Kick off the summer with a two-week intensive seminar that includes:
- Field trips to learn about local conservation issues
- Social events to meet members of our professional network
- Classroom lectures and discussions to learn about the relevance of observational data for a variety of environmental topics.
Then switch gears for your technical courses in remote sensing and statistics that give you a solid foundation in using and interpreting environmental data. As a bonus, the summer also includes a multi-day hands-on workshop on working with geospatial data in R offered with the UW-Madison Data Science Hub.
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Environmental Leadership Seminar (1 credit)
The use of observational data in conservation problem solving is increasing in both scale and complexity. In this course, students in the Environmental Observation and Informatics MS program will begin to explore how data is used in conservation, while exploring the professional skills that are required for working successfully in data and conservation.
Through course readings, class discussions, and case studies, students will strengthen their understanding of how data links to various conservation scenarios, while beginning to develop the diverse professional skills required to build, manage, and lead effective conservation program teams.
Environmental Monitoring Seminar (2 credits)
Explores current topics in the “informatics” sector, while addressing science-based decision-making to address these global challenges. This seminar also begins focused planning for the summer placement proposal and work plan development.
Fundamentals of Environmental Remote Sensing (3 credits)
Introduction to the Earth as viewed from above, focusing on use of aerial photography and satellite imagery to study the environment. Includes physical processes of electromagnetic radiation, data types and sensing capabilities, methods for interpretation, hard copy and digital analysis, pattern recognition and thematic mapping, and applications drawn from forestry, agriculture, urban areas, grasslands, geology, archaeology, and wetland environments.
Statistics: Foundations and Regression With Big Data (2 credits)
A review of foundational statistical concepts such as summarizing data with graphs and statistics and sampling from a population. The lessons will build on these foundations with calculating confidence intervals, performing hypothesis testing, and evaluating model assumptions. Finally, students will gain experience using statistical software to aid in data analysis, drawing conclusions from the analysis, and communicating results. Lessons and activities are taught in the programming language, R.
Geospatial Data Carpentry workshop
Special seminar with leading conservationists