Course Slides

HTML presentations for courses taught by the Chapman Research Group.

ATOC 4815/5815: Scientific Programming, Data Analysis and Visualization

Spring 2026

Week 0: Course Introduction - Welcome, syllabus, AI/LLM policy, setup, and getting started
Demo Lecture: Python & Data Analysis - Introduction to course structure and basic Python
Week 1: Python Fundamentals - Variables, types, strings, lists, dictionaries, control flow, loops, and functions
Week 2: Functions and Reusable Code - Advanced functions, error handling, file I/O, and object-oriented programming
Week 3: NumPy and Basic Plotting - NumPy arrays, vectorized operations, matplotlib basics, and data visualization
Week 4: Numerical Integration & Explicit Euler - Solving ODEs with Forward Euler, stability analysis, and Lorenz63
Week 5: Midterm Post-Test Review - The date, nested loops, and why integration needs a loop
Week 5.5: Linking Python Scripts Together - Modules, imports, if __name__ == "__main__", and multi-file project structure

Week 6: Git for Scientists - 5 commands to survive: clone, status, add, commit, push (practice repo)

Collaboration - Branches, merging, pull requests, and .gitignore

Week 7: Tabular Data & Pandas - Series and DataFrames, time series analysis, resampling, rolling windows, and aggregation
Week 8: Multi-Dimensional Data with xarray - NetCDF files, DataArrays, Datasets, coordinate-based selection, climatologies, and gridded data analysis
Week 9: Python Parallelization - GIL, vectorization, multiprocessing, concurrent.futures, Dask, and best practices for making code faster
Week 10: Packaging Your Python Code - pyproject.toml, pip install, entry points, TestPyPI, conda-forge, and making your code installable

More slides will be added throughout the semester

Fall 2025

Slides coming soon

These slides are created using Quarto with reveal.js. The source files (.qmd) can be found in the course GitHub repositories.

If you’d like to run the code examples from the slides: