Notebook Interview Preparation Tips

In this final segment, we shift from execution to preparation—learning how to practice effectively for notebook-style ML interviews. The focus is on developing real-world fluency, structured habits, and clear communication.

• We should practice with real, messy datasets from platforms like Kaggle or UCI to simulate the open-ended nature of actual interview problems.
• We’re encouraged to build a reusable workflow template—a consistent structure (e.g., data loading, EDA, feature engineering, modeling, evaluation)—to streamline our process and reduce cognitive load.
• We must get used to explaining our decisions out loud, as live interviews often involve narrating our thought process clearly and confidently.
• Throughout, we should document our work as we go, making it easier for interviewers to follow our logic and showing that we value clarity.
• Most importantly, we’re reminded to think beyond accuracy—considering the user impact, business value, and maintainability of our solution.
• A bonus tip: practice talking while coding to improve our ability to communicate effectively under real-time conditions. This small habit can make a big difference in live interviews.

If you want to learn even more from Yayun:

• Book 1 on 1 career guidance with her here: https://calendly.com/your-friend-yayun/1on1
• Follow her on LinkedIn: https://www.linkedin.com/in/yayunjin/