e-Book Conclusions#
Applied Geostatistics in Python: A Hands-on Guide with GeostatsPy#
Michael Pyrcz, Professor, The University of Texas at Austin#
Twitter | GitHub | Website | GoogleScholar | Book | YouTube | LinkedIn#
I sincerely hope that this e-book has been helpful. To maximize your learning experience please remember to review the YouTube lectures linked to each workflow to better understand the essential theory and for guidance on best practice and possible applications.
This has been my first attempt to convert my repository of well-documented Python workflows, GeostatsPyDemos: GeostatsPy Python Package for Spatial Data Analytics and Geostatistics Demonstration Workflows Repository [Pyr24c] into an online e-book. Given this source for the content of this e-book, there are natural consequences on the e-book scope, i.e., what is included and what is not? All the workflows demonstrate the functionality of GeostatsPy and the use of GeostatsPy to accomplish fundamental geostatistics workflow steps. Therefore, this is not a general introduction to statistics nor is it a comprehensive treatment of geostatistics. For this I invite you to refer to a standard textbook like, Geostatistical Reservoir Modeling [PD14]. Alternatively, you can review the linked lectures from my Data Analytics and Geostatistics Lecture Series [Pyr24a] for a deeper dive into the theory and practice of geostatistics. Also, I have a recent book chapter that summarizes GeostatsPy, GeostatsPy: Open-Source Geostatistics in Python[Pyr24d] that may be helpful. Recall my stated goal from the introduction.
Welcome!
The goal of this e-book is to teach the application of geostatistics in Python, for those new to geostatistics I provide theory and links to my course content, and for those experienced practitioners I provide example workflows and plots that you can implement.
I am only partially successful in this goal. More can be done! I welcome your feedback and errata, return often and watch this project continue to grow. Yet, I am pleased with this opportunity to expand the reach of my educational content to support stuudents and working professions.
Stay well,
Michael
MICHAEL J. PYRCZ, Ph.D., P.Eng., Professor B.J. Lancaster Professorship and Fellow of the Herring Professorship in Petroleum Engineering Hildebrand Department of Petroleum and Geosystems Engineering and Bureau of Economic Geology, Jackson School of Geoscience The University of Texas at Austin Twitter | GitHub | Website | GoogleScholar | Book | YouTube | LinkedIn
Cite this e-Book as:
Pyrcz, M.J., 2024, Applied Machine Learning in Python: a Hands-on Guide with Code, https://geostatsguy.github.io/MachineLearningDemos_Book.
Cite the MachineLearningDemos GitHub Repository as:
Pyrcz, M.J., 2024, MachineLearningDemos: Python Machine Learning Demonstration Workflows Repository (0.0.1). Zenodo. 