The September Environmental Data Science Bootcamps introduce relevant computational and statistical tools. They are intended to give new and continuing PhD students a jumpstart into the practical skills needed for conducting research. The courses are hosted and organized by our current DSEER fellows. Lessons are applicable to a wide range of fields, but examples will be drawn from environmental sciences.

Our program includes the following sessions:

  • Introduction to Scientific Programming [Python workshop]: For those new to programming, a Python course in the basics to get you up to speed: variables, arrays, list, for loops, if statements, functions, and how to work with NumPy, Pandas, and Matplotlib for basic data sciences purposes.
  • Computing for Research [Python workshop]: For those who already know the programming basics, this Python course will dive into more advanced computational methods, including data exploration and visualization, using the computing cluster, version control software (Git), and working with geodata formats.
  • Statistics for Research [R workshop]: This R course provides a broad overview of useful statistical techniques used in scientific research. Over the course of the bootcamp, we will develop a toolkit of methods students will likely need to complete their own research. Topics include modern approaches to regression, time series modeling, and Bayesian statistics. The focus is on practical application and therefore will include extensive coding exercises.
  • Life During Grad School [Panelist series]: Starting grad school is always a big leap. This series aims to help incoming PhD students make the transition. The first three days will be panels where senior grad students and faculty will discuss topics like navigating the student-advisor relationship and work-life balance. The last day will be an overview of campus resources and useful tools to help make grad school easier. Snacks will be provided!
  • Demystifying Machine Learning [Python mini workshop]: This session aims to make machine learning more approachable by removing some of the mystery. We will be diving into random forests and neural networks. Note that you must be registered for another bootcamp course to join. People from both coding tracks are welcome! This course is intended to bring everyone together as we wrap up our bootcamps.

Past Bootcamp Materials

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