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The core concepts that a data scientist needs to know about are correlation and causation. Both concepts are related, but their implications are rather different in statistical studies, and confusing them often leads to wrong conclusions. In this blog, we will not only explore the nuances between correlation and causation but also discuss their importance in the field of data science, and how mastering these concepts can be beneficial for your career with the help of a data science course in Pune.

Correlation refers to a measure that describes the strength of relationship between two variables. When two variables are said to be correlated, it simply means that a change in one variable will definitely bring about a change in the other. The Pearson correlation coefficient measures such relationships, and it falls within the range starting from -1 to +1. The values taking shape imply the following: - +1 indicates perfect positive correlation-as one increases, the other increases.

Again, one has to remind oneself that correlation does not necessarily mean causation. Similarly, just because two variables are related or correlated, it doesn't mean one variable causes changes in the other variable.

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