Scenario Based Interview Questions For Data Science
- How would you address missing data in a dataset with numerous features?
- What strategies would you use for feature selection in a dataset with many features and limited samples?
- How would you handle class imbalance in a classification problem?
- What approach would you take to deal with missing values and irregular intervals in a time series dataset?
- How would you assess the performance of a machine learning model in a real-world scenario?
- What steps would you follow if your model performs well on training data but poorly on validation data?
- What considerations would you have when deploying a machine learning model to production?
- How would you build a recommendation system for a new platform with minimal user data?
- What methods would you use to address severe outliers in your data?
- How would you approach feature engineering for a model with complex variable interactions?
- How would you compare and select the best model for a classification problem?
- What features would you consider when developing a churn prediction model for a subscription service?
- How would you process a dataset with a mix of numerical, categorical, and text features?
- What techniques would you use to address overfitting in your model?
- How would you handle noisy data or irrelevant features in your analysis?
- What approach would you take for sentiment analysis of customer reviews?
- How would you ensure a model remains effective when dealing with an imbalanced target variable?
- What methods would you use to integrate data from multiple sources and formats?
- How would you address multicollinearity in your dataset?
- What design would you use for an A/B test to evaluate a new feature on a website?
- How would you tackle challenges when working with sensor data for predictive modeling?
- How would you manage categorical variables with many unique categories in your model?
- What strategies would you use for processing large-scale datasets that do not fit into memory?
- How would you validate a model’s performance when working with time-dependent data?
- What techniques would you use to explain complex model predictions to a non-technical audience?
- How would you design a dashboard to visualize key insights from your data?
- What would be your approach to feature scaling in datasets with varying ranges and units?
- How would you forecast future trends using temporal data?
- What methods would you use to handle sparse data in a recommendation system?
- How would you perform clustering on a dataset with mixed feature types?