1. Introduction to Biostatistics
What biostatistics is, why biological data needs its own statistical toolkit, and the core methods — descriptive statistics, inference, and hypothesis testing — that every biotechnology student must know.
What biostatistics is, why biological data needs its own statistical toolkit, and the core methods — descriptive statistics, inference, and hypothesis testing — that every biotechnology student must know.
How to choose and apply the core statistical methods for biological data — t-tests, ANOVA, confidence intervals, and regression — with worked Python examples.
Comprehensive guide to applications of biotechnology and bioinformatics in various fields
The bioinformatics data analysis pipeline — preprocessing, exploratory analysis, statistical testing, and machine learning — applied to large-scale biological datasets like gene expression data.
A comprehensive guide to bioinformatics tools and software for students and beginners