Skip to main content

Data Science in Earth and Environmental Sciences


SyracuseUniversity

Problem Statement

This module is designed to introduce learners to the basics of R and Python programming as well as the application of emerging data analytics and machine learning methods in the Earth and Environmental Sciences.

Module Overview

Applying emerging data mining tools in resolving problems in Earth and Environmental Sciences

Topics Covered

(1) Basics of R coding in RStudio

(2) Basics of Python coding in Jupyter Notebook

(3) Analysis of driving forces of wildfire

(4) Analysis of impact of hydrocarbon production on groundwater quality

Prerequisites

N/A

Learning Objectives

At the end of this module, you should be able to describe and implement the steps involved in:

(1) reviewing and modifying others' codes in R and/or Python

(2) writing codes in R and/or Python

(3) developing and implementing a data science workflow for a data-driven project

(4) designing a data science project by conceptualizing a domain science problem

This will be accomplished through activities within each section. Results from each activity will be recorded in specified results templates. The results templates for each activity can be found at the beginning of each activity. The results templates are organized such that results from one activity can easily be used in successive activities.

Course Authors

Course Staff Image #2

Tao Wen

Assistant Professor, Syracuse University

Contact: twen08@syr.edu
Course Staff Image #1

Christina Bandaragoda

Senior Research Scientist, University of Washington

Contact: cband@uw.edu
Course Staff Image #1

Lucas Harris

Post-doctoral scholar, Penn State University

Contact: lbh146@psu.edu

Target Audience

This module is designed to serve a broad mix of learners whose coding expertise ranges from beginner to expert level and whose geoscience-related research interests

Tools Needed

Computer with access to the Internet

CUAHSI HydroShare account (https://www.hydroshare.org/)

Expected Total Hours

A student can expect to complete this module with approximately 30 work hours

Enroll