Fresh from another successful DataConnect Conference, Lauren Burke-McCarthy led our July session of Data & Analytics Wednesday talking about how to survive and succeed as a solo practitioner of data science.

Being a “solo practitioner” could mean being the only data scientist on your team, being siloed in some way, or even being a freelance contractor. The strategies that Lauren presented were focused on how to best communicate and set expectations with stakeholders. We’ve all been there when a project has gone off the rails because what a practitioner implemented didn’t match at all what a stakeholder had envisioned. Let’s nip these misalignments in the bud as best we can before they can blossom into fully grown issues.

In fact it turns out many (perhaps most!) of these techniques could work for us in any data-related role we were in. What after all even is a data scientist? Lauren also took a crack at answering that age-old question off the top of her head. To paraphrase her answer, a Data Scientist focuses on models and experiments to make future-looking prediction — vs a Data Analyst works on analysis of current and historical data to identify trends and develop insights. If those two things seem to blur into each other at times, that just shows how Lauren’s advice on processes and communication works for both! Perhaps even those of us who have now added “AI” in our job titles? Could well be…

Looking to learn more about these techniques? Lauren was kind enough to provide us with our slides so you can take a look for yourself:

And, of course, pictures!

Please join us next month when the ever-delightful Matt Gershoff will be in town to discuss how to think purposely about data as we move towards privacy by design.