Data Science on Demand

We’ve landed on five things that must happen to manage and develop a data science department effectively. In this post in an ongoing series, we look at #2 — Tracking Time.

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Trying to explain to college students or big corporate types that GPT doesn’t “solve” the need for engineers feels like swimming upstream. I’m sure the next 5 years will be met with broken expectations and using GenAI to create a pile of terrible systems at a scale that wasn’t humanly possible before. After that, engineers will be in high demand to maintain and build upon the mess.

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In this post, get insights into the remarkable accessibility and user-friendliness of large language models (LLMs). One key takeaway is the simplicity of integrating this technology into our analytical processes; it takes about 10 lines of code to incorporate the power behind ChatGPT into an analysis.

(Photo by Mohamed Nohassi via Unsplash)

Building an indispensable data science department requires much more than great data science. Contrary to an in-house data science team, which only needs to build one successful data science department, at System2, we build a new department for every client. We’ve landed on five things that must happen to effectively manage and develop a data science department.

(Photo by Igor Omilaev on Unsplash)

What’s a Data Scientist in the big city to do when he finds himself lonely and single for the first time in 17 years? Turn to census data for matchmaking intel, that’s what.