WEBVTT

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>> I was pretty excited when
I found out that Python

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is one of the most popular
languages for data science.

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Admittedly though, I was
a little bit nervous

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when Francesca mentioned
Jupyter Notebooks.

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I didn't have much
experience with those.

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I was thrilled when I found out
that VS Code, my favorite editor,

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has packages and
extensions for Python,

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Jupyter Notebooks, and
Azure Machine Learning.

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I think I'll feel right at home

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because I'll be in my Visual
Studio Code environment.

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So let's get our local
environment setup and hopefully,

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you'll feel comfortable
in VS Code too.

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As always, there's additional
documentation both on

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the screen and link
in the description

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down below. Let's get started.

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We need to download and
install three things:

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Visual Studio Code,
Python, and Anaconda.

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I'm going to verify
that I have Python

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installed in my Anaconda prompt.

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That's looking good. Next, I'm

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going to create an
Anaconda environment.

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I'm going to take the advice from

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the Visual Studio Code Team and

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create an environment
ready for data science.

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I'm going to call this
environment devintrods.

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I'm going to specify
version 3.7 for Python.

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I'm going to also install
Pandas, Jupyter, Seaborn,

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Scikit-learn, Keras, and TensorFlow,

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some of the most popular
tools for data science.

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Once the Anaconda environment
has been created,

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we can activate it with

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conda activate and the
name of our environment.

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Next, we want to
install Azure ML SDK.

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I'm going to specify
two parameters here,

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the AutoML and explain features,

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which will really help with some of

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those advanced topics that

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Francesca will lead
us through later on.

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We also want to make
sure that we have access

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to the AutoML training tools.

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So I'll go ahead and
install that as well.

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I think our environment is set up.

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Now we should set up
Visual Studio Code.

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There are three extensions we need.

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Head over to the
extension marketplace

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and install the Python extension,

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the Azure Account extension,

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which will allow us to sign in to
Azure within Visual Studio Code,

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and the Azure Machine
Learning Extension.

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Once we have all three
of those installed,

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we can verify that everything is

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working inside of Visual Studio Code.

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First, I want to sign in to Azure.

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I'll open up the command palette
and choose "Azure Sign In".

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This will open up a
new browser tab and

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allow me to sign in to
Azure through the browser.

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Notice, since I've
already signed into

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the Azure portal in another tab,

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it's just a quick click of a button.

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Now you can see that I'm signed in to

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Azure down here at the
bottom of VS Code.

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Before getting started,
I already created

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a folder where all of this
code is going to be stored.

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It's called dev intro data science.

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Now I'm going to create a new file,

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and we're going to call
this bike-share.ipynb.

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This will create a new
Jupyter Notebook file.

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Because we've installed
the Python extension,

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and we've installed Jupyter
through our Anaconda environment,

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VS Code will automatically convert

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this file into a Jupyter
Notebook interface.

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There's a few things to notice to
make sure that they're correct.

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One is that VS Code is
referring to the right Python.

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Down here at the bottom left
of your VS Code window,

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you should be able to see the
correct version of Python,

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and if you hover over it,

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you should be able to
see that this is using

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the Anaconda environment
that we just created.

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Next, you should make sure that
your Jupyter kernel is also

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set to that same Python version in
that same Anaconda environment.

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Now your local environment
is all set up.