>> I was pretty excited when I found out that Python
is one of the most popular languages for data science.
Admittedly though, I was a little bit nervous
when Francesca mentioned Jupyter Notebooks.
I didn't have much experience with those.
I was thrilled when I found out that VS Code, my favorite editor,
has packages and extensions for Python,
Jupyter Notebooks, and Azure Machine Learning.
I think I'll feel right at home
because I'll be in my Visual Studio Code environment.
So let's get our local environment setup and hopefully,
you'll feel comfortable in VS Code too.
As always, there's additional documentation both on
the screen and link in the description
down below. Let's get started.
We need to download and install three things:
Visual Studio Code, Python, and Anaconda.
I'm going to verify that I have Python
installed in my Anaconda prompt.
That's looking good. Next, I'm
going to create an Anaconda environment.
I'm going to take the advice from
the Visual Studio Code Team and
create an environment ready for data science.
I'm going to call this environment devintrods.
I'm going to specify version 3.7 for Python.
I'm going to also install Pandas, Jupyter, Seaborn,
Scikit-learn, Keras, and TensorFlow,
some of the most popular tools for data science.
Once the Anaconda environment has been created,
we can activate it with
conda activate and the name of our environment.
Next, we want to install Azure ML SDK.
I'm going to specify two parameters here,
the AutoML and explain features,
which will really help with some of
those advanced topics that
Francesca will lead us through later on.
We also want to make sure that we have access
to the AutoML training tools.
So I'll go ahead and install that as well.
I think our environment is set up.
Now we should set up Visual Studio Code.
There are three extensions we need.
Head over to the extension marketplace
and install the Python extension,
the Azure Account extension,
which will allow us to sign in to Azure within Visual Studio Code,
and the Azure Machine Learning Extension.
Once we have all three of those installed,
we can verify that everything is
working inside of Visual Studio Code.
First, I want to sign in to Azure.
I'll open up the command palette and choose "Azure Sign In".
This will open up a new browser tab and
allow me to sign in to Azure through the browser.
Notice, since I've already signed into
the Azure portal in another tab,
it's just a quick click of a button.
Now you can see that I'm signed in to
Azure down here at the bottom of VS Code.
Before getting started, I already created
a folder where all of this code is going to be stored.
It's called dev intro data science.
Now I'm going to create a new file,
and we're going to call this bike-share.ipynb.
This will create a new Jupyter Notebook file.
Because we've installed the Python extension,
and we've installed Jupyter through our Anaconda environment,
VS Code will automatically convert
this file into a Jupyter Notebook interface.
There's a few things to notice to make sure that they're correct.
One is that VS Code is referring to the right Python.
Down here at the bottom left of your VS Code window,
you should be able to see the correct version of Python,
and if you hover over it,
you should be able to see that this is using
the Anaconda environment that we just created.
Next, you should make sure that your Jupyter kernel is also
set to that same Python version in that same Anaconda environment.
Now your local environment is all set up.