Why the Engineer Mentality Is a Key Part of Data Science

When it comes to DS education, nowadays there is a lot of emphasis given in one of two things: the math aspect of it, and the complex algorithms of deep learning systems. Although all this is essential, particularly if you want to be a future-proof data science professional, there is much more to the field than that. Namely, the engineer mentality is something that you need to cultivate, since at its core, data science is an engineering discipline. I don’t mean that in a software manner, but more of a practicality and efficiency oriented approach to building a system.

This is largely due to the scaling dimension of a data science metric or model. Unfortunately most data science “educators” fail to elaborate on this point, since they focus mainly on parroting other people’s work, instead of inciting students to gain a deeper understanding of the methods and processes being taught. Also, scaling something is the filter that distinguishes a robust algorithm from a mediocre one. As we obtain more and more data, having an algorithm that works well on a small dataset only (or one that requires a great deal of parallelization to yield any benefits), is not sustainable. Of course some people are happy with that, since they have a great deal of resources available, which they are happy to rent out. However, we can often obtain good enough results with less resources, through algorithms that have better scaling. Even if most people don’t share this fox-like approach to data science, it doesn’t make it less relevant. After all, many people associate methods with the frameworks particular companies offer, rather than understand the science behind these methods.

Scaling a method up intelligently is the product of three things: 
1. having a deep understanding of a method
2. not relying on an abundance of resources to scale it up
3. being creative about the method, making compromises where necessary, to make it more lightweight

That’s where the engineering mentality comes it. The engineer understands the math, but isn’t concerned about having the perfect solution to a problem. Instead, he cares about having a good enough solution that is reliable and not too costly.

This kind of thinking is what drives the development of modern optimization systems, which are an important part of AI. Artificial Intelligence may involve things like deep learning networks, but there is more to it than that. So, if you want to delve more into this field and its numerous applications in data science, cultivating this engineering mentality is the optimal way to go. Perhaps not the absolute best one, but definitely one that works well and is efficient enough!
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