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

Product type Paperback

Published in Nov 2024

Publisher Packt

ISBN-13 9781837630967

Length 374 pages

Edition 1st Edition

Languages

Python

Tools

NetApp ONTAP

Concepts

Statistics

Author (1):

Darko Medin

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Table of Contents (24) Chapters

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

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23. Other Books You May Enjoy

Summary

In this chapter, we learned how to practically apply Python functions for loading the data and the Diabetes dataset. We also learned how to validate and describe the dataset. Finally, we learned how to create intuitive data visualizations for further describing the data visually.

Congratulations! You have finished the chapter. In the next chapter, the theme is more specialized methods for different biostatistical areas.

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Table of Contents (24) Chapters

Summary

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Table of Contents (24) Chapters

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