Data Science & the Myths surrounding it...

Data Science is considered to be one of the fastest growing fields across all the domains (corporate, social & government), geographies, and sectors (finance, agriculture, retail, security,...). The uses of it are already identified in many important areas like for better customer experience, risk & control, agricultural products research, so on and so forth. The new applications and solutions are being explored and lot many unprecedented results are coming out on a daily basis. Given this, of course there is so much of curiosity, anxiety, concerns and myths around the subject. I thought of sharing some of those myths:

-         People only with Engineering and Coding experience can be in this field: just like any area, people with good application and analytical skills fit well here. The Statistics (and many other disciplines) plays a major role in most of these applications; not only Technology and so to say Coding skills.

-         It is primarily Graphics/ Visualisation: the first impression to many about it is to simply have better graphical representation of data; which is just a small portion of numerous applications of this area; and not the only one. The real art though is to spot trends in huge data-sets, which will otherwise be very-difficult to do in conventional ways of analyzing the data; and to make it more interactive and visual friendly.

-         It is same as Business Intelligence (BI): Better business intelligence will be one of many benefits coming out of data science, due to more powerful predictive analytics and better patterns/ trends being captured through its tools. One of the other views is that BI stops at interpreting the past/ current data, whereas Data Science adds predictive analytics to to make future predictions.

-         It is often confused with Big Data: Many of them say that Big Data Analytics can be called Data Science, but Data Science cannot be called Big Data Analytics. Data Science is actually an approach to tackle Big Data, wherein the data is gathered from multi-streams, extracted in the desirable form, and then goes through machine learning or any other similar tools to understand the data and to get to a meaningful use from a current or future perspective.

-         Data Science always give accurate results: As I said that its usage is vast; and most of it is still being explored and discovered; but it also has limitations. For example, past data might not be the absolute representation of future trends all the times. The correlations in the data sets, right sampling (rather data collection), picking the right time-frames and many other factors make a huge difference to the quality and suitability of these solutions.

This list can go on; but these are the ones I come across very often; and seem quite opinionated views to me. I will suggest all to know more about the field, specially the People aspiring to get into the field, or the Ones not venturing into the field for these opinions or the Ones in the corporate world who might/might-not wish to apply data science tools in their areas...

(The views expressed above are purely my personal views).