What is Data Science?

There needs an answer to a question which the new generation probably ask: “What is Data Science?” - And that is an excellent question if one wants to be in this field. To different people, this means different things, but at its core, data science is using data to answer questions. This definition is pretty broad, and that’s because it’s a pretty full field!

Considering the standards data science can involve:

•  Statistics, computer science, mathematics

•  Data cleaning and formatting

•  Data visualization

An Economist Special Report sums up this melange of skills well - they state that a data scientist is a person:

“Who combines the skills of software programmer, statistician and storyteller slash artist to extract the nuggets of gold hidden under mountains of data.”

Which expertise do we need to become a Data Scientist?

And to answer this, I have this illustrative Venn diagram, in which data science is the intersection of three sectors - Substantive expertise, hacking skills, and math and statistics.

To explain a little on what it means by this, we know that we use data science to answer questions - so first, we need to have enough expertise in the area that we want to ask about to formulate our questions and to know what sorts of data are appropriate to answer that question. Once we have our question and relevant data, we know from the types of data that data science works with, often it needs to undergo significant cleaning and formatting - and this usually takes computer programming slash “hacking” skills. Finally, once we have our data, we need to analyze it, and this often takes math and stats knowledge.

The main reason data scientists are in such demand is that in this innovative world most of the answers aren’t already outlined - a data scientist needs to be somebody who knows how to find answers to novel problems.