HYDROLOGIC DATA ANALYSIS

Prerequisites & course information

Requisites: ( MATH 221 or 217) and ( STAT 311 , 324 , 340 , MATH/STAT 309 , 409, 431 , B M E 325 , I SY E 210 , or E C E 331 ), graduate/professional standing, or member of Engineering Guest Students Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2024 Learning Outcomes: 1. Demonstrate data literacy in the context of temporal and geospatial hydrologic and environmental datasets Audience: Both Grad & Undergrad 2. Employ modern statistical software to analyze and model hydrologic and other environmental phenomena Audience: Both Grad & Undergrad 3. Detect trends, relationships, and patterns in temporal and spatial hydrologic and environmental datasets using regression, extreme value analyses, and other popular hydrologic statistical techniques Audience: Both Grad & Undergrad 4. Derive and interpret predictions, parameters, confidence levels, uncertainties, and sensitivities in the context of common hydrologic applications such as rainfall/flood frequency, hydropower, streamflow forecasting, and water quality Audience: Both Grad & Undergrad 5. Identify applications of data analysis methods to specific water resources engineering and other civil and environmental engineering applications Audience: Undergraduate 6. Identify and apply appropriate statistical analysis methods to novel environmental datasets and problems Audience: Graduate Learning Outcomes: 1. Demonstrate data literacy in the context of temporal and geospatial hydrologic and environmental datasets Audience: Both Grad & Undergrad 2. Employ modern statistical software to analyze and model hydrologic and other environmental phenomena Audience: Both Grad & Undergrad 3. Detect trends, relationships, and patterns in temporal and spatial hydrologic and environmental datasets using regression, extreme value analyses, and other popular hydrologic statistical techniques Audience: Both Grad & Undergrad 4. Derive and interpret predictions, parameters, confidence levels, uncertainties, and sensitivities in the context of common hydrologic applications such as rainfall/flood frequency, hydropower, streamflow forecasting, and water quality Audience: Both Grad & Undergrad 5. Identify applications of data analysis methods to specific water resources engineering and other civil and environmental engineering applications Audience: Undergraduate 6. Identify and apply appropriate statistical analysis methods to novel environmental datasets and problems Audience: Graduate