Course Description

This course is intended for graduate students or highly qualified undergraduates. The overall objective of this course is to teach students how to conduct data analyses and modeling experiments to produce Findable, Accessible, Interoperable and Reusable (FAIR) science in hydrological, weather and climate studies. Students will be exposed to the philosophy behind FAIR principles, cyberinfrastructure resources for accessing data, community-based computing tools, computer programming skills, and publication of data and analysis results. Utilizing scaffolding and templates provided in lecture and via online resources the students will apply what they have learned in authentic learning exercises appropriate for research settings in hydrological, weather and climate sciences.

Prerequisites 

It is expected that students have knowledge of either hydrology, weather or climate science and related computational tools, and familiarity with the principles and application of statistics and data science at or beyond the advanced undergraduate level. Programming skills in interpreted and statistical languages such as Python, Matlab, or R will be required to complete the course successfully.

Learning Outcomes

After taking this course, students will be able to: (i) obtain and process hydrologic, weather and climate model data using public domain resources; (ii) Perform geospatial and time series analysis of this data using publicly available cyberinfrastructure tools; (iii) Process model data/output using publicly available toolkits; and (iv) publish data and its results in public domain for general access and reproducibility.

Grading

 20% for class participation, 40% for assignments/homework and 40% for final project.

Required Texts/Resources

No text is required for this course. All reading material will be provided by the instructor. 

Date Modified: 07/2016

Site is under construction and will be finished by August 2016