Point of contact: Dr Brett Melbourne
Pronouns: he, him, his
email: [email protected]
Office: Ramaley N336
Office hours: Monday 10-12
Contacting me: I prefer email
Course: Thursday 9:30-11, Ramaley N240
This course will cover data science approaches for synthesizing, integrating, and analyzing multiple data sources to forecast ecological change. It's a semester long hackathon - hands-on and project-based. Participants will form (potentially interdisciplinary) groups to complete a forecasting project. Datasets will range from field observations to remote sensing and include the National Ecological Observatory Network (NEON), Niwot Long Term Ecological Research (LTER), or other data you might be interested in. We will explore quantitative methods for forecasting appropriate to projects at hand, such as hierarchical models, time series analysis, and machine learning. Data wrangling, visualization, and code management will be critical components too. We will also engage with the emerging community around ecological forecasting, and strive toward results that we could later publish.
This is a learning-by-doing community of faculty and graduate students based around some real, messy projects using NEON and LTER data.
You should be comfortable around data and coding and be prepared to work in a group. If you have taken a graduate level quantitative class of some kind, you should have skills to contribute. If you're not sure - just ask.
Work as a team to produce a forecast using current best practices.
This is a hackathon. You should bring a laptop to every class with the necessary tools. If you don't have access to a laptop please let me know as soon as possible. We can totally work something out.
Google Drive - University of Colorado only
Let's discuss this. Here is one idea:
Make one tutorial, on a topic to be determined, prepared as a group: 20%
Forecast project Github repository, developed as a group: 80%
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