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2d Data Analytics: Joint, Marginal, Conditional Probability
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A supplemental lecture with discussion on joint, conditional and marginal, probability and distributions. This is an important concept for exploring our data, i.e., moving beyond traditional correlation to find patterns.
Follow along with the interactive demonstration workflow in Python:
Follow along with the demonstration workflow in Excel:
Data Analytics and Geostatistics is an undergraduate course that I teach fall and spring semesters at The University of Texas at Austin. We build up fundamental spatial, subsurface, geoscience and engineering modeling, from probability, statistics, heterogeneity measures, spatial continuity models, spatial estimation, spatial simulation, model checking, decision making, up to machine learning basics. I provide accessible content to help you face the digital revolution!
I hope you find this course content useful,
Michael J. Pyrcz
Professor
Cockrell School of Engineering
Jackson School of Geosciences
The University of Texas at Austin
Follow along with the interactive demonstration workflow in Python:
Follow along with the demonstration workflow in Excel:
Data Analytics and Geostatistics is an undergraduate course that I teach fall and spring semesters at The University of Texas at Austin. We build up fundamental spatial, subsurface, geoscience and engineering modeling, from probability, statistics, heterogeneity measures, spatial continuity models, spatial estimation, spatial simulation, model checking, decision making, up to machine learning basics. I provide accessible content to help you face the digital revolution!
I hope you find this course content useful,
Michael J. Pyrcz
Professor
Cockrell School of Engineering
Jackson School of Geosciences
The University of Texas at Austin
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