Beginner-friendly resources to help you dive into data science and modeling techniques:
An overview of Geographic Information Systems (GIS), explaining their importance and how they’re used to analyze and visualize spatial data in real-world applications.
A practical guide for getting started with QGIS, an open-source alternative to proprietary GIS tools, ideal for users seeking accessible and free technology for spatial analysis.
A comprehensive introduction to GIS fundamentals, including essential tools, concepts, and hands-on exercises, provided by MIT OpenCourseWare.
A comprehensive introduction to R programming, covering essential concepts like data manipulation, visualization, and statistical analysis, perfect for those new to data science.
Learn the basics of Python programming, focusing on data manipulation, visualization, and simple statistical analyses.
Learn how to get started with the Jupyter Notebook, an open source web application that allows you to create and share documents that contain live code, equations, visualizations, and text.
Take your data analysis skills to the next level with these intermediate resources:
Learn to create professional, publication-ready data visualizations with ggplot2 in R, focusing on practical examples and clear, hands-on instructions.
A comprehensive online textbook that introduces Python programming for environmental and earth science applications, focusing on open-source tools and real-world data analysis workflows.
This comprehensive textbook provides R programming tutorials for geospatial sciences, originally developed for the University of Delaware’s Department of Geography and Spatial Sciences 473/673 course. It covers data ingestion, quality control, reprojection, publication-quality visualization, and machine learning techniques, designed to take students from beginner to proficient in geospatial analysis with R.
Resources for tackling complex data challenges and advanced workflows:
Learn foundational machine learning techniques using Python, focusing on real-world applications such as environmental and earth science data analysis.
A comprehensive online textbook that introduces Python programming for environmental and earth science applications, focusing on open-source tools and real-world data analysis workflows.
by United States Department of Agriculture (USDA) and United States Forest Service (USFS)
by United States Department of Agriculture (USDA) and United States Forest Service (USFS)
Dr. McGarigal discusses the landscape as a human construct and a “lens” through we look at patterns and processes in the real world from a particular perspective.
Dr. McGarigal discusses the scale-dependency of pattern and process and the landscape implications.
Dr. McGarigal discusses landscapes as dynamic in space and time and the implications for understanding and managing landscapes.
Dr. McGarigal argues that a landscape has to be defined based on a specific pattern-process question, so asking the right pattern-process question is the most important step in any landscape assessment.
Dr. McGarigal discusses the essential steps involved in formally defining a landscape based on the specific pattern-process question.
Dr. McGarigal discusses the importance of landscape definition and why it is essential to any meaningful landscape application.
Discover tutorials focusing on specific tools and technologies used in the environmental data science space:
Explore how to use GitHub for collaboration in open science, focusing on version control, team workflows, and reproducible research practices for environmental and geospatial data projects.
