Spoiler Alert:
Economists don’t like tariffs. Economists tend not to like taxes in general; they distort incentives. If you only take home 50% of your paycheck, there’s less incentive to show up at work, and more incentive to hit the beach. But tariffs are particularly disliked as their distortions, in theory, come at a higher cost.
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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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Read MoreBuilding 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.
Read MoreIs 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?
Read MoreCall 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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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.
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One hears a lot about “Big Data.” Everyone seems to want to use it, in combination with AI, to print money and retire at 35. Big data is something System2 deals with every day. But what if someone asks you to come up with a forecast for direct-to-consumer revenue for Canada Goose? Or Allbirds? Historical revenue data can be pretty limited; typically, we’re dealing with 15 to 30 quarterly observations. How do we take big data and use it to forecast small data?
We're living in volatile times, especially for the banking sector.
With the Federal Reserve's interest rate hikes and the ongoing shifts in the commercial real estate industry, it's more important than ever to accurately assess a bank's financial health.
Read MoreModels serve as powerful tools that enable analysts to extract valuable insights from complex data. Moreover, model building gives analysts a tool to uncover biases, identify patterns, and separate real growth from seasonality. However, the famous quote by statistician George Box, "All models are wrong, but some are useful," challenges us to confront the inherent paradoxes that surround the world of modeling.
Read MoreAs a tangible asset not tied to stock market performance, art can be a good hedge against inflation while also providing diversification. Here we'll look at data to try and see what information may be useful for investing in an art piece.
Read MoreThis is the final piece in a three-part series dedicated to sourcing potential investment ideas by reimagining whom we think of as an “influencer.” The goal is not to use AI/ML to evaluate brands. Instead, it’s to use AI/ML to uncover and track micro-influencers who’ve proven they are already elite brand evaluators. If you’re an early investor in private companies, stick around.
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