A very practical, very look-behind-the-curtain talk that’s a bit irreverent to the myth of the all-magical, all-fixing, no-oversight-needing data science.
A few weeks ago (January 23rd, 2021 to be precise) I had the pleasure of joining GET Cities for their inaugural Kickoff Summit! GET Cities is an incredible fellowship program building a more inclusive future for tech by fostering community and accelerating growth in underrepresented genders. If you’re looking for […]
Most of us agree we should cross validate–and how to cut up the k-folds. Finally I’ve put down in writing why we do it and where it should actually fit into your workflow.
Why the impact hypothesis is so critical–and why we haven’t been talking about it till now.
Bite-sized bits of data science for the non-data scientist Disclaimer: All * terms to be defined at a later point. As well as many others data scientist: a role that includes basic engineering, analytics, and statistics; often builds machine learning models depending on the company, might be a product analyst, […]
If we want to capitalize on what data science can offer, stakeholders need to communicate with technical teammates in specifics, particulars, and with common understanding.
….or How to Answer Your Own Questions This walk-thru shows how to hunt down the answer to an implementation question. The example references sklearn, github, and python. The post is intended for data scientists new to coding and anyone looking for a guide to answering their own code questions. Does […]