Data Science Interview Question: Handling Missing Data in a Dataset

When dealing with missing data in a dataset, there are several methods that can be utilized depending on the nature of the data and the specific situation. Some common methods for handling missing data include:

1. Deleting rows or columns: This method involves removing rows or columns that contain missing values. It is typically used when the amount of missing data is minimal and removing it will not significantly impact the overall analysis.
2. Imputation: Imputation involves replacing missing values with substituted values. This can be done using statistical measures such as mean, median, or mode of the available data. Imputation is a common method for handling missing data when removing rows or columns is not feasible.
3. Advanced techniques: Advanced techniques such as predictive modeling or machine learning algorithms can also be used to predict and fill in missing values based on the existing data. These techniques are more complex but can be effective in handling missing data in a sophisticated manner.

Each method has its own strengths and limitations, and the choice of method will depend on factors such as the amount of missing data, the type of data, and the goals of the analysis. It is important for data scientists to carefully consider the implications of each method and choose the most appropriate approach based on the specific context of the dataset.