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[MUSIC]

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Hi, I'm Craig and I'm a developer.

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In this course we're going to be taking
a look at Python's wonderful data

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library, NumPy.

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You'll find NumPy in all
sorts of applications, and

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therefore, it's fairly critical that you
have an understanding of its fundamentals.

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It appears in every direction
you may head in Python.

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Should you plan to get into data analysis,
there's scientific computing, or

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even machine learning,
you're going to bump into NumPy and

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that's what this course is all about.

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It's an introduction.

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I want to introduce you
to the library early on.

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I'll walk through hands-on examples
that will give you a great introduction

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to the library, its main concepts and
the surrounding terminology.

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When you've complete the course,
you'll have a great foundation and

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you'll know where to turn when you
need more specific information.

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But, before we get started, let's take
some time to make sure that you're

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familiar with your learning environment.

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First off, there are some prerequisites to
this course, and I'd love for you to make

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sure that we're on the same page about
where you are in your coding journey.

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There's speed control on the video player,
so please feel free to speed me up or slow

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me down, pause me, make me repeat myself,
whatever you like, I won't mind at all.

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You are in complete
control of your learning.

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A quick reminder,
there are notes attached to each video.

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This section is usually filled with
additional information that will

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enhance your knowledge should you want
to dive deeper into related topics.

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Get in the habit of checking this space,
and

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I'll do my best to remind you when I've
put info there that you just have to see.

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One more tip,

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remember that there is a community of
fellow learners also taking this course.

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I encourage you to lean on each other.

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If you have a question,
make sure to ask it.

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Our community is very friendly and
approachable.

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Also, remember,

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nothing helps to cement your learning
better than answering a question.

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Make sure to check out the community
throughout the course, and

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see if you can help out a fellow learner.

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We've established that NumPy is
extremely popular in many fields of

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the Python landscape.

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But what is it exactly?

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NumPy is short for numerical Python.

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It deals with numbers.

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So that makes sense, all those
applications that I mentioned would indeed

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need to use numbers and
math equations in some shape or form.

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But as someone who is actively learning
Python, you might cleverly state,

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wait a second, I can use numbers and
do math just fine in plain old Python.

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What's the big deal?

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Why do we need this?

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That's a wonderful question and
the short answer is that NumPy is much

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faster than the straight Python approach,
no matter how great of a coder you are.

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It leans on a paradigm which we'll
get into here shortly called

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array programming.

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It completely removes the need to loop
over your data which speeds things up

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tremendously.

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NumPy also provides additional
mathematical abilities

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not available in standard Python.

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Many numerical concepts have
been extracted away for you and

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provided as functions.

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Chances are you probably aren't going
to use all of those helper functions.

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However, the applications
that you're building, you know,

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the ones that are relying on the library,
they most likely will.

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NumPy exposes concepts from linear
algebra, matrix multiplication,

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fourier transformations and many more
themes that you might remember from

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your math class if math
is in your area of study.

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Now, just a heads up,
it's totally fine is math isn't your jam.

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It really doesn't need to be.

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That's kind of the beauty
of these abstractions.

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You'll use them when you need them.

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My advice is just to stay focus
on where we're headed and

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don't let the shiny tools and
terms distract you too much.

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I'll point out what I think is important
at this part of your learning journey.

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Now, believe it or

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not, that was the short answer to the why
would you want to use NumPy question.

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The long answer is gonna
take me a couple of videos

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to get you to see
the beauty that is NumPy.

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One of the more challenging tasks
of picking up NumPy is simply just

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remembering to how to use
the object that it provides.

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So I was thinking of facing
that challenge head on.

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Let's do this.

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Let's build a Jupiter Notebook together.

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And then you'd have a reference, and we
can kind of treat it like a cheat sheet.

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You can then quickly glance at it or

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even practice some more with
the datasets that we build up.

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Sound good?

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Speaking of practice,
that gives me a great idea.

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Have you heard of the movement
called 100 days of code?

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It's a wonderful idea that the life-long
learner, Alexander Callaway came up with.

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The way it works is this,

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you publicly commit to coding at
least an hour a day for 100 days.

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You post about it on social media,
usually Twitter, and

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you hold yourself accountable.

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It's wonderful for learning.

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Steady practice will
strengthen your skills.

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It creates a great habit of learning.

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It also seems like a great way to
explore the NumPy array data structure.

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We can use it to track and
analyze our time.

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The only downside that I can see is that
it might create some pretty mega tweets.

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If you are committing to learning NumPy
and creating a log to help track and

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analyze your 100 days of code in NumPy,

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reporting on your learning is going to
create a tongue twister of a tweet.

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You'll figure it out.

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So what are we waiting for?

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What do you say we get things all set up?