It makes perfect sense that this job is both new and popular, since every move you make online is actively creating data somewhere for something. Someone has to make sense of that data and discover trends in the data to see if the data is useful. That is the job of the data scientist. But how does the data scientist go about the job? Here are the three skills and three tools that every data scientist should master.
It’s no secret. Tech talent is in high demand across industries, but finding people with the skill sets to fill these roles has been challenging, causing competition amongst businesses for talent in tech — in coding, UX design, data science, and digital marketing.
From my past experiences at both large tech companies and small startups, there is a tendency to assign data projects to rockstar developers and let them run with it. The results are often interesting from a data science standpoint, but nonfunctional from a product standpoint.
The problem with this approach is twofold. First, how the developers approach the data and how the data integrates into the product are fundamentally different. Second, datasets in the real world are messy—inaccurate, imprecise, and unstructured—and this can render them unusable in their initial format.
At VeryApt, every data project is allocated developer hours, analyst hours, and budget for external resources (such as tools and supporting datasets). With these constraints established, our analysts and developers work together to create clear goals for the product and its associated site integration. This allows the team to narrowly focus their efforts and determine if the data available can produce useable results. Continue reading →
What makes a job sexy? Money, power, sex appeal? To us, finding a job you love, that is totally in demand (read: people want you), is what’s sexy. With that definition, there is no sexier job these days than….data scientist.
According to IBM, we create 2.5 quintillion bytes of data every day. This data comes from all sorts of places; social media, e-mail, purchase transactions, digital photos and videos, and log data (data extracted from web servers), just to name a few. This data is too large and complex to handle with traditional means of data management, and data scientists are the individuals who know what to do with it.