Data Science for Geoscience
Instructor: Jef Caers, University of Stanford
Format: this extensive and interactive course consists of presentations, (reading) assignments, quizzes and several webinars with the instructor.
Next delivery: 8 July - 8 August 2021
|Early fee until
8 days before
|Regular fee from
7 days before the
|EAGE Bronze/Silver/Gold Member||295 EUR||345 EUR|
|EAGE Platinum Member||295 EUR||295 EUR|
|EAGE Green Member||345 EUR||395 EUR|
|EAGE Bronze/Silver/Gold Student Member||150 EUR||175 EUR|
|EAGE Green Student Member||175 EUR||200 EUR|
|Non Member*||395 EUR||445 EUR|
|Education Package||2 credits||2 credits|
*EAGE Membership for the remainder of the year is included in the non-member fee
|1||Extreme Value Analysis||Week 1||1h|
|2||Statistical Geochemistry||Week 2||1.5h|
|3||Spatial Data Aggregation||Week 3||1h|
Duration: 10 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.
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
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
PrerequisitesBasic intro to statistics & probability theory, some matrix algebra
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 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).