by Angela Guess
Yasmeen Ahmad of Teradata recently wrote in Forbes, “Trying to explain what I do to friends and family can be difficult. They’re intrigued by the title. Data Scientist. But ‘what does it mean, exactly?’ Invariably, the mystery deepens as puzzlers wrestle with the idea of a data and science mash-up. And as conversations continue, the whole thing sounds more and more like witchcraft. I work with car manufacturers like Volvo, to design a future where no passenger is ever hurt or killed in an accident again. Think what that would mean. Imagine the impact on all of us. At the same time, I’m working with Amazon-type companies to ensure that your purchase is moved to the nearest distribution centre before you’ve even clicked the buy button. Then delivered in 60 minutes.”
Ahmad goes on, “The reason data science can’t be fully automated at the moment is because the granular discussions and decision-making that lead to a workable solution haven’t happened yet. In other words, we’re still waiting for the rulebook to be written. Any analyst can follow a data cookbook that provides the ingredients – a specific set of datasets and algorithms – and a straightforward recipe. However, you need a data scientist to create that cookbook in the first place. He, or she, has to think outside-the-box to create something fresh and innovative – from scratch. Of course, you have to have in-depth, wide-ranging knowledge and experience to write a rulebook. But that’s not all. A data scientist has to be a natural problem solver. In fact, the full palette of skills is hard to pin down. Moreover, it’s nigh-on impossible to teach a good candidate to be great at this. Either you’ve got it, or you haven’t.”
Photo credit: Flickr/ jspatchwork