Computing and Data Analysis for Environmental Science

Environmental challenges require precise, data-driven solutions. Understanding how to process, model, and interpret complex environmental datasets is an essential skill for modern scientists and engineers. This course provides a structured path to mastering data analysis and computational modeling within the context of environmental science and civil engineering. You will transition from understanding core statistical concepts to writing clean, reproducible data analysis workflows. What you'll learn: - Understand foundational environmental data structures, measurement errors, and spatial-temporal variables - Apply statistical methods to analyze climate, water quality, and ecological datasets - Build predictive models using linear regression and time-series analysis for environmental forecasting - Practice modern data workflows including data cleaning, handling missing values, and version control basics - Interpret model outputs to make informed, data-backed environmental policy and engineering recommendations - Implement modern computational practices using structured programming and tidy data principles The course begins with foundational terminology, basic environmental parameters, and core statistical concepts before moving into practical data manipulation, modeling, and analysis techniques. It is designed for beginners, students, and professionals in environmental science, civil engineering, or sustainability who want to build a strong computational foundation. No prior programming or advanced statistical experience is required to begin.