If you are reading this post, the chances are that you have read this numerous times: “Data Scientist — The sexiest job of the 21st century” — the article that was published in Harvard Business Review, co-authored by Thomas H. Davenport & D J Patil. This was published October 2012.
Fast forward just to April 2014, an interesting WSJ blog post by none other than Thomas H. Davenport himself said It’s Already Time to Kill the “Data Scientist” Title. Its great that he put a word of caution very early on. However, the interest in Data Science still seems to be growing like wildfire if you go by the buzz, the number of specialized training schools, the enrollments at the MOOC platforms. Udacity has a great post on the skills for the loosely defined term “Data Scientist”
With Big Data already off the charts, last week Gartner announced “Machine Learning” as the king of hype. Even John Giannandrea, Google’s Head of AI at the Google I/O 2016 is rather surprised at amount of interest in AI among the developer community in general if the downloads for their Tensorflow library is anything to go by.
Every technology company is building tools and platforms to put data to work for themselves and their customers and continue to invest heavily.
So, if you are looking to build your career in Data Science or a budding Data Science professional or even an Founder/Entrepreneur exploring opportunities with Data Science, should you continue to focus and invest your time on Data Science?
The answer to me seems to be a resounding YES. The fundamentals are in place — kind of a perfect storm — the humongous amount of data, the ultra cheap computing power, the advancements in the core technologies and the availability of developer and end user tools and platforms utilizing these advancements.
Whether one becomes a Data Science Unicorn or not, only time can tell and how you make progress along the journey. Its like any start up, you won’t know until you start the journey and cover the distance.
The important part is that you truly enjoy the journey.
When it comes to big data, one thing seemingly everyone can agree on is that organizations face a shortfall of data science talent. After all, the ideal data scientists aren’t just wunderkinds in advanced mathematics and statistics, they’re creative, non-linear thinkers with excellent communication skills. In popular parlance they’re unicorns — magical creatures that don’t exist.
In the process, Rogers has helped to define what Intel looks for in its data scientists, and it’s not unicorns who have a background in math, statistics, physical science or hard science; the ability to write production-level code; and the ability to talk to business people in their own language.
“You don’t have to be a unicorn,” he says. “We’re looking for people who have one of the major skill sets and some comfort level with the others — the ability to be creative, handle ambiguity and communicate well