Curriculum

Data Foundations

Build a foundation in environmental data science by learning to define research questions, analyze environmental datasets, and develop computational thinking. Gain hands-on experience with R, Tidyverse workflows, data visualization, statistics, and an introduction to Python for multi-language data analysis.

Introduction to Environmental Data Science

• Introduction to the foundations of environmental data science, including problem definition, exploratory data analysis, and computational thinking.

• Learn to identify key features of environmental datasets and develop strategies for data-driven inquiry.

Foundational Data Analysis in R & Tidyverse

• Build foundational programming skills in R and learn core Tidyverse workflows for data manipulation and analysis.

• Practice writing, reading, and applying code to prepare and explore environmental datasets.

Data Management, Statistics, and Visualization

• Develop skills in organizing datasets, calculating descriptive statistics, and creating effective data visualizations.

• Learn how to communicate insights clearly through structured data analysis and graphical representation.

Introduction to Python

• Gain an introduction to Python programming and its applications in environmental data science.

• Understand similarities between Python and R while building a foundation for multi-language analytical workflows.

Data Stewardship

Build core skills in managing, organizing, and sharing environmental data using R and Python. Learn to find and assess open datasets, apply preprocessing and integration techniques, and use reproducible workflows, version control, and data governance practices to support transparent, collaborative, and long-term environmental data management.

Preparing and Integrating Environmental Data

• Learn how to locate, evaluate, and prepare environmental datasets for analysis.

• Develop skills in assessing data quality and bias, applying preprocessing techniques, and integrating datasets using R and Python workflows.

Version Control, Reproducibility, and Collaboration

• Explore tools and practices that support reproducible and transparent environmental research.

• Learn to manage versions, track changes, and collaborate effectively on coding projects.

Data Management, Organization, and Archiving

• Develop best practices for organizing, documenting, and preserving environmental data throughout its lifecycle.

• Learn strategies for file management, data governance, and archiving to ensure datasets remain accessible and reusable over time.

Data Cleaning & Analysis

Develop practical skills in cleaning, exploring, and analyzing environmental datasets using R. Learn to identify data-quality issues, apply statistical methods, evaluate relationships and distributions, and prepare real-world data for reproducible, responsible environmental analysis.

Foundations of Data Cleaning and Exploratory Analysis

• Develop foundational skills in assessing data quality through exploratory analysis and statistical reasoning.

• Learn to identify missing data, anomalies, bias, and variation while applying data-cleaning techniques to improve dataset reliability.

Visualizing and Modeling Relationships Between Variables

• Explore methods for visualizing and interpreting relationships between quantitative variables.

• Learn to apply correlation, regression, and diagnostic techniques to evaluate patterns, identify limitations, and strengthen analytical results.

Regression Foundations, Normality Assessment, and Advanced Data Cleaning

• Build a deeper understanding of statistical concepts that support regression and correlation analyses.

• Develop advanced data-cleaning skills, including handling missing data, validating distributions, transforming variables, and preparing complex datasets for analysis.

Spatial Analysis for the Environment

Build foundational skills in spatial data analysis using R by working with raster and vector datasets, coordinate systems, and map projections. Learn to visualize, manipulate, and integrate spatial data to investigate environmental change and support geographic decision-making.

Introduction to Spatial Data and Environmental Mapping

• Develop foundational skills for working with spatial data in R, including vector and raster data structures, coordinate reference systems, and spatial visualization.

• Apply core geospatial workflows to load, map, and analyze environmental data using real-world datasets.

Applied Spatial Analysis and Integration of Environmental Datasets

• Integrate vector and raster data to analyze spatial relationships using joins, buffers, masking, and zonal statistics.

• Build reproducible spatial analysis workflows and create clear maps using sf and ggplot2.

Text Analysis for the Environment

Learn to transform unstructured environmental text into actionable data using natural language processing (NLP) techniques. Gain hands-on experience with text cleaning, topic modeling, text classification, embeddings, and transformer-based models to analyze and predict environmental themes from large-scale text datasets.

Foundations of Text as Data for Environmental Research

• Transform unstructured text into analyzable data using cleaning, tokenization, sentiment analysis, and frequency-based methods.

• Apply text-as-data techniques including term frequency, document-term matrices, and topic modeling to extract patterns from environmental text.

Machine Learning and Modern NLP for Environmental Text Analysis

• Build supervised machine learning models for environmental text using feature representations such as bag-of-words, TF-IDF, and topic proportions.

• Train, evaluate, and interpret classification models while understanding embeddings and transformer-based approaches in modern NLP.

Data Communication

Learn to transform environmental data into meaningful impact through effective visualization, dashboard design, and audience-focused communication. Develop skills in data storytelling, ethical data practices, and collaborative approaches that engage diverse stakeholders and support informed environmental decision-making.

Communicating Environmental Data for Impact

• Learn strategies for communicating environmental data to inform decisions and inspire action.

• Develop skills in audience analysis, message framing, ethical data communication, and stakeholder engagement to translate data into meaningful impact.

Using Dashboards and Toolkits to Drive Decision-Making

• Explore how dashboards and communication tools support operational and environmental decision-making.

• Learn to design accessible, actionable dashboards and apply practical frameworks to communicate data clearly and effectively.

Advanced Data Visualization Strategies

• Develop advanced data visualization skills grounded in human-centered design and accessibility principles.

• Learn to evaluate, customize, and deliver visualizations that effectively communicate insights across different audiences, platforms, and contexts.