Turning Raw Data Into
Intelligence.

BS & MS in Computer Science (WGU) · ITILv4 · LPI Linux Essentials. Hands-on experience analyzing City of Buffalo open datasets to drive evidence-based decisions and public transparency.

Python SQL ETL Pipelines Buffalo Open Data ITILv4 · Linux
View Portfolio Contact Me
01 / Profile

Mission-Oriented Analytics

I am a data professional holding a Bachelor of Science in Computer Science from WGU, currently pursuing a Master of Science in Computer Science (AI/ML) — also from WGU.

My expertise lies in transforming messy, multi-source datasets into actionable intelligence. I have applied ETL engineering, statistical analysis, and data visualization directly to City of Buffalo open data.

2+
Data Projects
2
Industry Certs
MS
CS In Progress
BS
Computer Science
02 / Background

Education & Certifications

B.S. Computer Science

Completed
Western Governors University

Competency-based program covering algorithms, data structures, database design, software engineering, and applied statistics.

Key Coursework: Data Management, Applied Statistics, Scripting & Programming.

M.S. Computer Science

In Progress
Western Governors University — Expected 2026

Graduate-level coursework building depth in algorithms, data engineering, and computing systems applicable to civic data modernization.

Key Coursework: Advanced Algorithms, Systems Architecture, Machine Learning.

ITIL v4 Foundation

Certified
Axelos / PeopleCert

IT service management framework covering service lifecycle, continual improvement, and IT governance.

LPI Linux Essentials

Certified
Linux Professional Institute

Foundational Linux certification covering command-line operations, file management, scripting, and system administration.

03 / Capabilities

Technical Arsenal

Languages & Databases

  • Python
  • SQL (PostgreSQL/MySQL)
  • Bash

Data Engineering

  • ETL Pipeline Design
  • Pandas / NumPy
  • Data Quality Validation

Machine Learning & BI

  • Scikit-learn / NLP
  • Matplotlib / Seaborn
04 / Portfolio

Projects

Python Pandas ETL Matplotlib

Buffalo Absentee Landlord Analysis

Investigated whether landlords residing outside Buffalo have higher housing violation rates and lower case resolution rates, using three City of Buffalo municipal datasets joined across multiple keys.

  • Data Sources: 311 Housing Violations, 2025 Final Assessment Roll, Housing Court Cases — joined via PropertyID, Print Key, and normalized address matching.
  • Key Finding: Non-local landlords accumulate 28.8% more violations per 100 properties (189.4 vs 147.1) than local landlords.
  • Resolution Gap: Court resolution rates (~41%) significantly lag 311 closure rates (~96%), revealing a systemic follow-through issue.
  • Outputs: ETL pipelines, enriched CSVs, and publication-ready visualizations including violation rate and resolution comparison charts.
Python Pandas Analysis Visualization

Buffalo 311 Calls: Neighborhood & Resolution Time Analysis

Analyzed City of Buffalo 311 service request data to surface neighborhood-level disparities in call volume and service resolution times, translating findings into actionable policy recommendations.

  • Engineering: Data cleaning, aggregation, and time-series feature engineering to compute average resolution durations by neighborhood and call category.
  • Insights: Identified underserved neighborhoods with significantly longer resolution times across comparable call categories.
05 / Connect

Ready to Serve

Email: mhoss43@proton.me GitHub:mdintisarhossain Location: Buffalo, NY (City Resident)