline concerned with the collection, organization, analysis, interpretation, and presentation of data. While Python—an extremely popular general-purpose programming language—has become the programming language of choice for computation in most science and engineering disciplines, most (software-oriented) statistics books still teach statistics using the more special-purpose R language.This unique and highly practical book provides a gentle introduction to statistics and to using the Python programming language for building statistical models. It begins with a clear and useful introduction to statistics, including sampling, data distributions, hypothesis testing, and parametric and non-parametric statistical tests. It then progresses to describe in detail how to build statistical models using Python for a variety of problems, including for regression, classification, time-series, and survival analysis. The descriptions are clear and concise, and gradually present additional common and helpful Python packages for performing statistical analysis. The accompanying GitHub repository includes practical and detailed code examples, and is very helpful in reinforcing the materials and concepts presented in the book.I highly recommend this book to anyone interested in learning statistics and how to use Python for building statistical models. It requires no more than basic knowledge of the Python programming language, and will be ideal for data scientists, analysts, and industry professionals who are taking their first steps in the world of statistics or want to expand their knowledge in this area.Highly recommended!

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Building Statistical Models in Python: Develop useful models for regression, classification, time series, and survival analysis

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