The data science community has come a long way and has matured a lot in the last 5 years. Earlier, the IT sector used to place a lot of emphasis on technologies like Java which includes Spring and Hibernate for writing and testing code. Following the advent of machine learning and data analytics, the focus shifted to technologies such as R, Python, and SAS. These technologies are being constantly deployed for algorithms in machine learning, deep learning, artificial intelligence and much more cutting-edge discoveries that have taken the world and visionaries by surprise.
In modern times, the field of data science is evolving at a very fast pace. Hence, it is important for businesses to embrace the same before getting left behind at a distance that will just keep on increasing with the passage of time.
Do you know how much data is created in the world every day? Research by Forbes indicates that there are 2.5 quintillion bytes of data created each day. Over the last two years alone, 90 percent of the data in the world is user-generated and most of it is floating on the internet or on the cloud. To process and derive actionable insights from such humongous volumes of data to help in business decision making, it is imperative to use scientific methods, complex algorithms, and data analytics. The two important and powerful data modeling tools used by data scientists and statisticians are R and MATLAB. These two tools can process both structured and unstructured data.