As the world entered the era of big data, the need for its storage also grew. It was the main challenge and concern for the enterprise industries until 2010. The main focus was on building framework and solutions to store data. Now when Hadoop and other frameworks have successfully solved the problem of storage, the focus has shifted to the processing of this data. Traditionally, the data that we had was mostly structured and small in size, which
could be analyzed by using the simple tools. Unlike data in the traditional systems which was mostly structured, today most of the data is unstructured or semi-structured. The data trends show that by 2020, more than 80 % of the data will be unstructured. This data is generated from different sources like financial logs, text files, multimedia forms, sensors, and instruments. Simple tools are not capable of processing this huge volume and variety of data.This is why we need more complex and advanced analytical tools and algorithms for processing, analyzing and drawing meaningful insights out of it.so it can be said that Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. Data Scientist not only does the exploratory analysis to discover insights from it but also uses various advanced machine learning
algorithms to identify the occurrence of a particular event in the future. A Data Scientist will look at the data from many angles, sometimes angles not known earlier.A common mistake made in Data Science projects is rushing into data collection and analysis, without understanding the requirements or even framing the business problem properly. Therefore, it is very important to follow all the phases throughout the lifecycle of Data Science to ensure
the smooth functioning of the project.Data Science is one of the most dynamic industries today. The key to success in this field is to recognize and respect that it is an ever-morphing one, and you need to be up to speed at all times.