ENVS 1300

Environmental Data Science

Course Description

Course information provided by the 2026-2027 Catalog.

This course introduces the fundamentals of data relevant in environment and sustainability, including natural sciences and social sciences. Students will gain skills to visualize, quantitatively interpret, communicate, and manage relevant data. The course begins with the basic question “what are data?” and culminates in a final team project developed around student interests to answer urgent questions in environmental science today. Projects may focus on threats to biodiversity, impacts of changes in soil moisture, public attitudes about data centers, etc. Students will present their projects at the end of the semester, using skills learned in the course for data analysis, visualization, and communication. This course is designed to be complementary to a lower-level statistics course, and to prepare students for upper-level classes in environmental science disciplines.

Last 4 Terms Offered (None)

Learning Outcomes

• Define and differentiate qualitative and quantitative data, identify reputable data sources for questions of interest, and explain how bias can arise in data and data collection. Along with sources of data, students will identify strengths and limitations of the different data sources and types.
• Apply methods to clean data and handle missing data, and apply multiple visualization methods to explore data and explain how visualization choices influence interpretation. Students will use tools including Excel, Python, or R.
• Interpret patterns and trends in datasets, including distinguishing consistent long-term behavior from short-term variation or noise.
• Compute and interpret basic descriptive statistics including mean, variance, standard deviation to summarize and analyze datasets. Explain the role of statistical hypothesis testing and interpret results presented in standard formats to assess claims of statistical significance.
• Identify sources of error in data and quantify uncertainty in both raw data and statistical estimates.
• Communicate data-driven insights and limitations of statistical analyses to both technical and non-technical audiences.

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