Data Science for Geoscience

Instructor: Jef Caers, University of Stanford

Level: Intermediate

Format: this extensive and interactive course consists of presentations, (reading) assignments, quizzes and several  webinars with the instructor.

Next delivery: 16 October - 16 November 2023


Early fee until 
8 days before
course starts
Regular fee from
7 days before the
course starts
EAGE Bronze/Silver/Gold Member  325 EUR  375 EUR
EAGE Platinum Member   325 EUR   325 EUR 
EAGE Green Member   385 EUR  435 EUR
EAGE Bronze/Silver/Gold Student Member   150 EUR   175 EUR 
EAGE Green Student Member  175 EUR  200 EUR
Non Member*  465 EUR  515 EUR
Education Package   2 credits   2 credits 

*EAGE Membership for the remainder of the year is included in the non-member fee

 October 2023 Edition: Register Now

  Buy Education Package    Join EAGE today!

Schedule October 2023 edition
Webinar   Topic When    Duration 
1 Extreme Value Analysis  Friday, 3 November 2023   17:00 - 18:00 CEST 
2 Statistical Geochemistry   Thursday, 9 November 2023   17:00 - 18:30 CEST
3 Spatial Data Aggregation   Friday 10, November 2023  17:00 - 18:00 CEST 
4 Geostatistics  Thursday, 16 November 2023  17:00 - 19:00 CEST

Duration: 14 hours
Note: this is an estimate of the time required to go through the course, including watching/reading lectures and attending webinars. Lectures and exercises will become available on the first day of the course. You will have access to the course material for a period of 1 month after the start. Make sure to complete all the requirements for the achievement of the certificate by this date. 


A certificate of completion will be available upon completion of all course requirements. After the end of the course, your certificate will remain available for download in your Profile page.

Course description

This course provides an overview of the most relevant areas of data science to address geoscience challenges, questions and problems. Using actual geoscientific research questions and practical cases as background, principles and methods of data scientific analysis, modeling, and prediction are covered.  The material aims at exposure & application over in-depth methodological or theoretical development. Data science areas covered are: extreme value statistics, multi-variate analysis, factor analysis, compositional data analysis, spatial information aggregation models, spatial estimation, geostatistical simulation, treating data of different scales of observation, spatio-temporal modeling. Application areas covered are: predicting volcano magnitudes, landslides, finding causes of contamination, predicting sea-level rise, groundwater modeling & management, landslide susceptibility assessment, mineral & geothermal potential mapping, interpolating missing remote sensing data and others. Students are encouraged to participate actively in this course by means of their own data science research challenge or question. Home-works will consist of reading papers and being able to synthesize the essential data science tools. To run code of a few selected method and a presentation on a data scientific topic of choice. The code will be embedded in notebooks that will contain data examples

Course objectives

Upon completion of this course, participants will be able to: 

1. Identify a combination of data science methods to address a specific geoscientific question or challenge whether related to the environment, earth resources or hazard, and its impact on humans
2. Use statistical software on real datasets and communicate the results to a non-expert audience 


Basic intro to statistics & probability theory, some matrix algebra

Participant profile 

This course is intended for students and professionals who like to learn about data science method that address common geoscience challenges

About the instructor

Jef CaersJef Caers received both an MSc (’93) in mining engineering / geophysics and a PhD (’97) in engineering from the Katholieke Universiteit Leuven, Belgium. Currently, he is Professor of Geological Sciences (since 2015) and previously Professor of Energy Resources Engineering at Stanford University, California, USA. He is also director of the Stanford Center for Earth Resources Forecasting, an industrial affiliates program in decision making under uncertainty with ~20 partners from the Earth Resources Industry. Dr. Caers’ research interests are quantifying uncertainty and risk in the exploration and exploitation of Earth Resources. Jef Caers has published in a diverse range of journals covering Mathematics, Statistics, Geological Sciences, Geophysics, Engineering and Computer Science. Dr. Caers has written four books entitled "Petroleum Geostatistics” (SPE, 2005) “Modeling Uncertainty in the Earth Sciences” (Wiley-Blackwell, 2011), "Multiple-point Geostatistics: stochastic modeling with training images" (Wiley-Blackwell, 2015) and “Quantifying Uncertainty in Subsurface Systems (Wiley-Blackwell, 2018).