The Role of a Data scientist

A Data Scientist today is in much demand. The kind of skill set and abilities required are not very different from what say a computer engineer would do- coding, programming, analyzing etc. However, a major winner is being ruthlessly analytical and data driven.

Learning and researching about the new Machine Learning models “out there in the market” makes the work of a Data Scientist different from the traditional software developer. One needs to understand the problems of the clients, research on which model/s to be employed, research, and design and tweak Machine Learning models.

Today a Data Scientist works in the realm at what is called the “cutting edge of technology”. These are exciting times not just for AI but also for those interested in Natural Language Processing (NLP). Time is spent mostly in researching, analyzing and reanalyzing the results. It may sound glamorous to say that one is a Data Scientist, but the hard work and constant drive when it comes with working with data in real-time, can be both exciting & exhilarating, but lave you drained, and wanting to do more….

The main programming language used is Python- data scientists need to be strong in this one language besides R. It is used for NLP, server-side scripting, training AI neural networks, web frameworks, data visualization, and a host of other stuff. Google has a whole set of python libraries, including Tensor flow that is particularly used for deep learning.

AI helps solve specific problems – you need to sit down with the client and really know what the problem is all about and what exactly is the output being looked at. A mind with a research bent, focus, great analytic skills coupled with good software engineering skills which can help the data function correctly specially in production, makes for great Data Scientist skills.

IBM has predicted that the demand for data scientists will grow 28% by 2020. Further, the study also states that “data science and analytics” jobs remain open for an average of 45 days, which is five days more than the market average. The demand is high for data scientists, but unfortunately, the skill sets are still raw out there.

Today, AI is a competitive area and the hype is high. But until you think you have the passion for it – a research bent of mind, an analytical mind, focused, ready to sit long hours over tweaking ML models- only then should one take the jump into this exciting and growing field of AI….