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Hey.

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Hey.

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Welcome to the next section.

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So this section, as you can probably gather, we're still working with our books data.

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I hope you're not tired of it yet if you are almost done.

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But it's important that we keep working with one data set for a little bit at least, so that you can

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get familiar with it.

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And the reason that that matters is that as we do some of the more advanced things, we want you to

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be able to check your work.

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So in this section, we're going to learn a lot about different ways of performing analysis on data.

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So things like finding averages or summing a bunch of data together, grouping things by authors and

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calculating average quantities or page numbers per author.

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These are operations that if we had 10,000 books or 10,000 something else, it's really hard to know

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if you're doing it right or wrong.

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You get an answer, just a number, let's say 67.5.

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And how could you know if that's right or wrong?

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But if we're working with books and we have 20 of them and you're familiar with the page counts and

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the authors, you'll know, okay, this author has three books that seems right versus okay.

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That author only has two books.

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Why are we saying that?

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You know, we have three or something like that.

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Terrible example.

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But the same idea is true.

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We know our data at this point.

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We're going to at the end of this course and along the way, we're going to keep upgrading our data

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to more complex structures, more tables, more rows, complex stuff.

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But at the very end, kind of the capstone case study will be working with Instagram esque data, fake

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data for Instagram, and we'll have thousands and thousands of rows and you won't actually know if you're

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doing things right or wrong based off the number you're getting unless you've manually checked your

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work by doing 1000 additions or something.

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So all that's to say, stick with the books if you can.

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And in this section we're focusing a lot on these new aggregate functions.

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So those are things like finding averages, counting, summing things together based off of grouping

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data.

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So it's a bit hard to explain in a headshot without showing you code.

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So I'll let the code do that in just a few videos from now.

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But the rough idea is that we take our data and there's all sorts of insights we can gain.

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So rather than just working with an individual row or a group of rows, we can combine things into like

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mega rows.

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I can combine all of our authors and group books based off of who wrote them or group books based off

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of what year they're written in, and then perform operations on those groups.

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So it allows me to do things like find the average sales that we've had per year, or we could do things

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like find the average page number for books per genre, and then we could expand that obviously to more

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complex stuff.

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If you're working with advertising data or let's say our Instagram data, we'll be able to at the end

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of the course, do things like find out which one of our users is a power user or influencers, but

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they're called, I guess, meaning that they have the most comments, the most likes on each one of

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their posts on average.

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So who in our database is getting on average the most likes and comments?

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Or we could do things like which hashtag generates the most traction.

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And to do that we would need to analyze all of our hashtags and then take all of our photos that have

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those hashtags, group them together, figure out which one of those hashtags generates the most likes.

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So there's a lot of stuff that we can do with these aggregate functions.

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They form the backbone of a lot of the questions and analysis that we'll do throughout the course.

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All right.

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I'm rambling now.

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I'm going to go away.

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Hopefully you enjoy this section.

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It's important to say that a lot, but it really is.

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And of course, we'll have a bunch of exercises throughout the course, throughout the section and especially

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at the end.

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And I'm trying to keep those interesting, look forward to those.

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If you don't just remember it's a database course.

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I'm trying my best.

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It's it's databases.

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All right, I'm done.