# BloomInData


## Posts

- [CUF vs PR vs Specific Yield: Three Metrics, Three Different Questions](https://bloomindata.in/cuf-vs-pr-vs-specific-yield/): CUF vs PR vs specific yield: three solar metrics that sound similar but answer different questions. See the formulas, a four plant comparison and which one to use.
- [Solar KPIs: 12 Essential Metrics With Simple Formulas](https://bloomindata.in/solar-kpis-for-beginners/): Solar KPIs turn raw energy readings into decisions. Learn 12 essential metrics, from performance ratio and CUF to degradation and LCOE, with simple formulas and one worked example plant.
- [Revenue Grew 15% Last Quarter: Was It Volume, Price, or Product Mix?](https://bloomindata.in/revenue-analysis-sql/): Revenue is up about 15%, but why? A beginner-friendly revenue analysis in SQL on the Brazilian Olist dataset that splits growth into volume, price, and mix, with real outputs and a business recommendation.
- [Cart Abandonment Analysis: 4 Useful SQL Queries](https://bloomindata.in/cart-abandonment-analysis-case-study/): I add things to my cart and change my mind too, and it turns out businesses can measure exactly where that happens. Here is how I explored cart abandonment analysis with SQL and a dashboard.
- [Festive Season Funnel Analysis: Where Diwali Marketing Campaigns Actually Convert](https://bloomindata.in/festive-season-funnel-analysis/): Diwali offers are everywhere, but which channels actually convert? In this festive season funnel analysis, I break a Diwali campaign down by funnel stage, channel, CAC, ROAS and RTO, so "sales went up" turns into a decision you can act on.
- [75 Ecommerce and Marketing Analytics Interview Questions (With Model SQL and Answers)](https://bloomindata.in/ecommerce-marketing-analytics-interview-questions/): A working set of 75 ecommerce and marketing analytics interview questions, each with a model answer and SQL query built against one shared practice schema, from business fundamentals through advanced SQL and statistics.
- [Why Your SQL Answer Is Wrong: 5 Critical Questions to Ask First](https://bloomindata.in/why-sql-answer-is-wrong/): Revenue is up 20% — but is that really good news? Learn how e-commerce analysts can look beyond revenue, analyze discounts, returns, margins, and repeatability, and uncover when impressive growth is actually a warning sign.
- [Why domain knowledge matters (and why I had to learn it the hard way)](https://bloomindata.in/e-commerce-metrics-data-analyst/): A data-analyst aspirant's real breakdown of the e-commerce metrics that actually come up in interviews—CAC, ROAS, conversion rate, AOV, and LTV—with a worked example you can follow along with.
- [Data Analyst Interview Questions 2026: The Complete SQL, Python & Case Study Guide](https://bloomindata.in/data-analyst-interview-questions-2026/): 30 real data analyst interview questions for 2026 — full SQL schemas, Python/pandas code, product case frameworks, and company-by-company salary benchmarks. Everything from surviving the automated OA to acing the behavioral round.
- [Data Analyst Skills Roadmap (2026): The Complete Guide](https://bloomindata.in/data-analyst-skills-roadmap-2026/): SQL, Excel, statistics, Python, a BI tool — in that order, with real code, a working timeline, and the mistakes I made along the way. Here's the exact data analyst roadmap I'm building for myself in 2026, and why most versions of this plan skip the parts that actually matter.
- [Customer Churn: 5 Shocking Reasons You're Losing Users](https://bloomindata.in/customer-churn-5-shocking-reasons-youre-losing-users/): In this dataset alone, nearly 1 in 4 telecom customers cancel each year - over $1.6 million in lost revenue. Here's why, what it actually costs, and what a realistic, data-driven repair is worth.
- [SQL Window Functions Explained: 7 Powerful Techniques (With a Live Demo)](https://bloomindata.in/sql-window-functions/): RANK() and DENSE_RANK() appear practically identical until you reach a tie, and that single difference causes more SQL interviews than anything else. Here's an interactive description of each of the 7 window function strategies, along with a live demo you can try yourself.
- [Data Analyst Case Study: 5 Surprising Google Trends Lessons](https://bloomindata.in/data-analyst-case-study-google-trends-forecasting/): A data analyst case study on building a Python forecasting app with Prophet & Streamlit — full methodology, results, and honest lessons learned.

## Pages

- [Dataset Library](https://bloomindata.in/dataset-library/): Datasets I'm exploring and collecting as I learn data.
- [My account](https://bloomindata.in/my-account/)
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- [Portfolio](https://bloomindata.in/portfolio/): Explore my analytics projects across customer analytics, e-commerce, marketing, HR, telecom, energy and business intelligence.
- [Home](https://bloomindata.in/): I combine data analytics, business understanding and digital experience to turn data into meaningful insights, dashboards and actionable business solutions.
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- [Practice](https://bloomindata.in/practice/)
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- [Case Studies](https://bloomindata.in/case-studies/)
- [Career](https://bloomindata.in/career/)
- [Resources](https://bloomindata.in/resources/)
- [Blog](https://bloomindata.in/blog/)
- [About](https://bloomindata.in/about/): BloomInData is a Data Analytics and Data Science ecosystem designed to help people learn data skills, understand business problems, practice with realistic scenarios, build projects, and access resources for their data careers.
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- [Privacy Policy](https://bloomindata.in/privacy-policy/): BloomInData (“BloomInData,” “we,” “our,” or “us”) respects your privacy and is committed to protecting the personal information you provide while using our website.

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