Aditya Kumar Soni Email and Phone Number

In my current role as a Data Analyst at Onward Technologies, I have led end-to-end data analytics projects, improving operational efficiency by 15% through data interpretation and visualization. I developed and maintained Tableau dashboards that reduced reporting time by 25%, and optimized SQL queries that provided timely insights for decision-making. This experience has honed my ability to break down complex problems, collaborate with cross-functional teams, and deliver impactful data-driven solutions—all skills that align closely with the responsibilities at Bain’s BCN Labs.

Analyzed: Conducted comprehensive data analysis on Aerofit treadmill usage, customer demographics, and product performance, examining a dataset with 180 entries across 9 variables using Python, pandas, seaborn, and matplotlib.Visualized: Created 10+ insightful visualizations, including histograms, joint plots, and pair plots, to depict relationships between product usage and various demographic factors, enabling clear identification of trends and patterns.Normalized: Standardized income and mileage data, covering a range of $29,562 to $104,581 and 21 to 200 miles respectively, to enhance the accuracy of statistical analysis and ensure consistency across datasets.Encoded: Applied one-hot encoding to categorize 3 treadmill models (KP281, KP481, KP781), facilitating a more effective analysis of customer preferences and behavior.Identified: Determined marginal probabilities for treadmill models, with findings such as KP281 being chosen by 45% of users, KP481 by 33%, and KP781 by 22%, providing a clear understanding of market share.Detected: Identified and managed outliers, ensuring data integrity and improving the reliability of analysis results by maintaining a consistent range within 3 standard deviations from the mean.

Data Cleaning and Preparation: Processed and cleaned a dataset of 8,07 entries, addressing missing values and standardizing formats, resulting in a 100% usable data set for analysis.Visualization and Analysis: Developed comprehensive visualizations using seaborn and matplotlib, including histograms, pair plots, and heatmaps, to identify trends in the release years and types of Netflix content.Correlation Analysis: Conducted a correlation analysis of Netflix data, revealing key insights that guided strategic decisions in content production, contributing to a projected 10% increase in viewer engagement.Data Insight Extraction: Analyzed the distribution of Netflix content by type and release year, identifying that 70% of the content was released post-2015, which supported the development of targeted marketing strategies.