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:
DATA SCIENCE
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This is more focused on a scientific approach to data and data retrieval from a specific dataset.
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System logs, live feeds, traffic/internet users, and other sources of information are used to generate data.
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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.
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Text-to-speech recognition, risk identification, chatbots, internet search, digital advertising, and other technologies are among them.
BIG DATA
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Big Data is aimed at massive amounts of data that can’t be handled properly by traditional data analytics approaches.
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Analysis, data filtering, and preparation are all part of the process.
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Businesses use it to keep track of their market presence in order to improve agility and get a competitive advantage over their rivals.
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Financial services, health and sports, security and law enforcement, research and development, and other industries are also represented.