Becoming a Data Science Professional is perhaps as big a rocket science as the science involved in it. With the amount of Startups now emerging from every nook and corner of the globe, it is astonishing for the people to notice how typical algorithms and the art of predictive analysis could influence so many lives. Back in 2015 alone, Gartner predicting that there were approximately 4.4 million jobs worldwide pertaining to Big Data. Another stat mentioned, that one data science job would create three non – I.T jobs. So do we take our baby steps towards becoming a Data Science Professional, on our own?

Data Science Certifications are being provided by a gamut of institutions on a scale but not a budget that could be opted for by people from the developing countries. Majority of these certifications require approximately 4 years of life. If not that, then they would need a month or two’s worth of devotion but the validity of such graduates needs to be tested in comparison with industry veterans. Hence while shortlisting a course, go for those which are relatively feasible in tuition fees and add value to your resume.

Eat, love and Pray your Big Data:

Let alone entering the industry, it is highly important for a person to be capable of loving data. It is this relationship between A Data Science Professional and Big Data, that gives you the license to charge the stream of knowledge that is available on the topic outside.Even before getting a hang of the platforms which are used in Data Science such as Hadoop, NoSQL, an individual should have the loving quality towards the functions and best practices of Big Data.

Getting to know the Data Science Professional within you:

to be honest, there isn’t a clear enough definition of a Data Science professional. The descriptions would vary with as many people as asked. For some it is the person who can easily juggle the technicalities of Big Data. For others, it may be someone who can perform A/B testing.A good data scientist is the one who knows what is available “outside the box” and who he needs to connect with, hire, or the technologies he needs to deploy to get the job done, one who can link business objectives with data marts, and who can simply connect the dots from business gains to human behaviors and from data generation to dollars spent.