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So now it is your turn.

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Have talked a lot about derivatives and about different methods on how we can implement these derivatives.

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We have learned about the forwhat differences, central differences, methods and about the Richardson

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methods and also others.

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And now is the time to practice this.

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But you you're not supposed to just repeat this for different function.

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But instead, we will now deal with data files.

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So here you see in this template file, I have loaded already a file that you can download in the course

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and it is called exercise velocity acceleration data file.

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And you see, this is how you can load it and how you can store this in the variable called data when

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you call data.

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Then you see it's an array with data points and the first column goes from zero to 100 in steps of 0.1.

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This will correspond to the time in seconds, and the second column will go here from one to fifteen

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point four.

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But it is really long, monotonous in between.

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And this describes the position of some car, for example, and it's a position in meters.

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And when we clocked this, you see, it looks like this we have here at a time in seconds, coordinates

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x and meters and you see increases, decreases and so on.

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So your task is now to to calculate the velocity, which is of course, the first on a time derivative

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of the position and the acceleration, which is the second all the time derivative for the load of data

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set that describes the set of points, Ti and X.

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As you see here in the plot and you see we have 1000 points which describe to time from zero to 100

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and you're supposed to calculate the and I'm using the forward differences, central differences and

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the Richardson methods and then the bonus task, which is pretty short, but maybe you don't really

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know how to do it.

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So maybe you have to google some of the commands of Nampai how you can accomplish this.

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You're supposed to find then the maximum value of the acceleration A.I. and the corresponding point

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of time I.

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So here, just one more hint you have to be very careful here.

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So at first, you may think this is pretty easy.

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You just use the functions that we have programmed in the previous lectures and then you can apply this.

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But the problem is now we do not know the function f of X that describes this curve.

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We only have two data points, so you can only work with the data points.

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And this means you may have to program the routines that we have previously programmed a bit differently.

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So you really have to work with data and so on your functions.

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The only input parameter will be data.

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There will be no step size, no h because this is all determined by the data itself.

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So, for example, h in this case will always be zero point one, so please give it a try.

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But if you don't succeed, it's not a big deal.

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No problem.

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I will provide you the solution in the following lecture.

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So now click to the next lecture.

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There, you can download the template file and also the data file.

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So good luck and have fun, and I see you soon with the solution you.