Covariance in Data Science measures how two variables change together, helping analysts understand relationships and patterns within datasets. It is a key concept in statistics and machine learning that indicates whether variables move in the same or opposite directions. The importance of covariance in data science lies in its role in feature selection, dimensionality reduction, and correlation analysis, forming the foundation for advanced techniques like Principal Component Analysis and predictive modeling.