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Techniques used in Data Scientists

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Data science is surely one of the most easily found words or phrases in any technology related articles nowadays as every other person talks about big data and its impact on the businesses. Of course, big data is revolutionary and data science is the fuel for this revolution, but the figure that is often neglected in all these discussions is the figure of the data scientist. It is the scientist who makes it possible to represent the big data in the way you see through graphs and charts. Hence, it is important to invest the methods to do so.

Many avenues, few approaches

Data science can be used to do many things and it may well be the channelizing force for many businesses. But, there are few methods that circulate among data scientists who decide what should be the best method for this process and apply accordingly. Data science is more or less a known phrase, but no one knows about it because it is a matter of specialists which limits the knowledge.Techniques used in Data Scientists

Opening up of knowledge

The most important thing to remember in this context is that internet has made knowledge free and easily accessible. Hence, if you have the zeal and the theoretical background, you too can become a data scientist by learning about the tricks of trade. Not everything comes up with a simple Google search, but you can surely read articles about various regression methods and time series which you can find in good blogs and learn about them.

It is however important to understand that merely learning the method is not enough. Learning the application and implementing it to get test results and then compare it with other methods is the way to start your data science quest.

Deep data science

There are certain techniques that corroborate to a field called the deep data science. This is a field that hardly has any overlap with computer science, machine learning and many such fields. This is a special branch and hence, the techniques such as indexation, Attribution modelling and recommendation engine are to be studied only if you want to work in these fields in the future. While there are classical methods that theoretically come under this esoteric field, people recognize them through other popular variants.

Powering automation from core

Such esoteric techniques are often used for automation development and they affect the process from the core. Among the various techniques, some of them are championed by the tech giants as they use them more frequently than others. Often, people learn these techniques by heart and try to become a data scientist. However, if you aspire to be one, it is best to learn all these methods including the deep data science ones so that you can counter an unknown situation.

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