New Classes and Short Courses
CAS 6001 – Interdisciplinary Climate Adaptation Science Research Colloquium (x2)
CAS 6002 – Climate Adaptation Science Studio 1
CAS 6003 – Climate Adaptation Science Studio 2
CAS 6006 – Science Communication Capstone
BIOL/CAS 6750 – Programming for Biologists
Useful Short Courses
CAS 6888 – Leadership and Followership (0.5 credit)
CAS 6889 – Environmental Risk and Decision Making (0.5 credit)
Principal Components Analysis, Machine Learning, and Big Data-contact Will Pearse
WATS 6900 – Sediment Transport in Stream Assessment and Design Workshop (variable credit)
WILD 5680/4580/6580 – baseR: Management and Manipulation of Ecological Data Using R (1 credit)
This course provides instruction on the underpinnings of the R computing and statistical environment, as well as how to manage and manipulate data in the R environment. It is online and self-paced.
WILD 6900 – Species distribution and habitat association models using R A 1-week 40-hr workshop
This will either be held at the end of Fall 2017 or over spring break 2018. haven’t decided yet; date contingent on students. Can be taken for credit as a directed study if desired.
WATS 6491 – Streamflow Analysis (1 credit)
This course will provide theory and applications for analyzing streamflow data, focusing on the scale of small wildland watershed. We will use conceptual and mathematical models to understand hydrologic processes and quantitatively predict stocks and fluxes of water.
BIOL/PSC 6950 – Lay Science Communication (2 credits)
Graduate students learn to communicate their research to lay audiences by identifying the audience and appropriately tailoring the most relevant points of that research.
BIOL/CAS 6750 – Statistical Espresso for Biologists (1 credit)
BIOL 6750 – Writing Workshop: Dissertation Improvement Grants (1-3 Credits)
STAT 5810 (004) – SAS Certification (2 credits)
STAT 5810 (002) – Statistical Learning and Data Mining I (2 credits)
STAT 5810 (003)/6910 (004) – Statistical Methods for Big Data (2 credits)
STAT 6910 (005) – Statistical Learning and Data Mining II (2 credits)