Many candidates make avoidable mistakes in Python-based data science interviews. This guide highlights common pitfalls, such as inefficient loops, incorrect use of NumPy arrays, mishandling missing data, and misunderstandings of statistical functions. Learn how to write optimized, scalable code while avoiding common errors in data wrangling and feature engineering. We also cover best practices for debugging and structuring your code professionally. By recognizing these mistakes in advance, you can increase your chances of passing technical rounds with confidence.