‘Myths’ vs ‘Reality’ on data science


From Data scientists to HR professionals and engineers, anybody regardless of their educational background can be good at data science, a scientific method to extract knowledge and insights from a huge amount of structured as well as unstructured data. One must follow proper resources under the right guidance to acquire knowledge of data science. Today, in the modern world, it is a known fact that the majority of the organizations have a huge amount of data in structured as well as unstructured form. 

The internet generates a very huge amount of data every data and it is said to be an Exabyte. Data scientists, with the arrival of the Internet of Things, have more value in organizations to generate actionable insights out of almost 1 billion bytes of data. Data science, is a field of great potentials, now accepted by not only organizations but also some countries have decided to make use of data science for cost-saving. 

A report of McKinsey & Company showed that the US healthcare systems with the help of Big Data could decrease healthcare spending for USD 300 billion to USD 450 billion in the US healthcare expense.

Data science has immense advantages that could be benefitted by anybody. Data science provides an in-demand and bright future for professions. Well paid careers are waiting for those professions with mastery in data science. 

Despite being a job sector with great potentials, myths surround data science. The abundance of content and blogs is one of the reasons for the myths regarding data science. Myths confuse people and are often misled, thinking that only geeks can work in data sciences. 

Check out some of the MYTHS about Data Science:

  • Only engineers can do data science

According to KM Saqiful Alam, Analytics and Machine Learning expert said that anybody could be a master in data science, under good guidance. There is no such rule that only people with a particular academic qualification can learn or work in data science. Practical expertise is essential, in a large and dynamic field such as data science. Therefore, what matters is practical expertise. 

  • Data scientists are not programmers and coders

Programming and coding are essentials for a data scientist. But that never means that one must be an expert in programming to be a data scientist. Typically, people begin by learning to code with R or Python at first. Websites such as Data Camp are dedicated to educating people about analytics and machine-learning relevant coding.

  • Deep knowledge of statistics is a must for data science

One indeed needs to have an understanding of statistics, for data science. But there is no need for a formal course or graduate degree to become a data scientist. Anybody, with the help of an ample number of e-books, journals, and various other resources, acquires the basics of relevant statistics. 

Anybody wanting to become an analyst should practice simple coding steps and try to understand the importance of statistics, breaking all myths about data science.


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