Ajith Kumar Dandu

Imported and cleaned a large Blinkit dataset using Pandas and NumPy, effectively handling null values and preparing data for robust analysis.

Conducted in-depth exploratory data analysis (EDA) leveraging descriptive statistics, correlation matrices, and aggregated summaries to identify significant trends.

Developed compelling visualizations with Matplotlib and Seaborn, including box plots, bar plots, and scatter plots, to reveal critical patterns in sales distribution and customer behavior.

Utilized `relplot` and `countplot` visual techniques to assess variable relationships and frequency distributions, enhancing data interpretability.

Cleaned and transformed a complex raw dataset using Power Query, ensuring high data quality and integrity for analysis.

Designed and implemented a robust Power Pivot model to support dynamic visualizations and complex metric calculations.

Constructed comprehensive PivotTables and diverse chart visuals (line, donut, filled map, bar) to provide actionable insights into platform performance.

Developed interactive KPI summary cards (totals, averages) with dynamic formulas and slicers, enhancing user engagement and enabling real-time data exploration.

Engineered a fully interactive Excel dashboard, facilitating dynamic filtering by time and category for enhanced decision-making.

Cleaned and prepared the Netflix dataset using Power Query by standardizing dates, removing duplicates, and transforming categorical fields for analytical readiness.

Defined intricate relationships between data tables and authored advanced DAX measures to compute key metrics like total titles by genre, average duration, and release trends.

Constructed rich visualizations including bar charts, line graphs, KPI cards, pie charts, and drill-through tooltips, surfacing deeper insights into Netflix content.

Enabled an interactive dashboard experience utilizing slicers, bookmarks, and tooltip pages, significantly enhancing stakeholder-driven data exploration and decision support.

Designed and implemented robust relational database structures, accurately mimicking realistic business scenarios for data integrity.

Utilized complex JOINs and aggregations to compute vital metrics such as total sales, customer counts, and product performance, supporting data-driven decisions.

Developed advanced subqueries and Common Table Expressions (CTEs) to segment data, enabling sophisticated conditional analytics and deeper insights.

Applied powerful window functions (e.g., `ROW_NUMBER()`, running totals) for precise trend detection and rank-based insights, optimizing data analysis efficiency.