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So in this lecture, we are going to do a little warm up.

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So how can you think of this lecture?

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This is just like warming up when you do exercise.

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The goal is to get you primed for specific movements and for the specific ways of thinking that we will

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use in this course.

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It should also give you some sense of the kinds of tools we will use in this course and the level of

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complexity you should expect.

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Note that if you've taken courses with me before, you can comfortably skip this lecture.

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So let's begin.

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Your first exercise is to use numpy to generate 1000 random samples from the standard normal.

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That is, samples from the normal distribution with mean zero and variance one.

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Note that this lecture will also help us introduce some notation.

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You'll see that I'm using this unconventional letter n to mean the normal.

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This is so that we don't get it confused with the regular N, which I usually use to mean the number

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of samples in a data set.

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So once you've generated these samples, you want to plot the samples as a time series.

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Next, you'll want to plot the samples as a histogram.

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All right.

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So for the next exercise, you are going to add a trend line to the noise you just generated.

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Once you've done so, it should look something like this.

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Note that this is a scatterplot and not a line chart.

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I'll let you pick the parameters of the trend line.

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So play around with different values until you get something that looks reasonable.

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So the next exercise is to call the cumbersome function on your noise, which calculates the cumulative

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sum of the array you pass in.

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And next, I want you to take that output and plot it as a time series.

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Consider what this Time series reminds you of in the context of this course.

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The answer should be pretty obvious.

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But think about how what you just did might be related to what we will learn in the coming sessions.

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So in the next exercise, I want you to generate 1000 samples from the multivariate normal.

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This will have the mean vector zero zero and the covariance matrix one -0.5 -0.5 and two.

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You can use sci fi for this or any other library you prefer.

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Now, once you have your samples, I want you to plot them using a scatterplot.

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And again, this might not seem like much right now, but keep these concepts in mind as you go through

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the course.

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So your next exercise is to take the samples you just generated from the multivariate normal and calculate

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the sample mean and the sample covariance from your data.

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Consider whether or not these estimates are close to the values you expect.

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As a bonus, do not use the mean and co functions in numpy, but instead use the actual formulas.

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This will be useful for understanding many of the concepts in this course.

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So I hope this little warm up has been pretty easy.

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By the way, you can see that this is not the kind, of course, where we will spend 2 hours learning

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basic python, 2 hours learning how to use pandas and so on.

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As you can tell from these warmup exercises, this knowledge is knowledge you should already have.

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Now, it's my general feeling that if you just learn basic spelling and grammar today, you won't be

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ready to read Shakespeare tomorrow.

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And that's what it would be like if you just learn basic Python and then try to do financial analysis

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or time series analysis.

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So hopefully you agree with me that this is rational.

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It's really difficult to just learn how to code today and then apply all those skills to a complex field

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like Finance or Time series immediately after.

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So please take your time and remember that you have this course for a lifetime.

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There truly is no rush.

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And please note that I do teach numpy, pandas, psi, pi and map plot lib for free.

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Therefore, there is absolutely no excuse for not meeting these prerequisites.

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It's easy.

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It's free.

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And more importantly, it is only beneficial to learn them.

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In other words, it helps you.

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So help yourself and give yourself the best possible start that you can by taking advantage of this

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free content.