Fundamentals of Data Science and Machine Learning (course 1)

11-20 March 2025

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Ticket Type Price Qty*

GESGB Member

£225.00 GBP

Non-Member

£275.00 GBP

Event Information & Registration

Event Information

This event will be delivered online.

📅 Dates: 11 | 13 | 18 | 20 March 2025 (4 x 2hr sessions)

⏰ Time: 14:00-16:00 (UK time) per session

 

Registration

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If you are interested in becoming a member of GESGB please visit the membership page HERE.

Course Description

Training Partner Programme (online): Fundamentals of Data Science and Machine Learning

Facilitator: Professor Cédric M. John, Head of Data Science for the Environment and Sustainability at the Digital Environments Research Institute (DERI) at Queen Mary University of London

Course Overview

Are you a geophysicist, petrophysicist, or geologist looking to stay ahead in the rapidly evolving subsurface industry? Our “Fundamentals of Data Science and Machine Learning” course is designed specifically for professionals like you. Over four intensive 2-hour sessions, you’ll delve into the core concepts of machine learning, starting with an introduction to the landscape and essential Python tools like Scikit-Learn. Learn how to prepare and preprocess data, understand generalization, and differentiate between parametric and non-parametric models.

The course will guide you through critical evaluation metrics and algorithms, including logistic regression, KNN, and ROC-AUC, ensuring you can assess model performance accurately. You’ll also explore optimization techniques, including gradient descent and loss functions, to enhance model fitting. Finally, we’ll tackle advanced topics like the ‘No Free Lunch Theorem’, spatial and temporal co-variance, and feature selection, equipping you with the skills to handle complex data modeling challenges.

By the end of this course, you’ll have a robust understanding of data science and machine learning principles, empowering you to make data-driven decisions, optimize exploration and production processes, and stay competitive in the subsurface industry. This will put you in the best position to take our second course, dedicated to advanced machine learning algorithms. Don’t miss this opportunity to transform your expertise and drive innovation in your field.

Course Outline

Day 1 | General Concepts and Data Preparation

  • Introduction to the landscape of Machine Learning
  • Introduction to Scikit-Learn
  • Overview of Data Preprocessing
  • What is Generalization?
  • Parametric vs Non-Parametric models

Day 2 | Evaluation Metrics

  • Introducing Logistic Regression and KNN
  • Baseline Score
  • Regression Metrics
  • Classification Metrics
  • ROC-AUC

Day 3 | A deep Dive into Data Modelling

  • The ‘No free lunch theorem’
  • Dealing with spatial and temporal co-variance
  • Feature selection

Day 4 | Machine Learning Workflows

  • Motivation: why a workflow?
  • Introduction to the sklean.pipeline module
  • Custom transformers
  • Predictive piplines
  • From notebook to Python class

Facilitator Biography

Facilitator: Professor Cédric M. John, Head of Data Science for the Environment and Sustainability at the Digital Environments Research Institute (DERI) at Queen Mary University of London

Professor Cédric M. John is the Head of Data Science for the Environment and Sustainability at the Digital Environments Research Institute (DERI) at Queen Mary University of London. Previously, he was appointed as Reader in Earth-Centric AI at the Department of Earth Science and Engineering, Imperial College London (2008-2023).

Professor John’s research focuses on applying AI and machine learning to subsurface data, such as core images, logs, seismic, and geochemical datasets. His work includes automatic facies classification, generative AI for core image reconstruction, and deep learning for seismic data analysis. He has developed and taught undergraduate and master level courses like “Data Science and Machine Learning for Geoscientists” and “Data Science and Machine Learning for Planet Earth,” and “Advanced Carbonate Reservoirs”. At Imperial, he was co-creator and deputy director for the “Geo-Energies with Machine Learning and Data Science” MSc program.

Throughout his career, Professor John has supervised numerous PhD students and postdoctoral researchers. Much of his research has been industry funded, and he has work closely on technologies to promote the energy transition. His long-term ambition is to advance the integration of AI in geosciences through his research and teaching.

Venue Information

Venue information

Venue name:

Online

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This event will be delivered online.