2-Day Spatial Data Analytics and Geostatistics Course

This repository contains files related to a training class dated 05/19/2025.

Instructor: Michael Pyrcz, The University of Texas at Austin

Course Summary

Building from fundamental probability and statistics, we cover entire spatial data analytics and geostatistics best practice workflows from data preparation through to decision making. We will accomplish this with,

• Interactive lectures / discussion to cover the basic concepts

• Demonstrations of methods and workflows in Python

• Hands-on experiential learning with well-documented workflows for accessibility

Course Objectives

Spatial data analytics and geostatistics for building spatial prediction and uncertainty models.

You will learn:

• spatial data debiasing

• quantification and modeling of spatial continuity / correlation

• spatial estimation with uncertainty

• spatial simulation for subsurface resource forecasting

• checking spatial models

• decision making with spatial uncertainty models

Course Schedule

Spatial Data Analytics and Geostatistics 2-day Short Course

Day	Time	Topic	Objective	Notes	Demo	Interactive
Day 1	8:00 AM - 8:30 AM	Course Overview	Walk-through of the course plan, goals, methods and introductions	Overview
	8:30 AM - 9:30 AM	Introduction to Spatial Data Analytics and Geostatistics	Introduction to fundamental concepts and terminology, fit-for-purpose modeling and spatial modeling goals.	Introduction
	9:30 AM - 11:00 AM	Probability	Both frequentist and Bayesian probability approaches.	Notes		Dashboard
	11:00 AM - 12:00 PM	Data Preparation	Introduction to data debiasing methods to correct for sampling bias.	Notes	Demo	Dashboard
			Introduction to bootstrap for uncertainty modeling.	Notes	Demo	Dashboard
	12:00 PM - 1:00 PM	Lunch Break
	1:00 PM - 2:00 PM	Data Analytics	Univariate and multivariate statistical methods to support spatial modeling.	Notes	Demo	Dashboard
	2:00 PM - 3:00 PM	Spatial Continuity Calculation	Introduce spatial continuity quantification by calculating variograms.	Notes	Demo	Dashboard
	3:00 PM - 4:30 PM	Spatial Continuity Modeling	Introduce variogram modeling, omnidirectional, directional and nested structures.	Notes	Demo	Dashboard
Day 2	8:00 AM - 10:00 AM	Spatial Estimation	Introduce spatial estimators, theory and applications with kriging.	Notes	Demo	Dashboard
	10:00 AM - 12:00 PM	Simulation and Uncertainty Modeling	Cover the approaches to build a comprehensive uncertainty model, how to account for all salient sources of uncertainty?	Notes	Demo	Dashboard
	12:00 PM - 1:00 PM	Lunch Break
	1:00 PM - 2:00 PM	Advanced Simulation	Cosimulation for bivariate simulation models.	Notes
			Indicator simulation.	Notes	Demo
			Multiple point and object-based simulation.	Notes
	2:00 PM - 3:00 PM	Model Checking	Cover essential quality assurance methods for spatial, geostatistical models.	Notes	Demo
	3:00 PM - 4:00 PM	Decision Making with Uncertainty	Present the workflow to make the best decision given an uncertainty model.	Notes		Dashboard
	4:00 PM - 4:30 PM	Wrap-up, Review and Q&A	Conclusion, group discussion and Plus/Delta exercise

This is a nominal schedule. Note, we are learning and not schedule-driven; therefore the course delivery will adjust for the needs of the class.

Beyond the Course

There is Much More – the building blocks can be reimplemented and expanded to address various other problems, opportunities. There is much more that we could cover,

• Additional Theory

• More Hands-on / Experiential

• Workflow Development

• Basics of Python / R

• Advanced Data Preparation

• Advanced Model QC

• Methods to Integrate More Geoscience and Engineering

• Integration of Machine Learning Spatial Modeling

We are happy to discuss other, advanced courses and custom courses to meet your teams' educational needs to add value at work with data science.

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