Learn how to utilize biostatistics with Python for excelling in research and biomedical professions with practical exemplar projects
Key Features
- Bridge the gap between biostatistics and life sciences with Python
- Work with practical exercises for real-world data analysis in biology and medicine
- Access a portfolio of exemplar projects in the domains of biomedicine, biotechnology, and biology
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
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.
What you will learn
- Get to grips with the basics of biostatistics and Python programming
- Clean and describe data using Python
- Familiarize yourself with hypothesis testing and effect size analysis
- Explore predictive modeling in biostatistics
- Understand clinical study design and survival analysis
- Gain a clear understanding of the meta-analysis of clinical research data
- Analyze biological variables with Python
- Discover practical data analysis for biological research
Who this book is for
This book is for life science professionals, researchers, biomedical professionals, and aspiring biostatisticians who want to integrate biostatistics into their work or research. A basic understanding of life sciences, biology, or medicine is recommended to fully benefit from this book.
Table of Contents
- Introduction to Biostatistics
- Getting Started with Python for Biostatistics
- Exercise 1 – Cleaning and Describing Data Using Python
- Part 1 Exemplar Project – Load, Clean, and Describe Diabetes Data in Python
- Introduction to Python for Biostatistics
- Biostatistical Inference Using Hypothesis Tests and Effect Sizes
- Predictive Biostatistics Using Python
- Part 2 Exercise – T-Test, ANOVA, and Linear and Logistic Regression
- Biostatistical Inference and Predictive Analytics Using Cardiovascular Study Data
- Clinical Study Design
- Survival Analysis in Biomedical Research
- Meta-Analysis – Synthesizing Evidence from Multiple Studies
- Survival Predictive Analysis and Meta-Analysis Practice
- Part 3 Exemplar Project – Meta-Analysis of Survival Data in Clinical Research
- Understanding Biological Variables
(N.B. Please use the Read Sample option to see further chapters)
Darko Medin is a researcher and a biostatistician who graduated from the Faculty of Mathematics and Natural Sciences, Experimental Biology and Biotechnology, University of Montenegro. Darko is an expert biostatistician, especially in the fields of research and development in the biotech and pharma industries. He is a Python-based data scientist with more than 10 years of experience in the areas of clinical biostatistics and biomedical research. As a biologist and data scientist, he has worked with many research companies and academic institutions around the world and is an experienced machine learning and AI developer.
