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Biostatistics with Python: Apply Python for biostatistics with hands-on biomedical and biotechnology projects - Softcover

 
9781837630967: Biostatistics with Python: Apply Python for biostatistics with hands-on biomedical and biotechnology projects

Synopsis

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

  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

(N.B. Please use the Read Sample option to see further chapters)

"synopsis" may belong to another edition of this title.

About the Author

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.

"About this title" may belong to another edition of this title.

  • PublisherPackt Publishing
  • Publication date2024
  • ISBN 10 1837630968
  • ISBN 13 9781837630967
  • BindingPaperback
  • LanguageEnglish
  • Number of pages374

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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Learn how to utilize biostatistics with Python for excelling in research and biomedical professions with practical exemplar projectsKey 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 Elektronisches BuchBook 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 researchWho 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 Data Analysis Frameworks and Performance for Life Sciences Research Part 4 Exercise - Performing Statistics for Biology Studies in Python. Seller Inventory # 9781837630967

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