Python Programming in the Geosciences

Dr. Alex Haberlie

Department of Earth, Atmosphere and Environment

Northern Illinois University

EAE 483 / 583: Data Science for the Geosciences (Spring 2026)¶

What is this course about?¶

This course is for advanced undergraduate students and graduate students who have computer programming experience and want to develop their data science skills on applied projects in the geosciences. It is intended to be the second course in a geoscience data analytics sequence that starts with “Computer Programming in the Geosciences (EAE 493)”. Students will research case studies involving data ethics, apply best practices in scientific software engineering, and develop workflows that solve geoscience problems using machine learning and statistics.

Course Content¶

Chapter 7 - Geospatial Analysis¶

• 7.1 - Spatial Data Types

• 7.2 - Mapping Geoscience Data

• 7.3 - Vector Data Analysis

• 7.4 - Raster Data Analysis

• 7.5 - Spatial Statistics

• L1 - Lab 1: Colab and Github

• L2 - Lab 2: geopandas

• L3 - Lab 3: cartopy

• L4 - Lab 4: xarray

• A1 - Assignment 1 - Spatiotemporal Frequency Analysis

Chapter 8 - Machine Learning - Tabular Data¶

• 8.1 - Machine Learning Overview

• 8.2 - Clustering

• 8.3 - Decision Trees

• 8.4 - Random Forest

• L5 - Lab 5: scikit-learn preprocessing

• L6 - Lab 6: scikit-learn clustering

• L7 - Lab 7: scikit-learn classification

• L8 - Lab 8: scikit-learn ensemble methods

• A2 - Assignment 2 - Geoscience Data Clustering

Chapter 9 - Machine Learning - Geospatial Data¶

• 9.1 - Digital Image Processing

• 9.2 - Image Segmentation

• 9.3 - Image Filters

• 9.4 - Image Feature Detection

• 9.5 - Image Classification

• 9.6 - Pixel Classification

• L9 - Lab 9: Image Thresholding

• L10 - Lab 10: Image Filtering

• L11 - Lab 11: scikit-image - feature extraction

• L12 - Lab 12: convolutional layers - image classification

• L13 - Lab 13: pytorch - image classification

• L14 - Lab 14: pytorch - pixel classification

• A3 - Assignment 3 - Geoscience Data Image Analysis

• A4 - Assignment 4 - Object Detection and Description

• A5 - Assignment 5 - Object Classification

EAE 493 / 593: Computer Programming for the Geosciences (Fall 2025)¶

What is this course about?¶

Introductory programming techniques used to process and visualize geospatial data. Programming in Python, basic program logic and control structures, integration of Python with open-source scientific programming libraries, and 2-D and 3-D visualization of geospatial data.

Course Content¶

Chapter 1 - Development Environments¶

• 1.0 - Welcome Page

• 1.1 - Integrated Development Environments

• EX1 - Jupyter Introduction

• HW1 - Humidity Calculations

Chapter 2 - Syntax and Data Types¶

• 2.0 - Python Syntax

• 2.1 - Primitive Data Types

• 2.2 - Strings

• 2.3 - String Methods

• 2.4 - Indexing

• 2.5 - Lists

• 2.6 - Tuples, Sets, and Dictionaries

• EX2 - Basic Syntax

• EX3 - Primitive Data Types

• EX4 - Composite Data Types

• HW2 - Climate Records

Chapter 3 - Flow Control¶

• 3.1 - if / elif / else

• 3.2 - Complex Conditions

• 3.3 - while loops

• 3.4 - for loops

• 3.5 - functions

• EX5 - If Statements

• EX6 - Loops

• EX7 - Functions

• HW3 - Climate Toolkit

Chapter 4 - Classes¶

• 4.1 - classes

• 4.2 - imports

• 4.3 - numpy

• EX8 - Classes

• EX9 - Numpy

• EX12 - Classes Practice

• EX13 - Numpy Practice

Chapter 5 - Plotting¶

• 5.1 - matplotlib

• 5.2 - Plotting Examples

• EX14 - Matplotlib Practice

• HW4 - Making Plots

Chapter 6 - Pandas¶

• 6.1 - pandas

• 6.2 - Filtering

• 6.3 - groupby

• 6.4 - Plotting Examples

• EX10 - Pandas Filtering

• EX11 - Pandas Groupby

• HW5 - Tornado Dataset