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GEOSCI 353

PROGRAMMING FOR EARTH SCIENTISTS

3 credits.

Introduction to scientific programming with a focus on geoscience applications, utilizing Python as the primary programming language. Focus on gaining practical skills applicable to geoscience datasets such as analytical model implementation, time series analysis, and geospatial data visualization. Includes elementary topics in numerical computing and machine learning. The use of hands-on exercises, real-world datasets, and collaborative projects will be used to explore how to address geoscience problems with computational solutions.

Prerequisites & course information

Requisites: MATH 222 or graduate/professional standing Course Designation: Core GenEd - Natural Science & Wellness + Lab Level - Intermediate L&S Credit - L&S Liberal Arts and Science Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Learning Outcomes: 1. Write, debug, and optimize algorithms for solving problems in geoscience. Audience: Both Grad & Undergrad 2. Process, analyze, and interpret geospatial and time-series data to solve problems involving the timing of geologic events or environmental processes. Audience: Both Grad & Undergrad 3. Create effective visualizations of geoscientific spatial and temporal data. Audience: Both Grad & Undergrad 4. Efficiently employ AI tools to support programming tasks, and critically evaluate the accuracy, efficiency, and scientific validity of AI-generated code or analyses. Audience: Both Grad & Undergrad 5. Identify, implement, and evaluate machine learning approaches to classify, cluster, or predict geoscientific phenomena using large datasets. Audience: Both Grad & Undergrad 6. Apply fundamental numerical computing techniques to model and simulate Earth systems. Audience: Both Grad & Undergrad 7. Apply machine learning algorithms to a scientific problem in your area of study to analyze a scientific process. Audience: Graduate Learning Outcomes: 1. Write, debug, and optimize algorithms for solving problems in geoscience. Audience: Both Grad & Undergrad 2. Process, analyze, and interpret geospatial and time-series data to solve problems involving the timing of geologic events or environmental processes. Audience: Both Grad & Undergrad 3. Create effective visualizations of geoscientific spatial and temporal data. Audience: Both Grad & Undergrad 4. Efficiently employ AI tools to support programming tasks, and critically evaluate the accuracy, efficiency, and scientific validity of AI-generated code or analyses. Audience: Both Grad & Undergrad 5. Identify, implement, and evaluate machine learning approaches to classify, cluster, or predict geoscientific phenomena using large datasets. Audience: Both Grad & Undergrad 6. Apply fundamental numerical computing techniques to model and simulate Earth systems. Audience: Both Grad & Undergrad 7. Apply machine learning algorithms to a scientific problem in your area of study to analyze a scientific process. Audience: Graduate

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