Biostatistics with Python. Apply Python for biostatistics with hands-on biomedical and biotechnology projects Zabrze

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. Spis treści: 1. Introduction to Biostatistics2. Getting Started with Python for Biostatistics3. Exercise 1 – Cleaning and Describing Data Using Python4. Part 1 Exemplar Project – Load, Clean, and Describe Diabetes Data in Python5. Introduction to Python for Biostatistics6. Biostatistical Inference Using Hypothesis Tests and Effect Sizes7. Predictive Biostatistics Using Python8. Part 2 Exercise – T-Test, ANOVA, and Linear and Logistic Regression9. Biostatistical Inference and Predictive Analytics Using Cardiovascular Study Data10. Clinical Study Design11. Survival Analysis in Biomedical Research12. Meta-Analysis – Synthesizing Evidence from Multiple Studies13. Survival Predictive Analysis and Meta-Analysis Practice14. Part 3 Exemplar Project – Meta-Analysis of Survival Data in Clinical Research15. Understanding Biological Variables16. Data Analysis Frameworks and Performance for Life Sciences Research17. Part 4 Exercise – Performing Statistics for Biology Studies in Python O autorze: Darko Medin is a Biostatistics consultant and a Data Science expert working with Universities, Biotech, Pharmaceutical and Educational companies in different areas of Biostatistics, Data Science, Biology and Biomedical Research.