Md Mahmudul Hasan

Data Analyst | Power BI • Tableau • SQL • Python | Dashboards • Data Quality • Reporting Automation

My workflow: Intake → Validate → Reconcile → Analyze → Visualize → Stakeholder Support

Focus KPI dashboards & metrics Ops reporting automation Quality reconciliation & validation Tools Power BI · Tableau · Streamlit

I turn messy operational and financial datasets into decision-ready dashboards and reliable reporting pipelines—so teams can move faster and trust the numbers.

View featured projects Download resume Contact GitHub LinkedIn

Short on time? One project below gives the best overview before the resume.

Impact Snapshot

Flagship project: Hybrid GARCH–LSTM–VIX intraday volatility forecasting system

(Representative of my most technical project; similar principles applied across my work)

Quality

↓ reporting errors

Validated intraday datasets (SPY + VIX) and engineered EWMA + lagged volatility features to reduce input noise.

Speed

↓ manual effort

One-click pipeline + dashboard to compare models and metrics (MAE/RMSE) without manual rework.

Decision

Clear KPI visibility

Interactive Streamlit dashboard enabling quick model selection and explainable comparisons for stakeholders.

Quick Links:

• Resume: Resume PDF

• Projects: Projects page

Featured Projects

Three fast examples. Each one shows problem → build → outcome.

Dashboard screenshot: Intraday volatility forecasting Streamlit app

Intraday Volatility Forecasting Dashboard (Streamlit)

Streamlit | SPY 15-min + VIX | GARCH vs LSTM vs Hybrid

Problem: compare volatility models on high-frequency SPY data and explain performance clearly
Build: interactive Streamlit dashboard to benchmark GARCH, LSTM, and hybrid variants using MAE/RMSE and walk-forward evaluation
Outcome: faster, stakeholder-friendly model selection with a single dashboard view of accuracy and stability

Case study Live demo GitHub

Dashboard screenshot: Operational Reporting

Operational Reporting Dashboard

Power BI | KPI reporting | Data validation

Problem: reporting required manual checks and inconsistent inputs
Build: standardized KPIs + validation checks + refresh workflow
Outcome: faster stakeholder reporting and clearer performance visibility

Case study GitHub

Dashboard screenshot: Data Quality & Reconciliation

Data Quality & Reconciliation Checks

Python | SQL | Exceptions & discrepancy tracking

Problem: mismatched values across sources delayed reporting
Build: reconciliation rules + exception summaries + audit trail
Outcome: fewer discrepancies and higher confidence in outputs

Case study GitHub

Skills Map

Data	SQL, CSV/Excel, APIs, operational & financial datasets
Analysis	Python (pandas), R, trend & anomaly checks, KPI design
Visualization	Power BI, Tableau, Streamlit; stakeholder-ready dashboards
Quality	Validation rules, reconciliation, discrepancy investigation
Ops	Reporting workflows, documentation, SOPs, support

How I work

Clarify the business decision and define the KPI(s)
Audit data sources and establish validation rules
Build automated refresh and reporting pipelines wherever possible
Design the dashboard for one-glance comprehension
Document it (SOP) so it survives team turnover and handoffs

Want this mapped to your role?
I can tailor these projects to a job posting and show the exact KPI map it would use.

Contact

Email LinkedIn GitHub

Location: Canada (Moncton/Fredericton/Halifax)