Deep Learning vs Traditional Data Science: What’s the Difference?
The choice of data analysis method depends on the complexity of the problem and the nature of the data. Traditional models, such as linear regression and decision trees, are simple, interpretable, and effective for smaller datasets. In contrast, neural networks are powerful tools for processing large, unstructured data like images and text, making them ideal for complex problems—but they require more data and computing power. Nevertheless, traditional methods continue to be valuable, especially when data is limited and understanding the model’s decisions is crucial.