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All too often companies have only the vaguest idea about what kind of data they’re holding; because such data is very often hidden deeply away in a variety of databases and fragmented across different departments. We identify this data and bring it to light, making it visible, cohesive, comparable and easy to understand so that it really does support YOU in making the right decisions. And if need be, we can also identify any lacking data and define a concept to fill in the gap.

Why a Data Scientist should master both ‘concept’ and ‘context’

Posted by on Feb 19, 2019 in Insights, JSMNews, Thoughts | No Comments

Albert Einstein once said “If you cannot explain it simply, you do not understand it well enough”. My belief in this has only strengthened ever since I (along with Sandhya and Bala Kartheek) taught the first ever industry elective course named “Data Mining using Machine Learning ” by Department of Statistics, Osmania University. Thanks to this opportunity given to us by the Department of Statistics.

It was a fascinating experience to guide a batch of 76 students (divided in groups) over 3 months. It culminated with a project for each of the groups. There were a total of 12 projects, where the students received an opportunity to work hands-on on an end-to-end Data Science project.

I see a world of difference between explain concepts to a client and explaining the same to students. I say this, courtesy years of client servicing experience. Academic experience helps you bring in the rigour to your work, since it all about the concept/ algorithm, mathematics and statistical theories, which are the pillars on which Machine Learning / Data Analytics stands. It is this depth of understanding that provides you with the perspective of applying it in various scenarios.

As practitioners, we always strive to apply Data Science to solve business problems. All interactions, in such a case, are usually about answering the client’s queries from a business perspective. It is all about how the model is working rather than the model, per se. In most cases, it does not matter to the client what algorithm / technique has been used, you will rarely get an opportunity to explain concepts to a client. What client interaction teaches you is the width of application of concepts. Having a grip on both the ‘width of application’ and the ‘depth of concept’ gives a Data Science practitioner a 360-degree view.

Have you heard of ‘CCCF’ – conceptual clarity and contextual familiarity? Here, ‘width of application’ provides you with contextual familiarity and ‘depth of concept’ gives you the conceptual clarity. I am working towards making ‘teaching’ an integral part of my life. We now look forward to the 4th semester course on ‘Text Mining’. Hoping that someday, opportunity knocks on a ‘Deep Learning’ course as well.

Venugopala Rao Manneni

A doctor in statistics from Osmania University. I have been working in the fields of data analysis and research for the last 14 years. My expertise is in data mining and machine learning – in these fields I’ve also published papers. I love to play cricket and badminton.

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