Hey.
Hey, welcome to the next section.
So this section, as you can probably gather, we're still working with our books data.
I hope you're not tired of it yet if you are almost done.
But it's important that we keep working with one data set for a little bit at least so that you can
get familiar with it.
And the reason that that matters is that as we do, some of the more advanced things, we want you to
be able to check your work.
So in this section we're going to learn a lot about different ways of performing analysis on data.
So things like finding averages or summing a bunch of data together, grouping things by authors and
calculating average quantities or page numbers per author.
These are operations that if we had 10,000 books or 10,000 something else, it's really hard to know
if you're doing it right or wrong, you get an answer, just a number, let's say 67.5.
And how could you know if that's right or wrong?
But if we're working with books and we have 20 of them and you're familiar with the page count and the
authors, you'll know, okay, this author has three books that seems right versus okay, that author
only has two books.
Why are we saying that?
You know, we have three or something like that terrible example.
But the same idea is true.
We know our data at this point.
We're going to at the end of this course and along the way, we're going to keep upgrading our data
to more complex structures, more tables, more rows, complex stuff.
But at the very end, kind of the Capstone case study will be working with Instagram esque data, fake
data for Instagram, and we'll have thousands and thousands of rows.
And you won't actually know if you're doing things right or wrong based off the number you're getting,
unless you've manually checked your work by doing 1000 additions or something.
So all that's to say, stick with the books if you can.
And in this section we're focusing a lot on these new aggregate functions.
So those are things like finding averages, counting summing things together based off of grouping data.
So it's a bit hard to explain in a headshot without showing you code.
So I'll let the code do that in just a few videos from now.
But the rough idea is that we take our data and there's all sorts of insights we can gain.
So rather than just working with an individual row or a group of rows, we can combine things into like
mega rows.
I can combine all of our authors and group books based off of who wrote them or group books based off
of what year they're written in and then perform operations on those groups.
So it allows me to do things like find the average sales that we've had per year, or we could do things
like find the average page number for books per genre, and then we could expand that obviously to more
complex stuff.
If you're working with advertising data or let's say our Instagram data, we'll be able to at the end
of the course do things like find out which one of our users is a power user or influencers, but they're
called, I guess, meaning that they have the most comments, the most likes on each one of their posts
on average.
So who in our database is getting on average the most likes and comments?
Or we could do things like which hashtag generates the most traction.
And to do that, we would need to analyze all of our hashtags and then take all of our photos that have
those hashtags, group them together, figure out which one of those hashtags generates the most likes.
So there's a lot of stuff that we can do with these aggregate functions.
They form the backbone of a lot of the questions and analysis that we'll do throughout the course.
All right, I'm rambling now.
I'm going to go away.
Hopefully you enjoy this section.
It's important to say that a lot, but it really is.
And of course, we'll have a bunch of exercises throughout the course, throughout the section, and
especially at the end.
And I'm trying to keep those interesting.
Look forward to those.
If you don't just remember, it's a database course.
I'm trying my best.
It's.
It's databases.
All right, I'm done.