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[MUSIC]
0:00
Hi, I'm Craig and I'm a developer.
0:09
In this course we're going to be taking
a look at Python's wonderful data
0:11
library, NumPy.
0:14
You'll find NumPy in all
sorts of applications, and
0:16
therefore, it's fairly critical that you
have an understanding of its fundamentals.
0:18
It appears in every direction
you may head in Python.
0:23
Should you plan to get into data analysis,
there's scientific computing, or
0:25
even machine learning,
you're going to bump into NumPy and
0:28
that's what this course is all about.
0:31
It's an introduction.
0:33
I want to introduce you
to the library early on.
0:35
I'll walk through hands-on examples
that will give you a great introduction
0:37
to the library, its main concepts and
the surrounding terminology.
0:40
When you've complete the course,
you'll have a great foundation and
0:44
you'll know where to turn when you
need more specific information.
0:46
But, before we get started, let's take
some time to make sure that you're
0:50
familiar with your learning environment.
0:54
First off, there are some prerequisites to
this course, and I'd love for you to make
0:56
sure that we're on the same page about
where you are in your coding journey.
0:59
There's speed control on the video player,
so please feel free to speed me up or slow
1:03
me down, pause me, make me repeat myself,
whatever you like, I won't mind at all.
1:08
You are in complete
control of your learning.
1:12
A quick reminder,
there are notes attached to each video.
1:15
This section is usually filled with
additional information that will
1:18
enhance your knowledge should you want
to dive deeper into related topics.
1:21
Get in the habit of checking this space,
and
1:24
I'll do my best to remind you when I've
put info there that you just have to see.
1:26
One more tip,
1:32
remember that there is a community of
fellow learners also taking this course.
1:33
I encourage you to lean on each other.
1:36
If you have a question,
make sure to ask it.
1:38
Our community is very friendly and
approachable.
1:40
Also, remember,
1:43
nothing helps to cement your learning
better than answering a question.
1:43
Make sure to check out the community
throughout the course, and
1:47
see if you can help out a fellow learner.
1:49
We've established that NumPy is
extremely popular in many fields of
1:52
the Python landscape.
1:55
But what is it exactly?
1:57
NumPy is short for numerical Python.
1:59
It deals with numbers.
2:02
So that makes sense, all those
applications that I mentioned would indeed
2:03
need to use numbers and
math equations in some shape or form.
2:07
But as someone who is actively learning
Python, you might cleverly state,
2:11
wait a second, I can use numbers and
do math just fine in plain old Python.
2:15
What's the big deal?
2:19
Why do we need this?
2:20
That's a wonderful question and
the short answer is that NumPy is much
2:21
faster than the straight Python approach,
no matter how great of a coder you are.
2:25
It leans on a paradigm which we'll
get into here shortly called
2:29
array programming.
2:32
It completely removes the need to loop
over your data which speeds things up
2:33
tremendously.
2:37
NumPy also provides additional
mathematical abilities
2:39
not available in standard Python.
2:42
Many numerical concepts have
been extracted away for you and
2:44
provided as functions.
2:47
Chances are you probably aren't going
to use all of those helper functions.
2:49
However, the applications
that you're building, you know,
2:53
the ones that are relying on the library,
they most likely will.
2:55
NumPy exposes concepts from linear
algebra, matrix multiplication,
2:59
fourier transformations and many more
themes that you might remember from
3:03
your math class if math
is in your area of study.
3:07
Now, just a heads up,
it's totally fine is math isn't your jam.
3:10
It really doesn't need to be.
3:14
That's kind of the beauty
of these abstractions.
3:15
You'll use them when you need them.
3:17
My advice is just to stay focus
on where we're headed and
3:19
don't let the shiny tools and
terms distract you too much.
3:22
I'll point out what I think is important
at this part of your learning journey.
3:25
Now, believe it or
3:28
not, that was the short answer to the why
would you want to use NumPy question.
3:29
The long answer is gonna
take me a couple of videos
3:34
to get you to see
the beauty that is NumPy.
3:37
One of the more challenging tasks
of picking up NumPy is simply just
3:40
remembering to how to use
the object that it provides.
3:43
So I was thinking of facing
that challenge head on.
3:46
Let's do this.
3:49
Let's build a Jupiter Notebook together.
3:49
And then you'd have a reference, and we
can kind of treat it like a cheat sheet.
3:52
You can then quickly glance at it or
3:55
even practice some more with
the datasets that we build up.
3:57
Sound good?
3:59
Speaking of practice,
that gives me a great idea.
4:01
Have you heard of the movement
called 100 days of code?
4:03
It's a wonderful idea that the life-long
learner, Alexander Callaway came up with.
4:06
The way it works is this,
4:11
you publicly commit to coding at
least an hour a day for 100 days.
4:12
You post about it on social media,
usually Twitter, and
4:16
you hold yourself accountable.
4:19
It's wonderful for learning.
4:21
Steady practice will
strengthen your skills.
4:23
It creates a great habit of learning.
4:25
It also seems like a great way to
explore the NumPy array data structure.
4:27
We can use it to track and
analyze our time.
4:32
The only downside that I can see is that
it might create some pretty mega tweets.
4:34
If you are committing to learning NumPy
and creating a log to help track and
4:39
analyze your 100 days of code in NumPy,
4:42
reporting on your learning is going to
create a tongue twister of a tweet.
4:44
You'll figure it out.
4:48
So what are we waiting for?
4:50
What do you say we get things all set up?
4:51
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