Data Science vs. Machine Learning: Key Differences 

Data Science 

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It deals with understanding and finding hidden patterns or useful insights from the data, which helps to make smarter business decisions. 

It is a broad term that includes various steps to create a model for a given problem and deploy the model. 

A data scientist needs to have skills in using big data tools like Hadoop, Hive, and Pig, statistics, and programming in Python, R, or Scala. 

Data scientists spend lots of time in handling the data, cleansing the data, and understanding its patterns. 

Machine Learning 

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It is a subfield of data science that enables the machine to learn from past data and experiences automatically. 

It is used in the data modeling step of data science as a complete process. 

Machine Learning Engineer needs to have skills such as computer science fundamentals, programming skills in Python or R, statistics and probability concepts, etc. 

ML engineers spend a lot of time managing the complexities that occur during the implementation of algorithms and the mathematical concepts behind them. 

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