Environmental Data Analysis and Computing with MATLAB

Environmental science and engineering rely heavily on translating raw field and lab measurements into actionable insights. Understanding how to manage uncertainty and analyze data programmatically is a fundamental skill for any modern environmental researcher or practitioner. In this text-based course, you will learn how to apply probability concepts and computational tools to real-world environmental datasets. You will transition from manual calculations to writing clean, structured MATLAB code that automates data visualization, tests scientific hypotheses, and models environmental systems. What you'll learn: Understand core probability concepts to systematically describe and manage uncertainty in environmental systems; Write MATLAB scripts to load, clean, and organize raw field and laboratory data; Create clear quantitative data displays to identify trends and anomalies in ecological datasets; Apply statistical hypothesis testing to evaluate environmental claims and research questions; Identify and analyze causal relationships between multiple environmental variables; Model environmental processes using computational simulations and virtual experiments. The course begins with foundational definitions of probability and uncertainty before moving into hands-on MATLAB scripting techniques. You will read through practical explanations, analyze real-world case studies, and practice your skills with written coding and analytical exercises. This course is designed for beginners in environmental science, civil engineering, or ecology who want to build their computational skills from scratch, with no prior programming experience required. Start learning how to turn raw environmental observations into meaningful scientific discoveries today.