Are you a data scientist looking to branch out on your own and start something new?

Maybe you're just looking for a way to work with those exciting libraries that aren't yet

in play at the day job.

Rather than putting everything on the line, quitting your job, and hoping things work out,

maybe you should start a side hustle.

On this episode, you'll meet Keith McCormick, a data scientist who has many irons in the

fire, and he's here to tell us about the different

types of side hustles and why you might want to try or avoid a certain one. This is Talk Pythonomy.

Episode 290, recorded October 1st, 2020. Welcome to Talk Python to me, a weekly podcast on Python, the language, the libraries, the

ecosystem, and the personalities. This is your host, Michael Kennedy. Follow me on Twitter

where I'm at M. Kennedy, and keep up with the show and listen to past episodes at talk python.

And follow the show on Twitter via at talk python.

This episode is brought to you by Linode and Talk Python training.

Please check out the offers during their segments.

It really helps support the show.

I got a joke for you.

What's the world's most popular IDE? Excel. Funny, right? Except many companies really do run on Excel to the point where they

would be much better off using clean and simple programming tools. For many, Python's data

science stack would be vastly better. But moving from Excel to Python is a challenge. Most

data science courses don't focus specifically on the Excel use cases. That's why we've teamed up with

Chris Moffitt from Practical Business Python to create a course tailor-made for helping people learn

just enough Pandas and Jupiter to replace the problematic Excel usage with clean and scalable Python

...