Geoscience Data Analytics

Catalog description

Modern geoscience careers require students to be versatile in managing and analyzing data, solving quantitative problems, comfortable in statistical analysis and projection, and adept in presenting numerical interpretations to stakeholders. The proposed course will provide students with the numerical skills to be successful in their undergraduate careers and in the workplace and will serve as entry for more advanced quantitative courses in Geosciences and the College of Earth and Mineral Sciences. The course has five major objectives: (1) To give students an overview of the different types of geoscience data and the skills to organize, manipulate and structure them appropriately for conducting simple analyses including regression, import/export, conditional subsetting, creating and using database structures and design, queries, and metadata; (2) To train students in the fundamentals of a widely used programming language (e.g., Python, Matlab), including variables, functions, loops, boolean logic, and arrays; (3) To teach students how to use programming skills to conduct a range of basic numerical and statistical analyses; (4) To show students how to integrate and analyze several related datasets in solving complex geoscience problems; and (5) To train students how to summarize and present data in an effective manner, including appropriate data visualizations, and to communicate interpretations to stakeholders. Instruction will consist of demonstrations followed by hands-on activities in which students learn skills on laptops. Assessment will include these activities and follow-on homework problems. In addition, students will conduct a capstone project in the last few weeks of the course in which they integrate several data sets to interpret a complex geoscience problem. The course map is designed to reinforce key concepts and skills through scaffolding: Unit 2 applies the principles of geoscience data and data analysis introduced in Unit 1 in a basic programming environment. Unit 3 reinforces the programming concepts from Unit 2 while developing more advanced data analytics skills. Unit 4 applies and synthesizes the concepts and skills covered in the previous three units in the completion of a capstone project.