Hello, humans of the internet!
Everyone is talking about Data Science. I wonder how many actually know what it stands for. I myself do have a definition for it. Or at least I know what to tell my friends when they ask me about it. The funny thing though is, that there isn’t really a clear definition.
Hal Varian gave the statement below in an interview in 2009 with McKinsey. Many people quoted him on that. We all heard “Data Scientist is the sexiest job in the 21st century”.
I keep saying the sexy job in the next ten years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s? The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids.- Hal Varian
Statistics and Mathematics can help you understand your data. Therefore a Data Scientist is a Statistician.
More than Statistics
Most people screak when they hear the words Statistics or Mathematics. Not a Data Scientist.
As a statistician, I was getting tired of explaining that no, I don’t spend my time writing down baseball or cricket scores. I think “Data Science” better describes what we actually do: a combination of computer hacking, data analysis, and problem solving. – David Smith
He wrote this on a blog in May 2011. A Data Scientist is, therefore, a hacker and a problem solver.
This also goes along with a very famous Venn Diagram from the same blog which you can find here. Data Science in the diagram is a mixture of hacking, statistics and expertise. A Data Scientist cannot work with only two of them. The lack of one of these three is actually dangerous! Referred to as the “Danger Zone” in the Venn Diagram.
As somebody with a strong background in statistics, mathematics or any other number-heavy field, you might think that decisions are made on a logic or rational basis.
Many of the heavily-recruited individuals with advanced degrees in economics, mathematics, or statistics struggle with communicating their insights to others effectively—essentially, telling the story of their numbers.
Brent Dykes wrote in this article that often the storytelling part around the data gets lost. Hinting that as a Data Scientist, you also have to pass on the information you gained from the data in a story.
To sum up
So what do all those quotes teach us?
There is no widely accepted boundary for what’s inside and outside of data science’s scope. – Pete Warden
In 2011 Pete Warden wrote on the O’Reilly blog some arguments against the term Data Scientist. He himself actually does like the term and as in the first quote, it makes it easier to describe what he actually does. But of course the name does not really make sense.
For this post I had to read a lot of articles and posts. I found it a very interesting journey and certainly learned a lot. Most of all, that things are evolving and changing every day. If you want to read up on the history of Data science, I can recommend you this article 😀
Let me know your definition of the term Data Scientist down below in the comments!
Thank you for reading!