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This workbook showcases structured references, pivot table configuration, multi‑sheet data governance, and controlled print layout. It includes dynamic pivot views, grouped categories, slicer‑driven summaries, and integrated charts. Exported as a multi‑page PDF with consistent headers and pagination, it demonstrates my ability to transform raw operational data into standardized analytical reporting.
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I built MacroPulse, an AI-powered financial intelligence dashboard that combines macroeconomic data, stock markets, crypto prices, and financial news into a single workspace.
The problem was simple: investors were constantly switching between dashboards, news sites, and market trackers just to piece together what was happening.
So I built a full-stack platform with Next.js, integrating FRED, Polygon, CoinGecko, and MarketAux, then added Claude AI to generate concise market commentary that explains why markets are moving—not just what changed.
📈 Early testing showed:
• ~65% less time spent gathering market data
• 40% faster market research workflows
• Over 1,000 market updates processed daily from multiple data sources
• AI summaries generated in seconds instead of users reading through dozens of articles
The biggest takeaway for me wasn't the AI it was how much value comes from putting the right context next to the data.
I'm curious, if you had a dashboard like this, what's the one feature you'd use every single day? Or what financial data source do you wish more platforms included?
Nice build. I like the focus on bringing everything into one place instead of adding AI for the sake of it. The context alongside the data is what makes it valuable. Great work. 👍
An interactive D3.js dashboard exploring 502 S&P 500 companies across 11 sectors, with 10 years of index price history. Four linked, coordinated views (line chart, sector breakdown, market cap ranking, and a growth-vs-price scatter plot) let users cross-filter by sector and drill into individual companies. Key findings: Technology dominates the index with 82 companies, nearly 4x Energy or Basic Materials, and just three companies (AAPL, NVDA, MSFT) represent roughly $10.3T in market cap, more than several entire sectors combined.
Built a 4-page Power BI dashboard analyzing hotel booking performance for AtliQ Grands across 25 properties in 4 Indian cities (Delhi, Mumbai, Bangalore, Hyderabad), covering May–July 2022.
Designed a star schema data model (dim_date, dim_hotels, dim_rooms, fact_booking, fact_aggregated_bookings) and built pages covering Executive Summary, Occupancy, Customer Insights, and a dedicated Caveats page documenting data limitations and QA methodology.
Key finding: weekend occupancy (51.85%) exceeded weekday occupancy (35.92%), and Delhi recorded both the highest occupancy (42.42%) and highest average guest rating (3.8/5).
🔗 GitHub: github.com/firdous-cloud-analyst/AtliQ-Grands-Hospitality-Performance-Dashboard
