ENVIRONMENTAL DATA SCIENCE BOOTCAMPS

We’re hosting our 5th annual “Environmental Data Science Bootcamps” (dates TBD)! These free bootcamps will be occurring either in-person or hybrid.

The September 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.

The calendar listed below is from the 2021 Bootcamps. 

Our 2022 program includes the following sessions:
 

  • Introduction to Scientific Programming (Aug 29 – Sep 14, 9:30 am – 12:30 pm CT): For those new to programming, a crash 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. This course will be taught in Python + Jupyter notebooks. It is intended for graduate students who have little or no computer programming experience.

     

  • Computing for Research (Aug 29 – Sep 7, 9:30 am – 12:30 pm CT): For those who already know the programming basics, this course will dive into advanced computational methods, including data exploration and visualization, numerical methods, using the computing cluster, optimizing your code, and working with geodata formats. The course will be taught in Python + Jupyter notebooks. It is intended for graduate students with some programming experience in Python or similar languages who can make a fast transition to more advanced data science practices.

     

  • Statistics for Research (Sep 8 – Sep 14, 9:30 am – 12:30 pm CT): This course provides an introduction to some basic statistical techniques used in environmental research and beyond. We will give students a toolkit of methods to understand datasets they may encounter in their own work. We will cover modern approaches to regression and inference, time series modeling, and Bayesian statistics. The focus is on practical applications and will include extensive coding exercises. The course will be taught in R. The first lesson will help students who know Python or similar languages make the transition.

     

  • Life During Grad School (Sep 12 – Sept 15, Time TBD): Starting grad school is always a big leap. This week-long series aims to help incoming PhD students make the transition. The first three days will be panels where current graduate students and faculty will discuss topics like “what is graduate school anyway?”, “navigating your research and the advisor-student relationship”, and “the importance of mental health”. The last two days will be an overview of resources available on campus and other useful tools for your workflow that will help make grad school easier.

  Applications will be released soon! Decisions will then be sent out once the application deadline has passed.

Announcement Flyers: