Answer:

During my time as a Data Analyst, I encountered a situation where I had to clean and manipulate messy data in order to derive actionable insights for decision-making. The dataset I was working with contained numerous missing values, inconsistencies, and outliers, making it challenging to extract any meaningful information.

To address this, I first conducted a thorough data cleaning process, which involved removing duplicates, filling in missing values using interpolation techniques, and standardizing the format of the data. I also identified and dealt with outliers by applying statistical methods such as z-score analysis.

Once the data was cleaned, I proceeded to manipulate it by performing various transformations and aggregations. This included feature engineering to create new variables that better represented the underlying patterns in the data, as well as aggregating data to different levels of granularity for more insightful analysis.

Through this process, I was able to derive actionable insights from the cleaned and manipulated data, which informed key decision-making processes within the organization. This experience not only showcased my technical skills in data cleaning and manipulation but also demonstrated my ability to translate messy data into valuable business intelligence.