What Is the Difference Between Big Data and Data Science?

Answer 1

The main difference between Big Data and Data Science is in their focus and purpose.

Big Data refers to large volumes of data — structured, unstructured, or semi-structured — that are too complex to be handled by traditional data tools. It focuses on storing, managing, and processing massive datasets using technologies like Hadoop, Spark, and NoSQL.

Data Science, on the other hand, focuses on analyzing and interpreting that data to find useful patterns, trends, and insights. It uses techniques from statistics, machine learning, and AI to make data-driven decisions.

In simple terms, Big Data deals with data handling, while Data Science deals with data analysis.

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Answer 2

Big Data refers to large volumes of structured and unstructured data that are too complex to be processed by traditional data management tools. It focuses on collecting, storing, and managing huge datasets.

Data Science, on the other hand, involves analyzing and interpreting complex data to extract meaningful insights and support decision-making. It combines techniques from statistics, machine learning, and data analysis, often working with big data to derive actionable knowledge.

In short, Big Data is about the data itself, while Data Science is about analyzing and making sense of that data.

Answer 3

Big Data refers to the humongous amount of data which is tracked by businesses on a day to day basis. This data is majorly unstructured; and is generated by routing business processes running on a daily basis. The nature of Big Data in organizations is defined by 3Vs – Volume, Velocity & Variety. ‘Volume’ refers to the large quantum of data in terabytes which may be transactional data, customer data etc. ‘Velocity’ refers to the rate at which the data is generated which could be either scheduled batch process, or real-time data collection. Finally, ‘variety’ denotes the variation in the data formats and structures.

Data science, on the other hand, is a more specialized field which involves using mathematical & statistical modelling techniques on underlying data to devise patterns in data; and find actionable solutions to real-world problems.

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