Discovering the ‘Science’ in Data Science - Part 1
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. Moreover, it encompasses such a broad area that if you ask ten people what it means you may be discovering ten completely different and sometimes contradictory answers.
We capture, collect, persist, stream, clean, transform, query, analyse and visualise data. That’s a lot of work indeed and that’s been the traditional way of dealing with data.
Throw in ‘big data’ into the mix and things and terminology confusion increases. 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?
What does discovering the ‘science of data’ mean?
I will attempt to answer that question in this article.
I would encourage anyone to pause a bit and think about how we can use science to explore hidden things in data.
My first stop in the process was asking Google the definition of science because who knows more stuff than Google itself!
Hence, here is what I got-
The intellectual and practical activity encompassing the systematic study of the structure and behaviour 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 behaviour of the physical, natural and data world through observation and experiment.
In fact, we are now discussing an activity that involves the systematic study of the structure and behaviour of ‘data’ through observation and experiment.
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