What is difference between Data Science and Big Data?

While these terms are loosely used interchangeably, they refer to very distinct concepts in the sphere of data analysis.

Big Data

Focus: The core aspect of big data is related to handling, storing, and processing huge data volumes.

Characteristics: Characterized by the 3Vs, namely, Volume, Velocity, and Variety.

Tools: Hadoop, Spark, NoSQL databases.

Goal: The goal is to deal with technical issues that have been created by large volumes of data processing and storage.

Data Science

Focus: A superset of tasks involving extracting insights and knowledge from data, ignoring the size.

Techniques: It has various statistical, mathematical, and machine learning techniques at its core.

Tools: Python, R, SQL, TensorFlow, PyTorch Goal: Extract meaningful information, patterns, and predictions from data to inform decision-making.

In essence, Big Data deals with how to deal with large datasets. Data Science deals with what you can do with that data.

Relationship: Though Big Data provides the base for data science, it is the latter which applies techniques to uncover useful insights from these vast datasets.

Example: A company might use Big Data to hold and process terabytes of transaction data for its customers. The data scientist analyzes this information for trends, prediction of future behavior, and personalization of their marketing efforts.

Summary

Big Data is basically a subset of the former, that is, Data Science. Big Data goes towards the technical aspects dealing with large data sets, while Data Science is a wider discipline of knowledge discovery or extraction from data.