Focusing on these points has led to my continuous adoption of a query library -- a git repository for saving and sharing commonly (and uncommonly) used queries, all while tracking any changes made to these queries over time. 

I used the 1 Second Everyday app to take a series of one second videos of what my work as a data scientist at an IoT startup looked like during the month of August.

I’m freshly back from JupyterCon in NY and still feeling the bubbly optimism that comes with bringing all you’ve learned at a conference back to your office. In that spirit, I wanted to share some of the coolest and most interesting things I learned with you all.

In hopes that others can follow their example, I’d like to share some of the things that “good guys in tech” have done to help my career.

I have a fantastic coworker who I've been pair programming a lot with lately, and he does one thing that I wish everyone did.

Thoughts on why imposter syndrome is so prevalent in data science, how I deal with it personally, and ways we can encourage people who are feeling the impact.

Using data to find the most depressing Christmas song (and rooting for 'Have Yourself a Merry Little Christmas').

Not all of the data was equally relevant to the questions we ask of it, not all of the data is trustworthy, and not all analyses are neatly reproducible.