I’m new to the field and would like to understand the relationship between machine learning and data science. What are the key differences and overlaps between these two disciplines? How do they complement each other in practical applications?
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Hello, I will give you the difference in terms of the specialists involved
Our data scientists write production code, but there’s this guy (not data scientist) who’s especially good at spark. If situation allows, he writes the production code. He spends 0% of time on research.
I feel like having MLE who turns models into production helps free up time for data scientists who can focus on creating better models.
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From a business standpoint, data science transforms raw data into actionable insights, while machine learning automates tasks and facilitates data-driven decision-making. Together, these technologies offer a competitive advantage by revealing hidden patterns and forecasting future trends.
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In practical scenarios, these disciplines frequently intertwine. For instance, a data scientist might employ machine learning techniques to construct a sales forecasting model. This involves data cleansing, model training, and interpreting outcomes to guide strategic business choices. Machine learning facilitates automation and valuable insights, while data science ensures a comprehensive approach to the project.