The world today is incomplete without data. Humongous amounts of data are generated by users every day. If this data can be somehow analyzed and interpreted to capture what the user wants and make innovations accordingly, we could bring in a revolutionary system where businesses can provide state-of-the-art solutions to the problems faced by a common man and that too at low costs. Better still, this system can improvise and improve itself to be more innovative by the day. This revolution is data science and involves data analytics, machine learning and much more.
The words data science and machine learning are often used in conjunction, however, if you are planning to build a career in one of these, it is important to know the differences between machine learning and data science.
Before doing so, we need to understand a few important terms that are related but different.
Data analytics is a subset of data science that deals with gathering and analyzing data and then applying various techniques to convert the same into meaningful information usable for decision-making and enhancing productivity for a business.
Data science and data analytics are among the top career avenues in the 21st century. In the present data-savvy world, the possibilities pertaining to data analytics are immense. With the right knowledge and skills, you can grab lucrative work opportunities.
Our little planet is now becoming a Digital planet and by 2020 we will have 40 times more bytes than there are stars in the universe. Over 90 percent of the data sitting and floating in all possible devices and systems in the world today was simply generated in the last two years alone. These humongous volumes of data – now called Big Data – can mean a lot to businesses and can help gain insights and trends about their users and user behavior. The massive volume of data in both structured and unstructured formats is difficult to process through traditional database modeling and tools. Hence there is a need to use scientific methods, algorithms, and tools to analyze and to make sense out of Big Data and the need for Data Science and Data Analytics.