DailyPost 724


Data is the new oil is an adage, we have become very familiar with. Data and oil are radically different in the application of mind to make it market ready. Oil is more of natural resource and translating it into cash is all too well known. It shall deliver the way it is intended to. Data broadly is an intangible commodity and making a value add business out of it, is not very easy. But today the standard models cannot be the differentiator. Given the centrality of data in our existence, data science has emerged as one of most lucrative careers of the day.

Data scientists thus are presumed to be of a different breed. And the aura keeps on increasing. One of the top medical treatment / solution provider in the US is a data scientist. Data scientist has a finger in every pie. But still the reality is that data scientists are to come of age and deliver the magic expected out of them. Data science is in it’s infancy and so are the data practitioners. It will still be long when it is effectively used as the big IT giants do. Leaving the organised players aside, what are the mistakes one commits while wanting to ace data science?

The first mistake is the pace. Try to learn everything in haste. Basic and advanced topics are learned at the same pace without being proficient in the former. Emanating out of the first mistake is the second; inconsistent learning. Complexity has to be accepted and it can be learned only the hard way. Mastery is a game of well planned deliberate learning & practice.

The next issue, the third, is transcending learning. The more learning mode should cross over into applications to be of value. Data science has value only because of application. The algorithm has to have real world fruits. Fourthly, as techies are, they turn out to be myopic. The focus is on data rather than the problem it aims to solve. Data in itself solely cannot be a solution. Data science expertise and knowledge has to marry. That gives imagination the wings and also has the capability to deliver. And lastly, nothing much can be achieved without the background knowledge of certain level of mathematics and statistics. Like any new discipline, it is slowly finding it’s own trajectory.


Sanjay Sahay

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