This course provides critical skills and foundational knowledge for graduate students and early career scientists working with heterogeneous spatio-temporal data to address ecological questions. Our 2017 Institute focuses on remote sensing of vegetation using open source tools and reproducible science workflows – the primary programming language will be Python.
Apply now for this nine-day course on Bayesian models
The National Socio-Environmental Synthesis Center (SESYNC) will host a nine-day short course August 15 - 25, 2017 covering basic principles of using Bayesian models to gain insight from data.The goals of the course are to:
New seats opened for the 2017 Data Institute in Remote Sensing!
Applications reviewed as they are received!!! Application closed 25 May.
The 2017 Data Institute in Remote Sensing with Reproducible Workflows provides a unique opportunity for participants to gain hands-on experience working with open data using well-documented reproducible methods. Participants will also gain important applied knowledge about using heterogeneous remote sensing data sources to answer spatio-temporal ecological questions.