Statistics and R for the Life Sciences

Analyzing complex datasets in the life sciences requires a solid grasp of both statistical theory and programming. This text-based course bridges the gap, helping you build a strong foundation in statistics while learning to code in R. You will transition from a beginner to confidently writing R code to import, clean, analyze, and visualize scientific data, while mastering critical statistical tests and learning how to interpret your results accurately and responsibly. What you'll learn: - Understand foundational statistical concepts, probability distributions, and hypothesis testing - Write clean, modern R code using the tidyverse ecosystem for data manipulation - Create publication-ready data visualizations using modern plotting libraries - Apply parametric and non-parametric statistical tests to experimental data - Interpret statistical significance, confidence intervals, and effect sizes accurately - Organize and document your data analysis workflows for reproducibility The course begins with essential statistical definitions and basic R syntax before guiding you through data manipulation, visualization, and practical hypothesis testing. You will learn through clear written explanations, code examples, and conceptual exercises designed to reinforce your understanding. Designed entirely for beginners, this course requires no prior programming experience or advanced mathematical background. Start reading today to unlock the power of statistical computing in your scientific journey.