Practical Debugging for Data Science

Now before writing about this topic, I did a quick Google Search to see how much of this is already covered and quickly observed a phenomenon that I see increasingly in the field — Data Science = Modelling, at best, Modelling + Data Processing. Open a MOOC, they talk about the different models and architectures, go to a bootcamp, they will make you write code to fit and train a machine learning model.

While I understand why the MOOCs and bootcamps take this route (because these machine learning models are at the heart of data science), they sure have made it seem like machine learning models are the only thing in Data Science. But Data Science in practice is radically different. There are no curated datasets or crisply formatted notebooks, only a deluge of unorganized, unclean data, and complex processes. And to effectively practice Data Science there, you need to be a good programmer. Period.

Author: Manu Joseph

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