Discovering the ‘Science’ in Data Science – Part 1
Updated: Aug 15, 2021
The term “Data Science”, the modern version, was coined in 2008. It has ever since taken the world by storm! Revolutionary as the idea is, it has, quite understandably, created some confusion too – in that it encompasses such a broad area that if you ask ten people what it means you may get ten completely different and sometimes contradictory answers.
We capture, collect, persist, stream, clean, transform, query, analyze and visualize data. That’s a lot of work and that’s been the traditional way of dealing with data. Throw in ‘big data’ into the mix and things and terminology confusion increasing. What do I do? Do I term it Big Data or Data Science? Or is it Data Science with big data? Irrespective, what is the state of data and how do we qualify it?
The basic question however to first understand. What does the ‘science of data’ mean?
I will attempt to answer that question in this first blog post.
I would encourage anyone who is interested in this area and wants to build a career in Data Science to pause a bit and think how we can use science to maybe explore and discover hidden things in data.
My first stop in the process was asking Google the definition of science (after all who knows more stuff than Google!!) and here is what I got: “the intellectual and practical activity encompassing the systematic study of the structure and behavior of the physical and natural world through observation and experiment.” That got me thinking. How would we be able to apply this definition of science to data? And, the more I thought and researched, I began to believe that the definition of science needed a little tweaking. In my opinion – here is what science is – “the intellectual and practical activity encompassing the systematic study of the structure and behavior of the physical, natural and data world through observation and experiment.” – We are now talking of an activity (intellectual and practical) that involves systematic study of the structure and behavior of ‘data’ through observation and experiment.
Click here to read Part 2