Data Science vs Big Data vs Data Analytics

By Shweta

Data is the new buzz word in today's world. Courses which comprise data, that is, data mining, data analytics, data science, big data are all selling in the market with big promises. These streams may sound similar, and are somewhere related but still there a different feature to each one of them. Let us see and understand the three major courses - data science, data analytics and big data.

Data Science: This is the study of data and is related to everything involved in the preparation of data, cleansing of data and analysis of data. Data science involves Maths, Statistics, Visualization, scientific methods and data engineering. It does develop working applications from the underlying pattern of complex systems.


Data science can be called an expertise to convert raw data into a useful form. A data scientist uses the data given and his/her own analytical abilities to interpret data and build mathematical models using the data.

Also read: What is Data Science? Scope and Career Opportunities

Data Science vs Big Data vs Data Analytics

Big Data: Big data analysis uses a large amount of data available to get insights of a given problem. When a data set is so large and complex that it cannot be dealt with traditional data processing applications, it is known as big data. Correct analysis of big data results in greater operational efficiency, cost reduction and reduced risk.

Data Analytics: Data analytics uses raw data to draw conclusions from it. It is the technique of getting automated insights into a given dataset. Data analytics can be described as a part of data science and it does find its applications in analyzing big data. The main focus of data analytics is inferencing some conclusion from the given data.

Data science, big data and data analytics - they all make use of principles of Mathematics and Statistics with some software. They have immense use in various industries and thus are expected to give a lot of jobs in immediate future.

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