Installing and managing your Python environment can be tricky, as this famous XKCD comic has aptly pointed out. After many years of experimentation, here is my advice on the best way to install Jupyter Lab (and notebook).
You may think I am crazy, but I really enjoy setting up my computer. I am the kind of person who likes to wipe their computer clean every so often and start fresh with a clean slate. This blog post is an update to an earlier blog post from 2020. A lot has changed since then, so here is a look at my current setup.
As a data scientist one of the most common questions I get from colleagues and clients is how to get started on learning R. There is a plethora of great options out there today. Some of which are paid, and some of which are free.
One of the funnest (and most frustrating) parts of data science is the vast array of tools available to us. It can be overwhelming where to start. Every now and then I like to completely wipe my computer clean, and then reinstall everything from scratch. This helps clean up my computer, and make sure everything is running smoothly.
When a UFC match ends with a knock-out or submission there is never any doubt who the better fighter was. But only 54% of fights end with a knock-out or submission. The other 45% of fights go to the judges score cards.
During my Masters of Data Science I was often working on many GitHub repos at the same time. Most of our homework was graded on GitHub.com, so it was important to ensure that after pushing my local repo to GitHub.com that everything rendered correctly.
Jupyter Notebooks are an awesome tool. The standard way to open a Jupyter Notebook is from the command line.
I love using Plotly to build interactive visualizations. The syntax is very similar across R and Python, and plots looks great.