This course is designed for students to understand the general ideas and concepts of ecology, to be able to integrate information, formulate solutions, and solve ecological problems in modern life. We will emphasize connections with mathematical, physical, and chemical processes, as well as the application of ecology to conservation and global change issues.
By the end of the course, the successful student will:
- be familiar with fundamental ecological theories in order to understand and explain patterns observed in nature;
- learn ecological terminology and be able to use it in the proper context in order to communicate intelligently about natural systems;
- be able to critically evaluate primary ecological literature and interpret case studies in the context of ecological theory;
- be able to apply ecological theory to formulate solutions to modern conservation problems;
- be able to locate, read, and summarize primary scientific papers and clearly convey ideas and criticisms in writing.
This course will examine the causes and consequences of spatial patterns and processes in natural systems, as well as those dominated by human activities such as population, community, and ecosystem ecology. Find out why it matters to populations, communities, and ecosystem processes. Because landscapes are largely conventional definitions, this course will also integrate ecological processes from the local scale of field studies to their implications at the landscape scale and at levels where management and policy operate. The understanding is built on conceptual models and reinforced with biophysical, statistical, or ecological simulation models as appropriate.
Data Science for the Environment
Data science is rapidly changing the environmental scholarship. Environmental data is growing exponentially in size and quality at an unprecedented rate. The data deluge comes with new challenges of sifting, processing, and synthesizing large and diverse sources of information. In this course, students will learn the fundamental practices of environmental informatics mainly using the R programming language. The workshop-style class will include environmental-related modules; each module will introduce new datasets and questions, leading to new hands-on skills. Throughout the quarter, students will use these skills to find an environmental-related topic and complete an analysis paper.
This class is supported by DataCamp, the most intuitive learning platform for data science. Learn R, Python and SQL the way you learn best through a combination of short expert videos and hands-on-the-keyboard exercises. Take over 100+ courses by expert instructors on topics such as importing data, data visualization or machine learning and learn faster through immediate and personalised feedback on every exercise.
Design and Analysis of Biological Experiments
This course addresses how to set up and how to draw conclusions from biological experiments. It introduces basic theories in statistics, interwoven with data analysis using software packages. Students will learn to design statistically sound data collection in observational or experimental studies. To answer given research questions, students will choose among modern statistical tools and analyze data using software. Students will also learn to effectively present results using statistical graphics. This class particularly focuses on ecological and environmental data.