Winter 2023 Class Schedule
|Course #||Course Title||Instructor||Day/Time||LAB|
|202||Health of the Biosphere||Walsh||TTh 12:30-1:50 PM|
|390-0-01||Topics: Geographic Information Systems (GIS) Level 1||Anderson||M 2:00-3:20 PM||W 2:00-3:20 PM|
|390-0-03||Topics: R Data Science||Anderson||M 10:00-11:50 AM||W 10:00-11:50 AM|
This course studies the growth of populations and their interactions in ecological communities. Topics include: the ecological niche; projections of population growth, including the history of human growth, harvesting populations, and population viability analysis of endangered species; interactions among species, including competition, predation, and disease transmission; measuring the diversity of ecological communities; the effects of diversity on energy flow. More advanced topics will also be addressed, including the biodiversity-stability relationship, the economic values of biodiversity and ecosystem function, and the biology and management of metapopulations in fragmented habitats.
Introduction to concepts underlying geographic information systems (GIS) and methods of managing and processing geographic information. Designed for students who have little background but want to learn the fundamentals and applications of GIS. Students will be exposed to both theoretical knowledge and technical skills in this course. Lab assignments and a project will promote students’ application of concepts and skills in solving real-world problems.
As we are in the era of ‘big data’, the quantity and quality of data available for environmental, ecological and earth science research has exploded over the past few decades. The free and open-source R programming language has become a powerful tool in data analysis in scientific research. This course offers an introduction to the fundamentals of data science using the programming language, R. The course contents span from basic R programming skills to advanced skills including data management, visualization and analysis of spatial data such as weather and satellite imagery data. By conducting hands-on exercises and an extensive project, students will develop dynamic and reproducible outputs based on their own fields of interests. This course does not require prior coding experience.