Fundamentals of Data Science for Earth and Environmental Systems Science

Course Code: ES0002
Course Description:
Modeling, simulation, statistical learning and data science methods are powerful tools for earth and environmental systems sciences. This course will cover the major concepts for building and evaluating models, including fundamentals of statistical and machine learning. Topics covered include (1) basic concepts and tools in data science, (2) statistical thinking, (3) goals and principles of scientific modeling, (4) model development, (5) model calibration and selection, (6) sensitivity analysis, (7) model evaluation, (8) model predictions, (9) results visualization and communication. Students will gain hands-on experience in developing models and simulations (using R programing language).


Semester Offered: Semester 2

Pre-requisites: MH1800 Calculus for the Sciences, ES2001 Computational Earth Systems Science

Course OBTL

Course coordinator: Assistant Professor David LALLEMANT
Office Location: N2-01c-45