The DFT/FFR is actually very much useful and transformative to computer science. It is, along with Karatsuba's algorithm, the basis for a lot of the triumphs of

The DFT/FFR is actually very much useful and transformative to computer science. It is, along with Karatsuba's algorithm, the basis for a lot of the triumphs of computer science. Want to do polynomial interpolation? FFT. Want to do convolutions on large-ish images? FFT. Want to compress audio/images/video? FFT, or FFT-ish. Want to break RSA on a quantum computer? FFT also (well, QFT). Any algorithms class that left over the Fourier transform is, in my opinion, a small tragedy. That said, I personally think that integration is less straightforward than the Fourier transforms. It's the first task you're given that requires creativity, as there is no practical algorithm for it (sometimes, there isn't even really a way to do it). If you try to approach the Fourier transform as a piece of linear algebra, seeing it as an orthonormal transformation to periodic vectors/functions, it's a lot more intuitive.