EBOOK
About
Learn to analyze biological data in both Python and R-even if you have never written a line of code.
Modern biology runs on data. Whether you are sequencing genomes, surveying ecosystems, or measuring gene expression, turning messy spreadsheets into clear answers is now as essential a lab skill as the pipette. Working with Biological Data in Python and R is the hands-on, beginner-friendly introduction written specifically for biology students and researchers.
This is the rare bioinformatics textbook that teaches Python and R side by side, so you learn each idea once and immediately see it done in both languages. Every chapter is built on real biological examples-fish, plants, microbes, genes, and DNA-with simulated datasets, ready-to-run scripts, and exercises so you practice as you read. No prior programming or statistics experience is required.
Across 20 approachable chapters you will learn to:
• Set up your computer and write your first scripts in Python and R
• Read, clean, and explore messy real-world data with pandas and the tidyverse
• Build clear, publication-quality data visualization with ggplot2, matplotlib, and seaborn
• Apply core biostatistics-comparing groups, correlation, and regression-and sidestep the most common mistakes
• Work with DNA and protein sequences in Biopython, plus genome annotation and key file formats (FASTA, FASTQ, VCF)
• Run a complete RNA-seq differential expression analysis with DESeq2 and interpret the results
• Measure biodiversity with vegan and scikit-bio, and read phylogenetic trees with ape and ete3
• Take your first steps in machine learning for biology with scikit-learn
• Make your work reproducible with notebooks, Git, and GitHub
By the end you will not just follow recipes-you will be able to ask your own biological questions and answer them with data.
Ideal as an undergraduate bioinformatics and biostatistics textbook, a self-study guide for life scientists, or a practical desk reference for anyone making the leap into computational biology and data analysis. Clear explanations, fully worked dual-language code, and real datasets make it the friendliest on-ramp to coding in the life sciences.
Open the book, open your data, and start coding today.
Modern biology runs on data. Whether you are sequencing genomes, surveying ecosystems, or measuring gene expression, turning messy spreadsheets into clear answers is now as essential a lab skill as the pipette. Working with Biological Data in Python and R is the hands-on, beginner-friendly introduction written specifically for biology students and researchers.
This is the rare bioinformatics textbook that teaches Python and R side by side, so you learn each idea once and immediately see it done in both languages. Every chapter is built on real biological examples-fish, plants, microbes, genes, and DNA-with simulated datasets, ready-to-run scripts, and exercises so you practice as you read. No prior programming or statistics experience is required.
Across 20 approachable chapters you will learn to:
• Set up your computer and write your first scripts in Python and R
• Read, clean, and explore messy real-world data with pandas and the tidyverse
• Build clear, publication-quality data visualization with ggplot2, matplotlib, and seaborn
• Apply core biostatistics-comparing groups, correlation, and regression-and sidestep the most common mistakes
• Work with DNA and protein sequences in Biopython, plus genome annotation and key file formats (FASTA, FASTQ, VCF)
• Run a complete RNA-seq differential expression analysis with DESeq2 and interpret the results
• Measure biodiversity with vegan and scikit-bio, and read phylogenetic trees with ape and ete3
• Take your first steps in machine learning for biology with scikit-learn
• Make your work reproducible with notebooks, Git, and GitHub
By the end you will not just follow recipes-you will be able to ask your own biological questions and answer them with data.
Ideal as an undergraduate bioinformatics and biostatistics textbook, a self-study guide for life scientists, or a practical desk reference for anyone making the leap into computational biology and data analysis. Clear explanations, fully worked dual-language code, and real datasets make it the friendliest on-ramp to coding in the life sciences.
Open the book, open your data, and start coding today.