>> Hi there, Welcome to yet another Python series in
case you or sick of us already we're back for more,
This time we're going to focus in on some tools for data science,
Now, before we get into it,
there's really three main questions
that we want to be able to answer,
Who are we, who are you
and what is it that we're going to be doing here?
So let's start off with, well, who are we?
>> So my name is Susan Ibach and I am
definitely a data geek also known as hockeygeekgirl online,
and one of the things that I loved to do when I'm
not sitting in front of a terminal coding,
I'm probably either maybe running
a marathon or maybe listening some good thrash fan,
>> As for me, I'm Christopher Harrison,
I've been a geek for about as long as I can remember,
I'm now a Program Manager at or with
academic ecosystems inside of Microsoft,
I've a propensity to tell dad jokes,
as well as saying the word
"Propensity" I'm a husband, I'm a marathoner,
and I'm a father of a four-legged child,
also known as a dog,
So let's talk a little bit then about like expectations,
prerequisites, if you will, basically,
who are you and why is
it that you might be interested in something like this?
So the assumption that we're making is that you've done
a little bit of Python and you're looking to
just get in and start to explore data science that
maybe there's a quick start that you'll want to check
out and you're just trying to figure out like,
what are these different things that I'm being told to
just follow along with and just run some code,
Hopefully, we'll explain some of what's going on inside of there,
>> Yeah, because a lot of times,
a lot of people get into Python because I have
an interest in exploring that world of data science,
but opening up that first data science tutorial,
you've got all the data science concepts and at the same time
all kinds of new Python libraries
and debates that you might not be familiar with,
So what we're hoping here is introduce you to some of
those Python tools and some of
those libraries so that maybe
when you do that data science tutorial,
you can focus more on just new data science concepts,
>> Yeah, so that's really what it
is that we're going to be doing here, is introducing those,
those libraries, walk you
through some common tools and techniques,
and navigate through some common scenarios,
The way that we're going to be doing it is by walking
through really some basic scenarios,
and you'll notice that there'll be a little bit of
a common thread throughout a lot of these modules,
where a lot of these modules,
a lot of these concepts will build on the ones prior,
That being said, there still all designed to be snackable,
all just like really short,
So you can jump in,
take a look at the particular tools concepts that you need,
and then jump back out from there,
>> Yeah, but you'll find it easiest to learn
these particular series if you do watch them sequentially,
You can still skip
the PowerPoint presentation and jump
straight to demos if you're one of those people who are just,
"Just show me the code in
action" You can still absolutely do that,
just follow the code modules,
but you will probably find easier to follow
the concepts if you do the modules in order,
>> Yes, so let's head on into it,
>> Let's start coding,