Data Science and Data Analytics can be considered as different sides of the same coin. Often terms, these two terms are used interchangeably but they are separate fields that have interconnected functions. The scope makes up the major difference between the two.
The interdisciplinary field of Data Science is employed to mine enormous sets of data. On the other hand, Data Analytics is only a part of the larger process dedicated to deriving actionable insights for existing queries. The notable difference is that Data Science is not for specific queries and instead generates insights after parsing through huge data sets, whereas Data Analytics is more suitable for answers that are more focused-based on existing data. An easy way to explain this is that Data Science generated insights focus on which questions need to be asked and Data Analytics is all about discovering answers to existing questions.
Despite their difference, it is important to think of them as working in unison as part of a bigger picture to better understand and analyze data.
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To help you understand the differences further, check out this blog by Intellipaat:
You can also check out this YouTube video explaining the difference -
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