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The data science industry is in a period of promising translational research. New developments in the areas of reinforcement learning, deep neural network architectures, and probabilistic modeling are opening the door for new applications in the data science industry. Being able to translate academic results into new applications is an essential skill in modern data

According to an International Data Corporation (IDC) Digital Universe Study, it is estimated that about 1.7 MB of new information will be created every second for every human being on the planet by 2020. This calls for investments in right tools, technologies, infrastructure, and talent that can facilitate the translation of this data into insights.

In today’s technologically powered world, data science industry has rapidly graduated from simple data warehousing, data mining solutions to business intelligence, machine intelligence/learning to finally deep learning solutions. By 2020, it is estimated that data science industry will become the biggest industry, producing more information, knowledge, solutions and wealth than all the other industries combined.

The current state of the data science industry leaves the unlimited potential for growth, but there is a lack of data scientists having a standard level of expertise demanded in this field across organizations. The need is outperforming the speed of the data science industry as many companies are investing a large amount of resources