┌─────────────────────────────────────────────────────────────┐
│ > SELECT * FROM analysts WHERE curious = TRUE │
│ AND detail_oriented = TRUE AND results_driven = TRUE; │
│ │
│ 1 row returned. ✓ │
└─────────────────────────────────────────────────────────────┘
Analytical and detail-oriented data professional who believes every dataset tells a story — you just need the right queries to hear it. I specialize in translating messy, complex data into dashboards and insights that drive real business decisions.
Currently seeking Data Analyst roles where I can apply SQL, Excel, Power BI, and Python to solve meaningful problems.
{
"querying": ["SQL — Joins, CTEs, Window Functions, Subqueries, Self-Joins"],
"spreadsheets": ["Excel — Pivot Tables, Power Query, Dynamic Dashboards"],
"visualization": ["Power BI — Data Modeling, DAX, Interactive Reports"],
"programming": ["Python — Pandas, Matplotlib, Seaborn, NumPy, EDA"],
"process": ["Data Cleaning", "EDA", "KPI Design", "Trend Analysis", "ABC Segmentation"]
}
SQL + Power BI analysis of 500+ data analyst job postings across 10+ Indian cities — skills, salaries & hiring trends.
| What I Did | Key Outcome |
|---|---|
| Designed normalized relational schema (jobs, skills, jobskills junction table) | Enabled multi-dimensional skill & salary analysis |
| Used self-joins to find skill co-occurrence patterns | SQL + Python is the most demanded combination (~338 mentions) |
| Salary distribution analysis by city and skill tier | Python & Power BI roles pay 20–30% more than Excel-only roles |
Skill gap analysis using NOT IN subqueries |
Identified exact upskilling path for entry-level candidates |
End-to-end BI solution analyzing 50,000+ retail transactions across 4 years (2015–2018).
| What I Did | Key Outcome |
|---|---|
MoM & YoY growth using LAG window functions + CTEs |
Revenue grew 50% from $4.8M (2015) → $7.2M (2018) |
| ABC customer segmentation using cumulative window functions | Identified high-LTV customer clusters for targeted marketing |
| 3-page interactive Power BI dashboard with DAX measures | West region drives 31% of total revenue |
| Product concentration risk analysis | Top 5 products = significant revenue concentration — diversification flagged |
Multi-table SQL extraction + interactive Excel Executive Dashboard with KPIs, slicers, and 7+ chart types.
| What I Did | Key Outcome |
|---|---|
| Multi-table JOIN across 9 tables (sales + production schemas) | Single flat dataset powering entire dashboard |
| Interactive slicers for Year, State, and Store | Baldwin Bikes drives 68% of $8.58M total revenue |
| Map chart (Bing) showing revenue distribution by state | 2017 was peak year at $3.84M — 42% YoY increase |
| Sales rep performance ranking | Marcelene Boyer led with $2.93M in personal contributions |
Exploratory data analysis on 9,827 Netflix movies spanning 1902–2024 using Python, Pandas & Seaborn.
| What I Did | Key Outcome |
|---|---|
| Full EDA pipeline — loading, cleaning, feature analysis | Drama is the most frequent genre; Action gets the most votes |
| Popularity vs vote count correlation analysis | Vote count is a stronger predictor of popularity than rating |
| Genre distribution and language diversity analysis | English dominates at 77% despite 43 languages in catalog |
| Release year trend analysis | 2020 saw the peak in Netflix movie releases |
▓▓▓▓▓▓▓▓░░ Power BI Advanced DAX
▓▓▓▓▓▓░░░░ Python for Data Analysis (pandas, matplotlib)
▓▓▓▓░░░░░░ Statistics & A/B Testing Fundamentals
"Without data, you're just another person with an opinion." — W. Edwards Deming