Retail & Warehouse Sales Analysis — data quality checks and exploratory dataset preparation
Conducted exploratory data analysis on a public retail and warehouse sales dataset (307,547 records, 2017–2020) sourced from data.gov. Performed data quality detection and documentation, including handling and assessing missing values (99.99% NA in WAREHOUSE_SALES). Generated derived metrics and insights (e.g., top products, monthly trends, seasonality, growth rates, volatility) to support downstream reporting and decision-making. • Cleaned and transformed raw tabular data in R • Queried and validated records using SQL in DBeaver (filters, aggregations, JOINs) • Computed statistical indicators such as CV and z-score for variability analysis • Built an interactive reporting layer in Power BI (KPIs, slicers, and visual breakdowns)