I have detailed instructions on how to install and setup all software used in this course. We will be using:

Python & Python Packages

Python is one of the main programming languages for data science. We will be using python along with Pandas, Numpy, Matplotlib, Plotnine, Papermill, Sktime, and many other packages.

I have setup instructions that will ensure you can reproduce my python software environment - the combination of specific versions of software. Make sure you follow these instructions to make the course reproducible.

Anaconda (Conda)

Anaconda (conda) is a python package manager that allows us to set up reproducible environments so your python environment matches mine as closely as possible). This is important for building software and making reproducible code. I have an environment that you will use to set up with specific python versions and package versions.

Visual Studio Code (VSCode)

This is an Integrated Development Environment that we will use in the course. I highly recommend that you use VSCode in this course because:

• Software development. VSCode is built for software development, a key strength of Python. VSCode has tight Git Integration, it supports Jupyter .ipynb files & has jupyter integration, and a huge extension marketplace for python development that you will be exposed to.
• Exposure to new tools. Even if you are more comfortable or productive in a different IDE, you will learn about VSCode's capabilities which will help you down the road.

Jupyter Notebooks & Labs

We will primarily be using VSCode (Not Jupyter) for the course because it's much better for software development (a huge strength for python). We however will use Jupyter Labs for challenges that require text and for the reporting automation in Part 3 of the course. You will become familiar with Jupyter in the Challenges.

I'm excited to teach you Python + VSCode for Data Science! Let's get going.