Polynomials, sampling theory, signal processing, Fourier series, etc. are all so closely related that learning even a little bit of one of these things opens up such a wealth of other things to think about and explore. Splines like this basically answer the question "what polynomial fits these points the best?" which, it turns out, is the same question you need to pose to begin formulating gaussian quadrature. And gaussian quadrature is super cool because it approximates integrals about a billion times better than riemann summation[1] for the same amount of computational effort.
[1]: https://www.youtube.com/watch?v=k-yUdqRXijo