What is difference between Data Science and Big Data?

Data Science is a branch of statistics that has evolved over time. Many people conflate machine learning with data science, however, this is not the case. Machine learning, on the other hand, is a subset of Data Science.

Big Data, on the other hand, is something that deals with a large amount of heterogeneous data from numerous sources that aren’t available in traditional database formats.

The following are the primary distinctions between Data Science and Big Data:


  • This is more focused on a scientific approach to data and data retrieval from a specific dataset.

  • System logs, live feeds, traffic/internet users, and other sources of information are used to generate data.

  • In order to construct a model to test the hypothesis and make business decisions, statistics and mathematics, as well as programming abilities, are heavily utilized.

  • Text-to-speech recognition, risk identification, chatbots, internet search, digital advertising, and other technologies are among them.


  • Big Data is aimed at massive amounts of data that can’t be handled properly by traditional data analytics approaches.

  • Analysis, data filtering, and preparation are all part of the process.

  • Businesses use it to keep track of their market presence in order to improve agility and get a competitive advantage over their rivals.

  • Financial services, health and sports, security and law enforcement, research and development, and other industries are also represented.