How would you approach analyzing a dataset that contains missing values?

In my final year project, I worked on analyzing survey data for a research study. During the data cleaning phase, I discovered that around 15% of responses were missing for certain questions. Instead of discarding these responses, I decided to use imputation techniques. I filled missing values using the mean of the responses for numerical data and the mode for categorical data. This allowed me to maintain the integrity of the dataset while still ensuring that my analysis was robust. Ultimately, this enabled me to generate more accurate insights and present reliable recommendations in my project report.

While volunteering for a local nonprofit organization, I assisted in a project that involved collecting data about community needs. During data entry, I noticed several missing entries in crucial demographic information. To address this, I suggested conducting follow-up calls to gather the missing data instead of discarding the incomplete entries. This not only increased our dataset's completeness but also enhanced our understanding of the community's needs. The final analysis helped the organization better allocate resources and plan future initiatives, demonstrating the significance of addressing missing values effectively.

In my first role as a data analyst at a startup, I frequently encountered datasets with missing values. One project involved customer feedback data, where about 20% of entries were missing ratings. I initially experimented with different imputation methods, including k-nearest neighbors, which helped fill in some gaps. I also presented the option of using models that handle missing data natively. By showcasing the analysis with and without the imputed data, I was able to communicate potential biases clearly. This experience taught me the importance of transparency and critical thinking when dealing with missing values.