Professional Background
I hold a Bachelor's degree in BCom (Honours), Accountancy and Finance from the University of Calcutta. From September 2024 to March 2025, I worked as a Software Sales Executive at AUTOsherpa, a SaaS company specializing in CRM solutions for automotive industries.
During my tenure, I supported dealership clients in implementing CRM solutions, tracked key sales performance metrics, and generated reports that influenced strategic decisions. This experience sharpened my communication skills and deepened my understanding of how data drives business strategy.
Now, with experience bridging business and data domains, I'm ready to transition into a full-time analytics role where I can solve meaningful problems through data-driven storytelling.
8
Projects Completed
Diverse analytics projects across multiple industries
3
Tech Stack
Excel, MySQL, Tableau
Instagram User Analytics
Project Overview
Analyzed Instagram user dataset to provide meaningful insights for business decision-making using MySQL Workbench. Focused on user engagement patterns, content performance, and growth metrics.
Key Approach
Mapped business questions to SQL queries, addressed missing values, executed complex aggregations and joins, and derived strategic insights from query outputs.
Tech Stack
MySQL Workbench 8.0 for database management, query execution, and testing. Gained hands-on experience with structured data analysis.
Marketing Analysis Insights
- Early Adopters: Identified users from 2016 like Derby Herzog for loyalty programs
- Inactive Users: Found accounts with zero posts - untapped engagement opportunity
- Top Performer: Zack_Kemmer93 achieved 48 likes, ideal for brand partnerships
- Popular Hashtags: #smile, #beach, #party drive viral content
100%
User Coverage
No bot-like behavior detected across platform
7.71
Average Posts
Photos uploaded per user indicating healthy engagement
Hiring Process Analytics
Data Cleaning
Used IQR method to identify and remove salary outliers. Applied lower bound = Q1 - 1.5 × IQR and upper bound = Q3 + 1.5 × IQR formulas.
Analysis Approach
Calculated gender distribution, average salaries, created salary class intervals, and analyzed departmental strength using Excel functions.
Visualization
Created bar charts, pie charts, and pivot tables for clear representation of hiring patterns and salary distributions.
Key Insights
4084
Male Hires
Total male employees hired
2673
Female Hires
Total female employees hired
The hiring analysis revealed a 60-40 male-to-female ratio in recruitment. Operations Department dominates with 39% of total workforce, followed by Service Department at 29%.
Salary distribution shows most employees fall in the 30k-90k range, with the 30k-60k bracket being most common. Position tier analysis revealed clear junior-to-senior progression patterns.
IMDB Movie Analysis
Comprehensive analysis of IMDB movie dataset to uncover trends in genres, languages, ratings, and profitability. Successfully identified popular genres, director influences, and the relationship between budget and box office success.
Genre Analysis
Comedy and Action dominate in volume but show average scores. Crime and Drama are quality gems with fewer films but higher ratings.
Duration Impact
Weak positive correlation (R² = 0.1304) between movie duration and IMDB score. Most successful films fall in 90-150 minute range.
Language Insights
French films achieve highest average ratings (7.29) despite lower volume. English films show mixed reception with high variability.
Director Influence
Christopher Nolan leads with 8.42 average rating. Strong correlation between director reputation and movie success confirmed.
Budget vs Success Analysis
The analysis revealed a surprising insight: higher budget doesn't guarantee higher profit. The correlation between budget and gross revenue is only 0.096, showing that creative execution often outweighs financial investment.
Avatar achieved the highest profit at $523,505,847, demonstrating that strategic storytelling and technical innovation drive success more than budget size alone.
48%
Model Accuracy
Regression model explains variation in movie success factors
0.096
Budget Correlation
Weak correlation between budget and box office success
Car Features Impact on Price & Profitability
Analyzed car dataset to uncover key factors influencing vehicle pricing and fuel efficiency. Used regression analysis and correlation studies to identify the most important price-determining features.
1
Data Preparation
Cleaned missing values, standardized categorical data, created new metrics like average MPG and grouped numerical values for analysis.
2
Regression Analysis
Used LINEST() function to identify price-impacting variables. Model explains 48% of price variation with R² = 0.48.
3
Interactive Dashboard
Built comprehensive Excel dashboard with filters and slicers for dynamic exploration of car features and pricing relationships.
Key Pricing Insights
Engine cylinders have the strongest positive influence on MSRP, while rear and four-wheel drive configurations actually reduce prices. The analysis revealed that luxury brands like Bugatti command premium pricing averaging $1.7M, while practical brands focus on volume sales.
Fuel efficiency shows an interesting trade-off: every additional engine cylinder reduces highway MPG by 3 units, confirming the classic performance vs. economy balance.
48%
Model Accuracy
Regression model explains price variation factors
-0.62
Cylinder-MPG Correlation
Strong negative relationship between power and efficiency
$1.7M
Bugatti Average
Highest average MSRP among luxury brands
ABC Call Volume Trend Analysis
Analyzed inbound call data for ABC Insurance Company to understand call patterns, optimize agent workload, and develop strategic staffing plans. Created data-driven recommendations to reduce abandon rates below 10%.
01
Data Analysis
Calculated average call duration per time slot and built call volume charts to identify peak hours and patterns.
02
Agent Requirements
Computed staffing needs using 90% call answer target, factoring talk time and 270-300 minute daily agent capacity.
03
Night Shift Planning
Designed night shift strategy assuming 30% of daily calls distributed across 12 hourly buckets with optimized staffing.
04
Optimization
Adjusted assumptions for realistic staffing: reduced call duration to 60s and increased agent capacity to 300 minutes.
Staffing Recommendations
Peak Hours Strategy
Call volumes peak between 11 AM and 1 PM, accounting for over 34% of total calls. Recommend highest agent strength during this window with gradual tapering post-3 PM.
15-20 agents needed during peak hours to maintain service levels and prevent agent exhaustion.
Night Shift Plan
Night shift requires 4-18 agents based on time slots. Lowest demand at 1-4 AM (4 agents) aligning with customer sleep cycles. Peak morning hours (8-9 AM) need 18 agents.
198.62
Average Call Duration
Seconds per answered call across all time buckets
10%
Target Abandon Rate
Maximum acceptable call abandonment threshold
90%
Service Level
Percentage of calls to be answered within standards