As a data scientist, a lot of mistakes aren’t dramatic—they’re small habits that quietly mess up results over time. Here are some common ones:
Jumping straight to modeling without properly understanding the data or the business problem
Ignoring data cleaning, assuming the dataset is already “good enough”
Overfitting models just to get high accuracy instead of real-world performance
Not validating properly (e.g., skipping cross-validation or using the wrong split)
Data leakage(accidentally using future or target-related information in training)
Over-relying on metrics without interpreting what they actually mean in context
Poor feature selection, either adding too many irrelevant features or missing key ones
Not documenting work, making it hard to reproduce or explain results later
Communicating poorly, especially presenting results without clear insights or impact
Ignoring domain knowledge, when context could easily improve decisions
