Python · Data · Reproducible science

Computational Methods for
Earth & Environmental Science

Learn Python through real Earth and environmental questions. Build reproducible workflows, analyze data, make clear figures, develop algorithms, and test simple environmental models.

Start here Open course materials

What you will practice

Code as a scientific tool

Reproducible work

Organize code, data, and environments so another person can repeat your analysis.

Data analysis

Use NumPy and pandas to work with weather, streamflow, water-quality, and gridded data.

Scientific reasoning

Write algorithms, quantify uncertainty, validate results, and explain limitations.

Clear communication

Create figures and notebooks that connect computation to an environmental question.

16-week teaching sequence

Course schedule

Lecture slides open in a new tab. Individual Lab 2 spans Weeks 2 and 3. Week 14 can be used for a reading week or academic break.

Week	Course topic	Lab focus	Slides
01	Course overview and reproducible computation	Jupyter setup and first plot	Open
02	Python basics	Lab 2, Part A: weather variables	Open
03	Conditionals, loops, and events	Lab 2, Part B: environmental events	Open
04	Functions and debugging	Hydrologic indices	Open
05	NumPy and array thinking	Gridded Earth data	Open
06	Pandas and time series	Streamflow and weather	Open
07	Scientific visualization	Group Lab 1	Open
08	Statistics, regression, and uncertainty	Group Lab 2	Open
09	Algorithm development	Group Lab 3	Open
10	Responsible AI-assisted coding	Group Lab 4	Open
11	Geostatistics and spatial prediction	Group Lab 5	Open
12	Simple environmental modeling	Group Lab 6	Open
13	Final project launch	Proposal workshop	Open
14	Reading week or academic break
15	Advanced applications and project support	Project studio	Open
16	Course synthesis	Final presentations	Open

Student workspace

Course materials

Download a clean notebook before you begin. Keep your completed work in a separate copy.

Individual labs

Five guided notebooks that build core Python skills.

Lab 01Setup, Jupyter, and a first plotDownload notebook ↓ Lab 02Python basics and event detectionDownload notebook ↓ Lab 04Functions, debugging, and flow indicesDownload notebook ↓ Lab 05NumPy arrays and gridded dataDownload notebook ↓ Lab 06Pandas time seriesDownload notebook ↓

Group labs

Six pair labs: each partner owns one module, and both finish with one joint result.

Group 01Scientific visualizationDownload notebook ↓ Group 02Statistics and uncertaintyDownload notebook ↓ Group 03Interpolation and smoothingDownload notebook ↓ Group 04AI-assisted scientific codingDownload notebook ↓ Group 05Geostatistics and IDWDownload notebook ↓ Group 06Model calibrationDownload notebook ↓

Final project

Move from a testable question to a reproducible analysis and presentation.

1Group proposal template 2Analysis template 3Presentation outline 4Contribution statement +AI usage log

Before the first lab

Set up the course environment

Install Anaconda or Miniconda, download the course repository, then run these commands from the repository folder.

Download environment.yml ↓ Working on your own laptop? Open a lab notebook in VS Code, step by step →

Terminal

conda env create -f environment.yml
conda activate earth-course
python scripts/download_data.py
python scripts/clean_data.py
jupyter lab

How work is evaluated

Grading and course policies

Grading weights

Individual labs: 30%
Group labs: 25%
Final group project: 30%
Contribution + participation: 10%
Attendance + engagement: 5%

Responsible tool use

When allowed, AI tools may support debugging, refactoring, comments, tests, and documentation. Disclose meaningful assistance, run and review every line, verify outputs, and be ready to explain your work.

Never use AI to fabricate data, citations, results, or interpretations. Never upload restricted data.

Lab debrief

Beginning with Lab 2, arrive at the next lab meeting with a short record of a solved problem, an unresolved problem with evidence, or a design and verification choice.

Download debrief guide ↓