2d Data Analytics: Joint, Marginal, Conditional Probability
Data Analytics and Geostatistics Undergraduate Course, Professor Michael J. Pyrcz
Lecture Summary:
A supplemental lecture with discussion on joint, conditional and marginal, probability and distributions. These are fundamental concepts for exploring our data, e.g., moving beyond traditional correlation to find patterns, and for building prediction models.
Here is a list of all of my probability lectures:
1. Probability - • 2a Data Analytics: Probability
2. Frequentist Probability - • 2b Data Analytics: Frequentist Probability
3. Bayesian Probability - • 2c Data Analytics: Bayesian Probability
4. Joint, Marginal and Conditional Probability - • 2d Data Analytics: Joint, Marginal, Condit...
5. Bayesian Coin Demon Walkthrough - • 2e Data Analytics: Bayesian Coin Demo
Free, Online Course e-book Chapter:
Probability - https://geostatsguy.github.io/Geostat...
Theory and well-documented workflows linked to the lectures and interactive Python dashboards.
Course Summary:
Data Analytics and Geostatistics is an undergraduate course that I teach 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, and machine learning basics. I provide accessible content to help you face the digital revolution!
My Shared Educational Content:
YouTube: / geostatsguylectures
GitHub: https://github.com/GeostatsGuy
Course e-book: https://geostatsguy.github.io/Geostat...
I share all of my university educational content to support students and working professionals interested to learn data analytics, geostatistics, and machine learning.
More About the Author:
https://michaelpyrcz.com
I hope that you find my educational course content helpful on your data science journey,
Professor Michael J. Pyrcz
Cockrell School of Engineering
Jackson School of Geosciences
The University of Texas at Austin
#dataanalytics #datascience #geostatistics #machinelearning