by Angela Guess
Michael Li recently wrote in Entrepreneur.com, “At The Data Incubator, I’ve spoken to hundreds of employers looking to hire data scientists — particularly those with advanced degrees. With all the hype surrounding big data these days, it’s unsurprising that there’s as much misinformation floating around as there are facts. Unfortunately, hiring managers often fall victim to believing many common misconceptions to be true. Here are three facts about data science that hiring managers may not understand.”
Li goes on, “(1) Data scientists and software engineers are not the same. Believing the two are synonymous is a common mistake. While engineers with software development backgrounds do sometimes call themselves data scientists to capitalize on the associated salary premium, the results tend to be mediocre. Engineers are trained to fix bugs in programming, but when they lack a deeper understanding of probability and statistics, they often struggle to solve statistical bugs. Even though their code itself might be just fine, their predictions will be off if they built their code upon flawed statistics. In order to create truly scalable predictive models, deeper and more nuanced statistical understanding is necessary — and many software engineers are lacking where data scientists are not.”
He continues, “(2) Big data is more than statistics and intelligence. Those with little to no experience with software development, many hiring managers among them, often fail to recognize this. Keeping a plant alive in your office window is quite different from running a farm, right? When you scale up, you have to change the way you do things in order to make them work. The same concept applies when you add more data. Big data strains the classic models of computation and eventually renders them ineffective.”
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