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Using R for Geostatistical Geospatial Modeling is the second course in a series of three, designed to teach you the art and science of geostatistical geospatial modeling. In this course, you will delve into the world of geostatistical geospatial modeling, using R, a widely-used interpretive coding language that is both user-friendly and immensely powerful.

This course builds upon the foundations laid in Course #1, Basic Principles of Geostatistical Modeling, taking your understanding of geostatistical geospatial modeling to the next level. You will learn how to code basic machine learning algorithms and geostatistical spatial models to solve real-world problems. By the end of this course, you will have a solid understanding of the principles and techniques of geostatistical geospatial modeling, and be able to apply them in your own work with confidence.

Case Studies in Geostatistical Modeling is the third course in a series of three, designed to teach you the art and science of geostatistical geospatial modeling. In this course, studentspresent their final geostatistical analyses to faculty, demonstrating practical applications and undergoing critique. This course builds on Course One鈥檚 foundational principles and Course Two鈥檚 structured workflow, emphasizing execution, interpretation, and evaluation.

Students apply geostatistics across five diverse projects: Environmental, Oil and Gas, Photo-Voltaics, Material Sciences, and Mining. This capstone course provides hands-on experience, reinforcing geostatistical adaptability across disciplines. By presenting and defending their work, participants refine their technical communication skills and gain insights into real-world geostatistical applications.