I don’t just analyze data — I question it, explore it, and translate it into decisions people can act on.
I’m building a strong profile in Data Analytics + Data Science + Data Engineering, with a focus on Azure, research-grade rigor, and clean storytelling.
- 🎓 M.S. Data Analytics — Webster University (St. Louis, USA)
- 🎓 Background: MBA (Technology Management) + B.E. (Electrical & Electronics Engineering)
- 🧠 Mindset: curiosity → experimentation → insight → impact
- 🌍 Long-term mission: build a research-strong profile for O-1 / EB-1 (papers, citations, real contributions)
✅ Analytical thinking & unconventional questioning (I love “why?” more than “what”)
✅ Exploratory Data Analysis (EDA) that actually drives modeling decisions
✅ Predictive Modeling (classification, regression, ensemble approaches)
✅ Time Series Analytics (patterns, stationarity, residual diagnosis, forecasting)
✅ Data Storytelling (turning numbers into clear business narratives)
✅ Dashboards that communicate trends, risk, and opportunity instantly
✅ Research-style work ethic (structured methodology, documentation, reproducibility)
✅ Full-stack builder mindset (I build products — not only notebooks)
- RFID-based security system (Embedded + IoT)
- Analytics + scripting (R Studio + Python)
- Practical build mindset + end-to-end delivery
I focus on projects that have clear business value, strong methodology, and crisp storytelling.
Highlights
- Forecasted Dow Jones for 3 months using time series workflow
- Tested patterns (trend/seasonality), stationarity, residual diagnostics
- Built interpretable forecasting outputs & evaluation
🔗 Repo: https://github.com/Pratyusha108
Highlights
- Built a routine tracking app (React Native + Node + SQLite)
- Logs completion history for long-term behavior analytics
- Export-ready data for dashboards and future EDA
- Designed with a “data product” mindset: tracking → storage → analysis → insight
🔗 Repo: https://github.com/Pratyusha108
What I practice deeply
- Smartphone resale price prediction (regression workflow)
- Mailing list response & tiering (classification + segmentation)
- Mortgage payback forecasting (predictive + business risk framing)
✅ Strong emphasis on EDA, modeling comparison, and decision-ready outputs.
- ⏳ Time Series: forecasting, validation strategy, real-world anomaly thinking
- 🧠 Deep Learning: foundations + practical use-cases
- 🧪 Advanced EDA: feature engineering, leakage prevention, robust evaluation
- ⚖️ Data Ethics & Responsible AI: governance + risk thinking
- ☁️ Azure Path: building credibility with cloud-ready pipelines and MLOps mindset
- 🧾 Clean documentation & reproducible workflows
- 🧠 Strong reasoning + structured problem solving
- 🧩 I connect the dots across business + model + data constraints
- 🔍 Detail-oriented without losing the big picture
- 🎯 Consistent improvement mindset — I iterate until it’s excellent
📫 Email: saipratyushagorapalli333@gmail.com
🔗 LinkedIn: https://www.linkedin.com/in/pratyusha-g-a92915229/
💻 GitHub: https://github.com/Pratyusha108
📊 Kaggle: Coming soon (I’m diving deep into challenges)
“If you have never failed, you have never tried something new.”