This book leverages the author’s decade-long experience in biostatistics and data science to simplify the practical use of biostatistics with Python. The chapters show you how to clean and describe your data effectively, setting a solid foundation for accurate analysis and proficiency in biostatistical inference to help you draw meaningful conclusions from your data through hypothesis testing and effect size analysis.
The book walks you through predictive modeling to harness the power of Python to create robust predictive analytics that can drive your research and professional projects forward. You'll explore clinical biostatistics, learn how to design studies, conduct survival analysis, and synthesize evidence from multiple studies with meta-analysis -- skills that are crucial for making informed decisions based on comprehensive data reviews. The concluding chapters will enhance your ability to analyze biological variables, enabling you to perform detailed and accurate data analysis for biological research. This book's unique blend of biostatistics and Python helps you find practical solutions that make complex concepts easy to grasp and apply.
By the end of this biostatistics book, you’ll have moved from theoretical knowledge to practical experience, allowing you to perform biostatistical analysis confidently and accurately.

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Spis treści

Biostatistics with Python. Apply Python for biostatistics with hands-on biomedical and biotechnology projects eBook -- spis treści

• 1. Introduction to Biostatistics
• 2. Getting Started with Python for Biostatistics
• 3. Exercise 1 – Cleaning and Describing Data Using Python
• 4. Part 1 Exemplar Project – Load, Clean, and Describe Diabetes Data in Python
• 5. Introduction to Python for Biostatistics
• 6. Biostatistical Inference Using Hypothesis Tests and Effect Sizes
• 7. Predictive Biostatistics Using Python
• 8. Part 2 Exercise – T-Test, ANOVA, and Linear and Logistic Regression
• 9. Biostatistical Inference and Predictive Analytics Using Cardiovascular Study Data
• 10. Clinical Study Design
• 11. Survival Analysis in Biomedical Research
• 12. Meta-Analysis – Synthesizing Evidence from Multiple Studies
• 13. Survival Predictive Analysis and Meta-Analysis Practice
• 14. Part 3 Exemplar Project – Meta-Analysis of Survival Data in Clinical Research
• 15. Understanding Biological Variables
• 16. Data Analysis Frameworks and Performance for Life Sciences Research
• 17. Part 4 Exercise – Performing Statistics for Biology Studies in Python