Geomaap offers customized courses in geodata science. Our approach is to provide 1-3 day courses in which you can learn and apply introductory and more advanced methods used in geospatial analysis and data visualization. We believe that reproducibility is an essential aspect of all learning, which is why we only use open source tools such as Python, QGIS, SGeMS, OpendTect, Orange and Dash. All the material used in the course (script, code, projects, datasets) will be yours to use and you will be able to reproduce the analysis learned in class.

• Geocomputing
  with Python
• Geodata Science
  (Geostatistics)
• Geodata Science
  (Machine Learning)
• Instructor

Python Introduction

• Python Introduction and Setup Configuration
  01 Hour

Python for Exploratory Data Analysis

• NumPy Getting Started
  45 Min
• Pandas Getting Started
  01 Hour
• Matplotlib Getting Started
  45 Min

Python for Geoscientist

• Handling GIS Data with Geopandas
  01 Hour
• Interactive Map Visualization with Plotly
  1.5 Hour
• Image Processing with Scikit-Image
  01 Hour

Introduction to Geostatistics

• Spatial Analytics - Variogram, Kriging, Geostatistical Simulations.
  Their advantages and limitations.
  2.5 Hour

Geostatistics with Python (GeostatsPy)

• GeostatsPy Getting Started
  30 Min
• Variography analysis
  01 Hour
• Kriging
  01 Hour
• Geostatistical Simulations
  01 Hour
• Post-Simulations Analysis and Visualization
  01 Hour

Geostatistics with SGeMS

• SGeMS Getting Started
  30 Min
• Working with Data and Grids in SGeMS
  01 Hour
• Variography Analysis
  1.5 Hour
• Kriging
  02 Hour
• Geostatistical Simulations
  02 Hour
• Post-Simulations Analysis and Visualization
  01 Hour

Introduction to Machine Learning

• General concepts and theory
  01 Hour
• Database and Input Data
  01 Hour
• Feature Engineering
  01 Hour

Supervised and Unsupervised Learning in Geosciences

• Unsupervised Methods for Geoscience with Scikit-Learn and Orange
  02 Hour
• Supervised Methods for Geoscience with Scikit-Learn and Orange
  02 Hour

Applied machine learning in Python

• Introduction to Python Libraries for Machine Learning Applications
  02 Hour
• From Data to Models: End-To-End Custom Excercise with Scikit-Learn and Orange
  03 Hour

Lorenzo Perozzi

Geo Data Scientist, PhD

I'm a Geo Data Scientist with 10 years of experience in geospatial analytics for surface and subsurface characterisation and uncertainty quantification. Python is my daily language of choice, which offers a lot of flexibility when it comes to working with data and integrate new analysis within the framework of the standard GIS and modeling softwares. I am passionate about open-source developments and innovative approaches to help customers reach their objectives, and share what I know through blog posts and course materials.