For students new to programming, a crash course in the basics to get you up to speed: variables, arrays, list, for loops, if statements, and functions, and how to work with NumPy, Pandas, and Matplotlib for basic data sciences purposes in Python.
For those ready for more advanced data science: data structures, DASK dataframes and datasets, basics of spatiotemporal data, big data tips and tricks, version control software (Git), advanced plotting techniques, and statistical analyses in Python.
An introduction to basic statistical techniques used in environmental research and beyond, giving students a toolkit of methods to understand datasets in their own work: regression and inference, time series modeling, and Bayesian statistics.
Starting grad school is always a big leap. View responses from faculty, PhD students, post docs and research staff to questions such as: “What does success in grad school look like?” and “What advice do you have for managing your work schedule?”