Python set-up #
We are going to start by getting you set-up in Github and Codespaces. I’ll go through how our assignments are going to work in Github Classroom. These tools mean that you can do all of your coding in your browser using what are called Jupyter notebooks, but in an environment that acts like the common VS Code desktop setup, including version control with git and Github.
Our Day One Set-Up:
Sign-up for a Github account: https://github.com/signup. Use your Elon email address.
Set-up your personal Github page. You should add your own readme.md file.
Linking your Github account to our Github Classroom using the link on Moodle by accepting Lab00.
Open a VS Code instance in Github Codespaces. Make sure that we can see files associated with the lab. Learn about the layout of VS Code, including how to install packages.
Learn about git. Where are our files when we work in a Codespace? What are repos? How can we move files around and submit assignments? How can you download your work to your local drive?
Learn about Jupyter Notebooks. Jupytor Notebooks let you mix Python and Markdown and are what we will do our work in.
See the next few sections for more on each of these topics.
Other resources and tools#
From VS Code, we can also use Jupyter notebooks. I would encourage you to get very comfortable with the Jupyter notebook format and VS Code. We’ll go over everything in class as well.
The Writing Code section from Coding for Economists is a great place to start. His Code Preliminaries section also introduces some important ideas, like IDEs, packages, and VS Code.
You can read more about Python and its history in Chapter 1 of Python for Data Analysis, 3E. Chapter 2 discusses Python basics, like Jupyter notebooks, in detail.
Getting started in Python isn’t easy. Even if you’re coming from another programming background, like Java, or a statistical language, like R. Data scientist (and good Twitter follow) Vicki Boykis has written about why getting started in Python can be hard.
This is a nice article on a modern Python data “stack”, especially if you’re coming from R. Note how she suggests using seaborn and polars, two newer packages, in place of matplotlib and pandas.
This course is focused on using Python (and the VS Code IDE, Markdown, etc.) to solve financial problems. However, if you’re interested in building a full set of data/analytics tools, you need to know more.
This article, also by Vicki Boykis, outlines the need to learn three additional tools: Git, SQL, and the command line. I use all three in my day-to-day work, though I’m far from an expert in any of them. If you are thinking about working with data as a career, you should know these three tools. Our DataCamps will cover the basics of git and the command line. We’ll use both inside of Github Classroom and Codespaces.
Our notes touch on SQL, which is the standard database query language.