We’ve been recently looking at how to introduce data science concepts to the wider team, including business analysts, management and engineers.
This post is for anyone and everyone thats ever heard anything about Data Science but are still unclear on what it is, what it means for businesses and how to learn more.
So What is Data Science?
Well the term Data Science itself is heavily overloaded. It’s used in a bunch of different contexts to define a whole variety of different subjects. When trying to sell a concept like this, especially to management teams or senior stakeholders, a term that means nothing and is difficult to explain will simply just be ignored.
Bearing this in mind, we sat and tried to define what Data Science means to us , as depending on who you ask the answer will be slightly different. These answers range from using mathematical models to solve problems with data, investigating data to find insights, using machine learning to solve complex problems… and the list goes on. What we wanted was a concise definition that brings together all these key Data Science concepts into one simple definition.
“Data Science is the exploration, extraction and visualisation of insights from a variety of data types” - and when you look at this, it’s no different to what used to be called Business Intelligence or Business Analytics. Funnily enough - we don’t think it is, it’s a rebranding using some techniques that are exactly the same and others that are slightly matured due to the introduction of machine/deep learning techniques and higher computational power/Big Data/.
You might even have a Data Science team now with a different name…
Why has it blew up?
This is an easy one to answer and the answer is a single word - Data .
Data used to be seen as something that was collected by scientist running experiments. This data was hopefully converted into information after the experiment and was represented in a way for others to consume and understand.
As a Data Scientist, your job role is perfectly explained by that short passage above. You collect some data, explore it and run some “experiments” on a small sample, take your findings to a larger sample, learn something new and then represent your findings in a way that provides insight for the end user.