Statistics, Data Analysis & ML in Geology Using Python
Join our engaging workshop designed for geologists, researchers, and professionals to master Python for statistical analysis, data processing, and machine learning in geology. Learn to analyze geological datasets, create insightful visualizations, and apply machine learning models to solve real-world geological challenges. This workshop blends expert instruction with practical, hands-on exercises to elevate your data science skills in Earth sciences.
Workshop Objectives
Understand statistical methods for geological data analysis using Python.
Develop proficiency in processing and visualizing geological datasets.
Apply machine learning techniques to model geological phenomena.
Solve real-world geology problems using Python’s data science libraries.
Gain practical experience through hands-on, geology-focused projects.
About the Presenter
A highly accomplished geologist with expertise in structural geology, tectonics, and field mapping. He has a PhD in Geology from the University of Illinois at Urbana-Champaign and has held several teaching and research positions at prestigious universities in the United States. He is proficient in various geological software and programming languages, including Python, ArcGIS, and MATLAB. He has extensive experience in geological mapping, 3D modeling, and quantitative analysis of geological structures. He has also developed and taught several courses on Python programming for geoscientists.
Applied Machine Learning for Upstream and Subsurface Domains
This two-day hands-on workshop is designed for petroleum and subsurface professionals seeking practical skills in machine learning for upstream oil & gas operations. The program teaches participants how to apply machine learning to uncover patterns in production and reservoir data, discover equations from historical relationships, and build predictive models for vital engineering parameters. Real-world datasets from drilling and subsurface domains are used, focusing on automating reports, visualizing data, and interpreting model outputs for improved decision-making.