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 DescriptionThis 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 forThis 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.
Sprache
Verlagsort
Maße
Höhe: 235 mm
Breite: 191 mm
Dicke: 20 mm
Gewicht
ISBN-13
978-1-83763-096-7 (9781837630967)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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.
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
Data Analysis Frameworks and Performance for Life Sciences Research
Part 4 Exercise - Performing Statistics for Biology Studies in Python
