Sample interview questions: How do you handle missing or incomplete data during analysis?
Sample answer:
1. Data Imputation:
– Simple Imputation: Replace missing values with a constant (most commonly used), mean, median, or mode.
– Multiple Imputation: Create multiple plausible datasets by imputing missing values multiple times.
2. Listwise Deletion:
– Drop cases with missing values.
3. Machine Learning Algorithms:
– Use algorithms like K-Nearest Neighbors (KNN), Decision Trees, or Random Forests to predict missing values.
4. Model-Based Imputation:
– Estimate missing values using statistical models.
5. Robust Statistical Methods:
– Employ methods less sensitive to missing data, such as logistic regression or robust regression.
6. Data Augmentation:
– Generate synthetic data to augment the existing dataset.
7. Sensitivity Analysis:
– Assess the impact of missing data on the analysis results.
8. Data Preprocessing:
– Addr… Read full answer
Source: https://hireabo.com/job/1_0_49/Marketing%20Data%20Analyst