Could you describe how you would handle missing data in a dataset?

During my final year project, I worked on a dataset analyzing student performance across various subjects. I encountered missing data in some students' exam scores. To handle this, I first assessed the extent of the missing data and discovered it was minimal—only about 5% of the scores were missing. I decided to use mean imputation for the missing values, as it would allow me to retain the data without significantly skewing the results. After imputing the missing scores, I ran my analysis and compared the results to a version of the dataset without imputed values. This experience taught me the importance of justifying my method based on the context and being transparent about the choices I made in my analysis.

While volunteering with a local community organization, I helped gather data through surveys to understand residents' needs. Some surveys had missing responses, particularly in open-ended questions. To address this, I used a technique called 'last observation carried forward,' where I filled in missing responses with the most recent answer provided by the same respondent. This approach was useful as it allowed us to maintain the integrity of the data while acknowledging that some residents may have had similar responses. It was a great learning experience that emphasized the importance of adapting data handling techniques to the specific context and purpose of the analysis.

In my first job as a junior data analyst, I often dealt with client datasets that had missing values. One project involved sales data where several entries were incomplete. I analyzed the missing data patterns and found that it was primarily due to missing entries from specific months. I proposed to the team that we use a combination of deletion for those entries that had excessive missing data and interpolation for the months with minimal gaps. This approach allowed us to generate more accurate client reports without compromising data quality. This experience helped me appreciate the nuances of handling missing data in a business setting and the importance of collaborative discussions on the best strategies.