Say 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 dataset 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 something 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 10000 books or 10000 something else it's really hard to know if
you're doing it right or wrong.
You get an answer just a number of let's say sixty seven point five.
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 counts and
the authors you'll know.
OK.
This author has three books that seems right versus OK 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 you know we're going to keep upgrading our
data to more complex structures more tables more rose complex stuff.
But at the very end kind of the capstone case study we'll 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 you know doing a thousand additions or something.
So all this 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 and a headshot that's showing you code.
So I'll let the code do that.
And 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 them 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 general.
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 influencers what 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 hash tag 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 hash tags 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 or throughout the section and especially
at the end.
And I'm trying to keep it interesting.
Look forward to those.
If you don't just remember it's a database course.
I'm trying my best.
It's you know it's databases.
All right.
I'm done.