You’ve built your model, you’ve located your data sources, and you’ve done all the initial processing and ETL to get your data how you want it.
Your data is teeming with potential insights, ready to be teased out by predictive models. But doing that isn’t only about knowing what questions to ask
2020’s been a crazy year, but even with (or, should we say, because of) all the insanity, data science has had a whirlwind 12 months. The
As they say, the proof is in the pudding, and data preparation is where the pudding is put together. Any mistakes you make here will be
Not to sound alarmist or anything, but machine learning (ML) initiatives can be risky. It’s true, they sound amazing, and you hear success stories left and
Let’s say you own a factory that makes computers. You need to have a steady pipeline of parts and raw materials. You can approach this necessity
Like Italian cooking, data science is all about quality ingredients. It’s not enough to simply have a lot of data; you need to make sure the
There’s nothing more frustrating than laboring away at the early stages of your data science project, only to discover that the success of your model is
So, you’ve built a dataset you’re happy with, and your machine learning (ML) model ready to start making predictions left and right. Easy as pie, right?