If you are looking for the "Python version" of this knowledge, these are the modern industry standards: Numerical Recipes in Python - Zenodo

Data scientists and physical scientists. While less focused on hard-core ODE solvers, this free PDF (released under CC license) covers the core numerical recipes: linear regression, principal component analysis (PCA), and Fast Fourier Transforms using modern APIs.

The authors taught us to understand the math, respect edge cases, and test rigorously. Python gives us the tools to implement that philosophy in 1/10th the lines of code.

As a data analyst, Emily often found herself working with complex mathematical models and large datasets. She needed a reliable way to perform tasks such as optimization, interpolation, and integration. That's when she discovered "Numerical Recipes in Python."

: A formal textbook by Jaan Kiusalaas that mirrors the pedagogical style of the original series. Python for Data Analysis (Wes McKinney)

You haven't lost the "recipe"—you've automated the kitchen. The understanding of stability, error control, and adaptive stepping is still required, but the boilerplate code is gone.