The first order of business is to create a new folder where this project will live onto your desktop.
There are no limits to the number of environments you can have since they’re just directories containing a few scripts - these are easily created using the virtualenv or pyenv command-line tools. This means the environment allows us to use different versions of modules for different projects, without causing conflicts between the versions. What is a virtual environment and why do we need one? In short, Python virtual environments are used to create an isolated environment for Python projects. Saving our data to a csv - creating our own dataset.A step-by-step code and explanations on how to scrape the NBA data from Basketball Reference.Installing IPython, Jupyter Notebook, BeautifulSoup4, and Pandas within the virtual environment.This is what we will focus on in this step-by-step guide. This is where you gather the data to use statistics and computer science to uncover those insights - your technical skills. The next element in data analysis involves the how. This is the scope of the project (context, needs, vision, possible outcomes) where you ask the right questions to turn your data into insights - your analytical skills.
When analyzing data, you always start with the why.
