Python is a ridiculously popular language for programming and data science (currently the third most widely used in the world1 ) which is one of many reasons we’re using it for this course. The language itself is different from the programming environment used to write code in it, just as “English” is different from “Microsoft Word” and “Google Docs.” A programming environment is just a fancy name for a tool or application used to write programs. At a minimum, it must include a way to edit (write and revise) code, and a way to execute (run) it.
There are many different programming environments data scientists use to write Python code, just as there are many different word processing apps people use to write English. The choice largely comes down to personal preference. Some use full-blown IDEs (“integrated development environments”) like Spyder or Atom; some use text-based tools like Notepad++ or vim. In this class, we’re going to use the friendly and minimalistic “Jupyter Notebooks” environment since it’s appropriate for an intro experience.