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

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Key benefits

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

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

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Tiny Jan 09, 2025

An excellent practical guide for an intro to statistics with Python. Preferred application here was Anaconda and the Jupyter Notebook integration, sensible when one considers statistical analysis. The book clearly demonstrates how clinical studies and biology ties into the demonstrated functions. Full cases provided which start with the basics of loading and cleaning data, moves into linear regression testing, meta-analysis, and finally an end-to-end approach. Each chapter includes clear definitions, practical examples, and then the coding for Python. The latter sections discuss a great deal about important clinical design aspects, showing the reader how to consider the applications, and then build the coding representation. For me, one missing element was any connection to ML design or use in statistical applications, however, given the book’s basic nature, one can understand that element. Overall, a great guide to statistics, blending those statistics into Python, and using a biological studies format to demonstrate those elements. Read more

Amazon Verified review

Paul Pollock Dec 13, 2024

This book is a gem for anyone looking to bridge the gap between biostatistics and Python programming. As a professional in data science, I found its approach to explaining statistical concepts alongside Python implementation both clear and practical. The inclusion of real-world projects in biomedical and biotechnology domains makes it stand out as an applied learning resource.The author seamlessly introduces biostatistics concepts and methodically builds on Python's role in solving these problems. Each chapter is packed with hands-on exercises, from basic data cleaning to advanced topics like survival analysis and meta-analysis. The detailed examples using libraries like Pandas, SciPy, and Matplotlib are invaluable for both beginners and experienced programmers.I particularly appreciated the exemplar projects in cardiology and diabetes research, which are thoughtfully designed to reinforce learning. The book also provides step-by-step guidance on using tools like Jupyter Notebook and Spyder IDE, making it beginner-friendly.Overall, this book is an excellent resource for biologists, statisticians, and Python enthusiasts aiming to excel in biostatistics. Highly recommended! Read more

ivan Dec 15, 2024

Darko Medin's book Python for Biostatistics is designed to help readers easily understand how to use Python's ecosystem for biostatistical analysis. The author includes practical programming examples that cover exploratory data analysis, ANOVA, Student's t-test, and linear and logistic regression. Furthermore, the book features biotechnology projects introducing key concepts such as survival analysis with meta-analysis and causal inference. Overall, this book provides a solid introduction for anyone interested in utilizing Python for biostatistics. Read more

Om S Dec 22, 2024

I’m not directly in the biostatistics field, but this book showed me how Python can be used to solve real-world problems in biology and medicine. The examples are clear and easy to follow, from cleaning data to advanced topics like survival analysis. The hands-on projects helped me understand how to apply these techniques practically. It’s a good resource for anyone curious about using Python in life sciences! Read more

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

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

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What you will learn

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Contact Details

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