In data science, modeling success varies by continent because models that work in one region may not work in another, requiring creativity and experimentation with hyperparameters rather than relying on obvious solutions; the primary constraint on the modeling process is the data scientist's skill level, including how many models they know and how proficient they are at writing code, which directly impacts the ability to run experiments and produce results.
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Unlocking Modeling Skills: Experimenting Across Continents! #ShortsAdded:
If you say hey, I want to go into modeling, but I also want to break out by by continent, right? North America, South America, Europe, Africa.
That in itself is an experiment.
Maybe a model works in Western Europe, but it doesn't work in South America.
You know, vis-a-vis the data you're putting in, right?
So, these levers that I'm suggesting to you aren't always just going to be like a list of hyper parameters. They're not always going to be obvious.
That's where your creativity comes in.
Okay? So, keep that in mind. All right.
So, the next part is the modeling skills.
Running experiments.
What is the code you need? And then what are the resources you need to run that code? And by resources, I usually mean time and processing power.
Right?
Because you you are the you are the human resource. You are the data scientist that produces that code, right?
So, here um the soft skill is the modeling skills.
Basically, you know, if I, Dennis, am the data scientist assigned to this, then the correlation, excuse me, the the biggest um constraint is what I meant to say. The biggest constraint on that process is well, how many models does Dennis know?
Right?
You know, obviously models that fit the data, right?
But the but but it's dependent on my skill, right?
My my my modeling skills, how many models I know, and and and how proficient I am at writing that code is the constraint here.
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