BIG DATA VS DATA SCIENCE: KNOWING THE DIFFERENCE BY UNDOING THE KNOTS Posted on : Mar 05 - 2021

Big data and data science technologies have data as their core content and perform various actions

Ever wondered whether to choose big data or data science? If you are into data and a tech geek, you might have come under such a dilemma at least once. In the digital world we live in, data is increasingly becoming the most valuable asset for organizations. It won’t be a surprise if it crosses the price of gold one day. But to explore every bit of data, we need more than just the basics. Big data and data science technologies have data as their core content and perform various actions.

Even though big data and data science are two different technologies, they are interlinked with each other on the grounds of data. Both technologies play a big role in digital evolution. More and more companies across various domains are adopting big data and data science to enhance the routine. Since data is rapidly transforming the way we live and communicate, big data and data science application help collect, sort and study data to improve organizations’ performance. Data science is an extension of statistics that deals with large datasets with the help of computer science technologies. On the other hand, big data engages with the vast collection of heterogeneous data from different sources. In this article, we’ll undo every knot and reveal the difference between data science and big data.

Definition

Big data represents a large set of data, both structured and unstructured, that inundates business on a day-to-day basis. The data is very large in size that none of the traditional data management tools can store it or process it efficiently. But the massive amount of data can be used to address business problems that humans find difficult to tackle with simple calculations.

Data science is a domain that deals with vast volumes of data to derive meaningful information and make business decisions. Data science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from raw data. The term ‘data science’ was coined in 2008 when companies realized the need for data professionals who are skilled in organizing and analyzing massive amounts of data.

Concept

Big data holds diverse data types generated from multiple data sources. Henceforth, big data approach can’t be easily achieved using the traditional data analysis method. Instead, unstructured data requires specialized data modelling techniques, tools and systems to extract insights and information as needed by organizations.

Data science is a specialized field filled with intelligent data capture techniques, data cleansing, mining and programming to prepare and align big data for intelligent analysis to extract insights and information. Data science is comparatively a challenging area due to the complexities involved in combining and applying the different methods, algorithms and complex programming techniques to perform intelligent analysis in large volumes of data. View More