The field of data science and machine learning is emerging as one of the rapidly-growing and most sought-after field today. It turns out that data is the most valuable asset for every organization across industries. Companies have started investing into resources and technologies to capitalize the business value hidden inside the data. Hence the demand of the data scientists has been increased in the market substantially.
While the people are seeking a data scientist role by developing their skill to capture market space, organizations are confused about the required skill set. This is because the field is new for the top management and they are just trapped by the hype created around. Most of them are naturally thinking of hiring a statistician having a PhD in statistics, which is not required at all.
As per my experience, this is true only if the task in hand is to do research in advanced statistical models and algorithms. With the abundant supply of the off-the-shelf modeling tools and technologies in the market, 99% of the times organizations just require a well-equipped resource who is skilled in the state-of-the-art tools available with a good understanding of the statistical models and techniques. All the models and algorithms are predefined by the statisticians and numerical scientist and available in every data science toolkit out there in the market whether it’s Python, R, Octave or Matlab. At the end of the day it just comes down to the understanding that when to apply which tool on what data to obtain the business value for any organization.