During this 30-day internship, I completed structured, hands-on analytics work across Excel, Python, SQL, BI tools, and beginner-level ML. I worked directly with real datasets and delivered measurable outputs. I handled 1,000+ rows of raw sales and operational data, cleaned it using Excel, Power Query, and Python, and eliminated 90%+ data inconsistencies through standardization, duplicate removal, and formatting fixes. I built 4 analytical Excel dashboards using PivotTables, automated formulas, 6+ chart types, and VBA-based report automation—reducing manual reporting effort by 40%. These dashboards tracked monthly revenue, category-wise sales, inventory levels, and KPI changes. Using Tableau and Power BI, I developed 3 interactive dashboards with DAX measures, drill-throughs, and data modeling. These dashboards highlighted revenue trends, fast- and slow-moving products, and inventory flow, improving data visibility across categories. In Python, I processed 10,000+ data points using Pandas and NumPy, performed complete EDA cycles, and generated 20+ visualizations with Matplotlib/Seaborn. This helped identify outliers, distribution patterns, and category-specific performance issues. For SQL, I wrote 10+ MySQL queries covering joins, aggregations, date functions, and subqueries for sales and stock reporting. Additionally, I used MongoDB to practice CRUD operations and explore NoSQL structures. Overall, I worked across data cleaning, visualization, dashboarding, querying, and improving the clarity, accessibility, and usability of raw datasets.