The Pulse

Inspired by a recent birth in the System2 family, we decided to see who the retail winners and losers were when families prepping for a new baby took out their wallets.

Note: Some names of specific companies have been redacted in the following analysis. If you would like a free copy of the unredacted report, please email info@sstm2.com.

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Web scraping and hacking are not the same. Read this quick primer, learn the difference, and enjoy being more informed. This adorable little kitten sure does!

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Life must have been simpler when everyone thought the Earth was flat. Unfortunately back then, data science didn’t exist as a profession. But assuming we could achieve a flat Earth, how would things be better for data science (or anyone)?

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At System2, we love feedback; it drives improvement. We know that 20% of client projects will be deemed "a waste of time," but that doesn't mean the overall initiative isn't a success. Let us explain.

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One of our own recently attended two economic conferences in Europe and presented a paper on nowcasting with random forests at one of them. Here, we’ll take a look at the four points the paper focuses one: Data Quality, Mixed Frequency, Missing Observations, and Regression Leaves.

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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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