Budding Data Scientists thoughts…

Many of the budding data scientists are bombarded with programming-related questions in their job interview who according to the internet, focuses on various modeling techniques, ML algorithms, data analysis, and the list goes so on and so forth for years. This gives them a skeptical feeling of whether they are about to build careers in data science or into application development.

Well, then what should young data scientists focus on-understanding nuances of algorithms or faster application of them using tools? You might think of this as “an analytics vs technology” question. Well, my point agrees to disagree with this concept moderately.

Analytics evolved from a shy goose, a decade back. To an assertive elephant. With the advent of BigData, ML, AI the world of analytics has changed its perspective.

When these analytical solutions start interacting with technological ecosystems, that’s when programming comes into the picture. When the inputs are given to your optimization model changes in realtime and the model reruns that’s exactly when programming comes into the picture.

Agreed analytics and technology both go hand in hand but to what extent? The extent to which we focus on the preciseness of building our model?

To what extent should budding data scientists as well focus on programming, is hardly explained anywhere. They say data scientist is a field that belongs not just to computer science graduates but also opens up options for statisticians, economists so on and so forth. Here comes the question of how skillful are you as a programmer? what would these people know about data structures or rather coming to the conclusion how well should they train themselves into programming to crack the programming rounds of companies? What and how much is expected from them in this scenario? Is this ever explained?

Do comment your views and reach out to me!

Happy reading!!!