But wouldn’t it be better if they learned from the past to stop making the same mistakes again and again.
Would that make sense?And that is data science for you.
Most of the time people would argue about all sorts of questions like:Is Data Science just creating models?Would you call an excel pivot table as Data Science?What about ETL.
Is that Data Science?What about data visualizations for Web frameworks/portals?Hypothesis Testing.
Is that still Data Science?And my answer is really simple:Any valuable observation that could be derived from data that is not directly visible by watching just a few rows is data science.
You could use any tool, any model or any visualization interface.
As long it doesn’t rank the choices for the business it won’t be of value.
Alternatively, you could use any tool/model/visualization as long as it generates value from data it is data science.
So Is data Science dying or Is it a fad?I am highly optimistic about data science as learning from the past is becoming really important for companies to stay competitive.
In my view, there would always be a shortage of people who could wrestle with data and get their hands dirty to find out valuable insights.
And the game would be: Who uses their data better?Companies like Google, Amazon are so big based on how they deal with their data.
Right now Bing is far behind Google in the search advertising business.
Why is that?Because Google has used its data in a better way.
Although right now two Ph.
students won’t be able to beat Google at its game even if they create the best algorithm for search, a company like Bing would always look to find this best algorithm and would always look to find talented people who can put data to good use.
ConclusionSo, Why is Data Science Underestimated?.And where does this pessimism stem from?The main reason people underestimate the importance of data Science is due to its non-quantifiable nature in many cases.
Being a knowledge discovery process, it is highly intermittent and you might have to wait to get even a single answer.
This leads some people to tag it as a research problem and not worthy of all the appreciation it is getting.
But as I said it is all about “The Choice”.
And if you don’t make it then your competitor would.
If you want to learn more about Data Science, I would like to call out this excellent course by Andrew Ng.
This was the one that got me started.
Do check it out.
Thanks for the read.
I am going to be writing more beginner friendly posts in the future too.
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As always, I welcome feedback and constructive criticism and can be reached on Twitter @mlwhiz.