As a Data Analyst at Infinia Solutions, I have had numerous opportunities to engage in in-depth data cleaning. One notable project that comes to mind is when we were commissioned to analyze demographic data for a national survey conducted by a non-profit organization.
The dataset was initially filled with inaccuracies, lacked standardization, and contained multitudinous outliers. Leveraging the power of R and its dplyr and tidyr packages, I implemented various data cleaning stages. This involved treating missing values, regularizing categories for accurate comparisons, standardizing the measurement units across different data columns, and identifying and rectifying errors in data entry.
Post cleaning, we were able to generate valuable insights for our client that helped them tailor their outreach programs effectively. This project demonstrated the critical role data cleaning plays in ensuring the validity of our analysis and the actionable insights we provide to clients.