Course: Fundamentals of Data Science for Earth and Environmental Systems Science
Code: ES7023 & ES0002
Main instructor: David Lallemant
Academic Year: 2020-2021 - Second semester
Institution: Nanyang Technological University, Singapore
School: Asian School of the Environment
Objectives
Modelling using statistical learning and data science methods are powerful tools for earth and environmental systems science. 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 programming language). Such skills will prepare students for further research in earth and environmental systems, or careers in data-science.
Lectures
- Session 01 - 2021-01-15 - Introduction to R
- Session 02 - 2021-01-22 - R markdown
- Session 03 - 2021-01-22 - Git
- Session 04 - 2021-01-29 - Data wrangling - data
- Session 05 - 2021-02-19 - Data viz - data
- Session 06 - 2021-02-26 - Creating maps - data