As a Data Analyst at Pathway Tech, I frequently handle large datasets. A standout project involved a set of customer purchase records spanning five years – a total of about 10 million entries. The starting point was to familiarize myself with the dataset, understanding the different data types and checking for anomalies or missing values.
I utilized SQL for data extraction and manipulation, and Tableau for visualization. The data was cleaned, transformed, and normalized to facilitate accurate analysis, applying the ETL (Extract, Transform, Load) process. I then implemented the K-means clustering algorithm to classify customers based on their purchasing behavior.
The insights generated from this analysis allowed us to understand distinct customer segments better and tailor our marketing efforts for each group. This led to a 20% increase in targeted ad conversion rates, demonstrating the value of detailed data analysis in driving effective business strategies.