“I don’t just read data — I make it speak.”
I’m Vikas Khairnar, a results-driven Data Analyst & Power BI Developer based in Pune, India, transitioning from a software engineering background into the world of Data Analytics & Data Engineering.
With hands-on experience in Power BI, MS Fabric, SQL, and Python, I specialize in designing dashboards that tell stories, automating reporting pipelines, and translating complex datasets into clear business intelligence.
| ### 🔵 What I Do - Build **Power BI dashboards** for business reporting - Design **data models** and ETL pipelines in **MS Fabric** - Write **SQL queries** for complex data extraction - Automate reporting with **Excel + Power Query** - Translate business requirements into analytical solutions | ### 🟢 What I Can Do - End-to-end **BI development** (source → insight) - **Requirement gathering** & stakeholder communication - Build **Medallion architecture** in MS Fabric / Lakehouse - Python scripting with **Pandas, NumPy, Scikit-learn** - **Data storytelling** through visuals & reports | ### 🏆 What I've Achieved - 🎓 PG in **Data Science & Analytics** – Imarticus Learning - 💼 **1.7 yrs** as Associate Software Engineer – Xyrem Software Solutions - 📊 Built healthcare & bike sales dashboards (see pinned repos) - 📝 Created structured learning notes on Power BI & MS Fabric - 🔄 Successfully transitioned from Dev → Data |
| # | Course | Platform | Status | Notes |
|---|---|---|---|---|
| 1 | PG Program – Data Science & Analytics | Imarticus Learning | ✅ Completed | — |
| 2 | Power BI – End to End | Self / YouTube | ✅ Completed | 📝 Notes |
| 3 | Microsoft Fabric for Beginners | Microsoft Learn | ✅ Completed | 📝 Notes |
| 4 | SQL for Data Analysis | Self Study | ✅ Completed | — |
| 5 | Python for Data Science | Coursera / Udemy | ✅ Completed | — |
| 6 | Azure Data Engineering (DP-203) | In Progress 🔄 | 🔄 Ongoing | — |
💡 More courses being added as I grow. Watch this space!
These are my structured learning notes (PDFs) created while studying topics end-to-end. Great for anyone learning these tools!
| Topic | Description | Link |
|---|---|---|
| 📘 Power BI – Complete Notes | DAX, Data Modelling, Visuals, Row-Level Security | View Notes → |
| 🏗️ MS Fabric – Architecture & Concepts | Lakehouse, Dataflows, Pipelines, Medallion Architecture | View Notes → |
| 🗄️ SQL – Query Optimization | Joins, CTEs, Window Functions, Performance Tuning | View Notes → |
| 🐍 Python for Analytics | Pandas, NumPy, EDA workflows | View Notes → |
📌 Notes are stored in the
/notesfolder of this repository or linked repos. PDFs are structured, clean, and beginner-friendly.
|
PG Data Science & Analytics Imarticus Learning 📄 View Certificate |
MS Fabric Fundamentals Microsoft Learn 🏅 View Badge |
Power BI Developer Self Certified via Projects 📄 View Certificate |
Python for Data Science Coursera / Udemy 📄 View Certificate |
📁 All certificates and badge images are stored in
/certificationsfolder.
Analyzing patient blood test data to identify trends, flag anomalies, and support clinical decisions.
| 🔗 View Repository | 📊 View Dashboard |
Tech Stack: Tableau Excel Data Cleaning Healthcare Analytics
End-to-end Excel dashboard tracking sales performance, customer demographics, and regional trends.
| 🔗 View Repository | 📈 View Dashboard |
Tech Stack: Excel VLOOKUP PivotTables Charts
Building a Bronze → Silver → Gold data lakehouse pipeline using Microsoft Fabric, Dataflows Gen2, and Delta Lake.
🔗 Repository in progress…
Tech Stack: MS Fabric Delta Lake PySpark OneLake Power BI
Workforce attrition analysis, headcount trends, and department-wise performance using Power BI with Row-Level Security.
🔗 Repository in progress…
Tech Stack: Power BI DAX SQL Power Query
| Title | Platform | Link |
|---|---|---|
| 🔵 Getting Started with Microsoft Fabric | Coming Soon | 📝 |
| 📊 Power BI vs Tableau — Which one & When? | Coming Soon | 📝 |
| 🏗️ What is Medallion Architecture? (Simple Explanation) | Coming Soon | 📝 |
| 🐍 5 Pandas Tricks Every Data Analyst Should Know | Coming Soon | 📝 |
💬 Blogs will be published on LinkedIn and Medium. Follow along!