How would you approach cleaning and transforming messy or incomplete datasets to prepare them for analysis?

How to Clean and Transform Messy or Incomplete Datasets for Analysis - Data Analyst Interview Question

When dealing with messy or incomplete datasets as a data analyst, it is crucial to follow a systematic approach to clean and transform the data for effective analysis. Below are the steps that can be followed:

1. Data Understanding: Begin by understanding the structure and content of the dataset. Identify missing values, duplicates, outliers, and inconsistencies.
2. Data Cleaning: Remove any duplicate rows, handle missing values by imputing them with mean, median or mode values, correct data types, and fix any errors or inconsistencies in the data.
3. Data Transformation: Transform the data by standardizing units of measurement, normalizing numerical values, encoding categorical variables, and creating new features or aggregating existing ones.
4. Data Validation: Validate the cleaned and transformed data by running quality checks, ensuring data integrity, and verifying that the data is ready for analysis.
5. Data Documentation: Document the entire process of data cleaning and transformation, including the steps taken, decisions made, and any assumptions or modifications applied to the data.

By following these steps, data analysts can effectively clean and transform messy or incomplete datasets to prepare them for analysis, ensuring the accuracy and reliability of the results.