- Data Science: collecting, cleaning and analyzing data to gain insights.
- Machine Learning: building models that learn to recognize patterns on their own and can make predictions.
Together, they form the basis for not only understanding data, but actively making decisions and actions from it.
Why is DSML important?
- Better decisions: organizations can see what’s going on and look ahead more quickly.
- Automation: processes become smarter and less dependent on manual work.
- Innovation: with DSML, applications such as chatbots, fraud detection and personalized recommendations are emerging.
Examples of DSML in practice
- A supermarket that predicts which products will become popular.
- A bank that automatically recognizes suspicious transactions.
- A factory that can predict maintenance via sensor data.
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