The Pulse

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.

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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.

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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.

Is GenAI destined for the same boom and bust as blockchain? Or will it be like the dotcom boom/bust, followed by a lot of incremental progress that changes how society functions? Or will it just be a slow change like electricity?

Call Reports provide a variety of metrics to determine banks’ health. These reports offer a range of metrics useful for assessing a bank's financial condition, including its exposure to specific sectors like commercial real estate (CRE).

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This summer, System2 challenged our bright and industrious interns to create an AI-powered chatbot that can pull information from documents. And they achieved just that. We're extremely proud of their work and want to share their write-up here.

Doing data science, in general, is terrible. Does it have to be? Let’s dig into how the data sausage is made. Read on if you want to dissuade yourself from a career in data science. Skip to the end if you want to know why you should hire System2.