#!/usr/bin/env python3# arijit_mondal.py — no exaggeration, no fillerclassArijitMondal:
""" Final-year B.Tech ECE student @ IEM Kolkata. Data analyst by practice. IEEE researcher by night. Firm believer that clean data beats clever models. """def__init__(self):
self.name="Arijit Mondal"self.location="Kolkata, India 🇮🇳"self.degree="B.Tech — Electronics & Communication Engineering"self.institute="Institute of Engineering & Management, Kolkata"self.status="Final Year → Actively seeking DA / BI / Analytics roles"self.publications=3# IEEE conference papers. peer-reviewed. real ones.@propertydefstack(self) ->dict:
return {
"languages" : ["Python", "SQL"],
"data_libs" : ["Pandas", "NumPy", "Scikit-learn",
"Matplotlib", "Seaborn", "Plotly"],
"visualization" : ["Tableau", "Excel (Pivot Tables, Power Query)"],
"workflow" : ["Git", "GitHub", "Jupyter", "VS Code", "Linux (basic)"],
"research" : ["Silvaco TCAD"], # semiconductor device simulation
}
@propertydefcurrently_building(self) ->list:
return [
"🌍 Seismic Risk Analysis → 18,000+ USGS records, ETL + Random Forest",
"🎭 Deepfake Evaluation Study → ~1,000 samples, adversarial robustness",
"📊 Sales Analytics Dashboard → Tableau KPI tracking, YoY, segmentation",
"🔬 Organic Transistor Paper → IEMECON 2025 (Silvaco TCAD simulation)",
]
def__repr__(self):
return (
"I list what I can defend in an interview. ""What you see here is what you get in the room."
)
me=ArijitMondal()
print(repr(me))
# → "I list what I can defend in an interview. What you see here is what you get in the room."
💡 Why no 30-tool dump?
I've been burned by overstating skills in interviews. Everything listed above:
I can write the code, explain the logic, and answer follow-up questions. Full stop.
Dataset: 18,000+ USGS earthquake records What it does:
| Dataset: ~1,000 video samples What it does:
| ||||||||||||
Dataset: Superstore Sales + E-commerce customer behavior What it does:
| Tool: Silvaco TCAD
Peer-reviewed. Published. Real citations. Not side projects. |
| Certification | Issuer | Verified |
|---|---|---|
| 🏅 McKinsey Forward Program | McKinsey & Company | Credly ✅ |
| 🔐 SC-900: Security Fundamentals | Microsoft | Credly ✅ |
| 🗃️ SQL for Data Science | UC Davis / Coursera | Coursera ✅ |
| 📈 Data Analytics Job Simulation | Deloitte / Forage | Forage ✅ |
| 🛡️ ISWDP Certification | — | Verified ✅ |
$ cat current_status.txt
📚 Learning → Advanced SQL (CTEs, window functions, query optimization)
📊 Building → Tableau storytelling + business-framed EDA writeups
🎯 Targeting → Entry-level DA / BI Analyst / Analytics Engineer roles
🤝 Open to → Referrals, collaborations, and brutally honest feedback2024–25 checklist:
- 3 IEEE conference publications
- McKinsey Forward Program
- Microsoft SC-900 Certification
- SQL for Data Science (Coursera)
- End-to-end projects on real, large-scale datasets
- First full-time Data Analyst role ← this one's next
- Open source contribution to a data tooling project
- Publish a public EDA writeup that actually gets read
Hiring for DA / BI / Analytics roles? Or know someone who is?
I'd genuinely appreciate the connection — not a form message, just a real one.
What I bring to the table:
- Python + SQL fundamentals I can demonstrate live in an interview
- ETL and EDA experience on messy, real-world datasets (not just Kaggle defaults)
- IEEE research background — I know how to structure, document, and explain technical work
- No exaggeration. No inflated stacks. Interview-proof skills only.
