The difference between data science, data analytics, and data engineering

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The difference between data science, data analytics, and data engineering

In: 7

As an example for weather:

Data engineering: Collect weather data from various sources, put it in a consistent format it, output it.

Data analytics: Use weather data to gain insight and answer questions about the past. “What % of days rained in the last year in London”

Data science: Use weather data and advanced techniques to predict the weather in the future.

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More about it professionally:
Data engineering is about how to manage a lot of data, and store it efficiently to be used.
Data analytics is about getting insight from past data.
Data science is about using data for machine learning and future prediction.

Although the last 2 can blend together, and vary across companies.