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

Huy Hoang Nguyen

Paul N Adams

Stuart J Miller

£26.98 ~~£29.99~~

4.9 (11 Ratings)

eBook Aug 2023 420 pages 1st Edition

Huy Hoang Nguyen

Paul N Adams

Stuart J Miller

£26.98 ~~£29.99~~

4.9 (11 Ratings)

eBook Aug 2023 420 pages 1st Edition

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Key benefits

Gain expertise in identifying and modeling patterns that generate success
Explore the concepts with Python using important libraries such as stats models
Learn how to build models on real-world data sets and find solutions to practical challenges

Description

The ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation. This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you’ll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you’ll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more. By the end of this book, you’ll gain fluency in statistical modeling while harnessing the full potential of Python's rich ecosystem for data analysis.

Who is this book for?

If you are looking to get started with building statistical models for your data sets, this book is for you! Building Statistical Models in Python bridges the gap between statistical theory and practical application of Python. Since you’ll take a comprehensive journey through theory and application, no previous knowledge of statistics is required, but some experience with Python will be useful.

What you will learn

Explore the use of statistics to make decisions under uncertainty
Answer questions about data using hypothesis tests
Understand the difference between regression and classification models
Build models with stats models in Python
Analyze time series data and provide forecasts
Discover Survival Analysis and the problems it can solve

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Amazon verified reviews

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!

Amazon Verified review

JRVV Oct 19, 2023

The book provides a broad primer on statistical modeling using Python. This book can also serve as a starting point to those who eventually want to go into machine learning. Recommended.

Amazon Customer Oct 02, 2023

This book is exceptionally crafted, serving as a comprehensive review of fundamental statistical knowledge, complemented with practical Python codes. Unlike other market options, which either focus solely on theory or coding, lacking depth in theoretical insight, this book seamlessly bridges theory to application. While many statistical texts predominantly utilize the R language, this book's emphasis on Python is a refreshing change. It not only rejuvenates and reinforces my existing knowledge but also significantly advances my understanding of Statistics and Machine Learning. It stands out as a balanced and insightful resource for both theoretical comprehension and practical application in the field.

Steven Fernandes Oct 09, 2023

The authors offer a compelling dive into making informed decisions under uncertainty, equipping readers with practical skills, such as hypothesis testing and data analysis. They thoughtfully elucidate the distinctions between regression and classification models and provide a hands-on approach to building models using Python's statsmodels. The text also insightfully explores time-series data analysis, forecasting, and survival analysis, adeptly linking theory with real-world applications. This book emerges as an invaluable guide for both beginners and seasoned practitioners, intertwining robust theoretical constructs with practical applicability in data analysis and model-building, rendering it a must-read in the field of data science.

Ratan Nov 23, 2023

Authors have done a good job in maintaining the comprehensiveness of the book. They have maintained adequate amount of mathematics what is needed. I particularly loved the way they have presented Hypothesis testing for models which is often missing in many places. They have nicely covered both parametric and non parametric testing.The other part I liked was somewhat less visited topic survival analysis. Overall I found this book an excellent read ! Definitely recommend it.

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

What do you get with eBook?