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Biostatistics with Python

You're reading from   Biostatistics with Python Apply Python for biostatistics with hands-on biomedical and biotechnology projects

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Product type Paperback
Published in Nov 2024
Publisher Packt
ISBN-13 9781837630967
Length 374 pages
Edition 1st Edition
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Author (1):
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Darko Medin Darko Medin
Author Profile Icon Darko Medin
Darko Medin
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Table of Contents (24) Chapters Close

Preface 1. Part 1:Introduction to Biostatistics and Getting Started with Python
2. Chapter 1: Introduction to Biostatistics FREE CHAPTER 3. Chapter 2: Getting Started with Python for Biostatistics 4. Chapter 3: Exercise 1 – Cleaning and Describing Data Using Python 5. Chapter 4: Part 1 Exemplar Project – Load, Clean, and Describe Diabetes Data in Python 6. Part 2:Introduction to Python for Biostatistics – Methodology and Examples
7. Chapter 5: Introduction to Python for Biostatistics 8. Chapter 6: Biostatistical Inference Using Hypothesis Tests and Effect Sizes 9. Chapter 7: Predictive Biostatistics Using Python 10. Chapter 8: Part 2 Exercise – T-Test, ANOVA, and Linear and Logistic Regression 11. Chapter 9: Biostatistical Inference and Predictive Analytics Using Cardiovascular Study Data 12. Part 3:Clinical Study Design, Analysis, and Synthesizing Evidence
13. Chapter 10: Clinical Study Design 14. Chapter 11: Survival Analysis in Biomedical Research 15. Chapter 12: Meta-Analysis – Synthesizing Evidence from Multiple Studies 16. Chapter 13: Survival Predictive Analysis and Meta-Analysis Practice 17. Chapter 14: Part 3 Exemplar Project – Meta-Analysis of Survival Data in Clinical Research 18. Part 4:Biological and Statistical Variables and Frameworks, and a Final Practical Project from the Field of Biology
19. Chapter 15: Understanding Biological Variables 20. Chapter 16: Data Analysis Frameworks and Performance for Life Sciences Research 21. Chapter 17: Part 4 Exercise – Performing Statistics for Biology Studies in Python 22. Index 23. Other Books You May Enjoy

Preface

Unlike other books aimed at biostatisticians, this book is written for all those who wish to use the power of Python for biostatistics in their respective fields. Python has become the most used programming language, adopted in almost all segments of the biotech industry, the medical and pharma research industries, and academia, due to its ability to be integrated with artificial intelligence and machine learning. For this reason, implementing biostatistics with Python is one of the most important fields of research today. This book capitalizes on Python’s strengths and scalability to augment and improve the researcher’s toolbox, helping anyone in the life sciences and biostatistics fields. This book is one of the rarest resources for this book’s audience.

This book provides a comprehensive guide to combining Python programming with biostatistics for applications in life sciences, biotech, and AI-driven fields. It offers real-world projects and examples from oncology, cardiology, biology, and biotech, making learning practical and relevant. The book integrates the biological, data science, and statistical domains with coding, catering to both novices and experienced programmers. Python’s scalability and efficiency make it an invaluable tool for biotech, clinical, life sciences, and bioinformatics professionals, enabling the automation of data processing and analysis tasks while significantly reducing time and effort.

You will gain the modern programming skills necessary to perform complex statistical analyses and connect your Python expertise to cutting-edge fields such as artificial intelligence, machine learning, and digital product creation. The book equips you with domain-specific biostatistics knowledge tailored to life sciences and biotech, eliminating the need to learn additional programming languages. It also empowers AI developers, software engineers, and digital product creators to evaluate AI models, test results, and deliver insightful analyses within the Python ecosystem.

By bridging Python programming and biostatistics, Biostatistics with Python offers a well-structured approach to mastering essential statistical concepts, unlocking powerful applications across numerous scientific and technological domains.

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