My name is Nick and I am stuck in the middle. In fact, most of the analytics community is stuck in the middle somewhere. Search LinkedIn and you will see thousands of professionals calling themselves “data scientist.” However, there is no unified definition of what a data scientist is. What do they study? What tools do they use? How many years do they have to be labeled a data analyst and climb up the totem pole of professional networking to be recognized as a data scientist? How many posts to GitHub or Kaggle competitions must one enter? Does one use R or Python? Does simply being an Excel Jedi make someone a data scientist? Do you have to know advanced calculus and be an expert in linear algebra to earn the title of data scientist. These questions plague the analytics community and there are no simple answers.
Through my own work in the advanced analytics community I have witnessed a wide range of data skillsets in many accomplished data miracle workers. These skillsets have all stemmed from different backgrounds: economics, mathematics, engineering, computer science, policy, international relations. Not one of these accomplished analysts followed the same path, yet all have risen to the top in their respective analytics communities. This is not the traditional route to become a scientist. One does not become a data scientist by taking a test, performing hard labor to earn a PhD, or taking a 1,000 hour online course. This reputation is earned. But how?
Through the following blog posts, podcast interviews, and a persistent asking of the question, “What is a data scientist?”, I hope to unstuck both myself and other confused data nerds from the middle ground between data analyst and data scientist. More importantly, I want this to be an opportunity to drive a discussion that is lacking in the analytics community. For if we are to be translators of nerd, how do we define our profession?