So I hope that now you understand better the concept of derivatives in Python.
So we have learned about several different methods here.
And for example, the Richardson method gives us a way higher accuracy compared to the forward differences
methods, and we have even discussed why this is the case analytically.
So I hope you like the example or the exercise in the middle of the section where you have calculated
the velocity and the acceleration based on a data set position versus time.
And I hope that you also got the results that the Richardson method is much better than to for what
differences methods.
So then at the end of the section, we discussed multi-dimensional derivatives, so the gradient, the
curl and divergence.
And before we get to an example with these concepts, let's first discuss integrals in the next section.
So at the end of the next section, we will have an example where we will use both concepts integration
and differentiation in multiple dimensions to calculate the magnetic fields of a charged wire.
This may sound like a very simple problem because you know about the results already in high school,
but it's actually very difficult to calculate this magnetic fields.
But with the features and with the tools that we have just learned, we are able to do it.
So I hope you're excited about the next section and let's get started.