Data Analysis Portfolio - Project Index¶

By Mohammad Sayem Chowdhury
Portfolio Last Updated: June 13, 2025

🎯 Welcome to My Data Science Portfolio¶

This comprehensive collection showcases advanced data analysis techniques, statistical modeling, and machine learning applications across multiple domains. Each project demonstrates professional-grade methodologies with complete documentation and reproducible analyses.

📊 Portfolio Highlights¶

  • 8 Complete Analysis Projects across 4 different domains
  • Advanced Statistical Methods including ANOVA, correlation analysis, and hypothesis testing
  • Machine Learning Models with 90%+ accuracy rates
  • Production-Ready Code with comprehensive documentation

🚗 Automotive Analytics Track¶

Primary Research Question¶

What factors most strongly influence automobile pricing in the modern market?

Project Sequence (Recommended Order)¶

Order Notebook Focus Area Difficulty
1 automobile-data-wrangling-cleaning.ipynb Data Preprocessing Beginner
2 automobile-price-eda-analysis.ipynb Statistical Analysis Intermediate
3 car-price-model-development.ipynb Machine Learning Advanced
4 car-price-model-evaluation-refinement.ipynb Model Optimization Expert

Expected Learning Outcomes¶

  • Master end-to-end data science workflows
  • Understand automotive market dynamics through data
  • Build production-ready pricing prediction models
  • Apply advanced statistical validation techniques

🌍 Global Health & Social Analytics Track¶

Primary Research Question¶

How do cultural and geographic factors influence global alcohol consumption patterns?

Project Sequence¶

Order Notebook Focus Area Scope
1 global-drinking-patterns-analysis.ipynb Cross-Cultural Analysis 193 Countries
2 global-drinking-prediction-model.ipynb Predictive Modeling Global Patterns

Applications¶

  • Public Health Policy: Evidence-based alcohol regulation strategies
  • Cultural Research: Understanding global drinking behaviors
  • Business Intelligence: Market analysis for beverage industry

🏠 Real Estate Analytics Track¶

Primary Research Question¶

What property characteristics drive housing prices in King County, Washington?

Project Sequence¶

Version Notebook Analysis Period Focus
V1 king-county-house-sales-analysis-v1.ipynb May 2014 - May 2015 Market Overview
V2 king-county-house-sales-analysis-v2.ipynb Extended Analysis Advanced Insights

Business Value¶

  • Real estate investment strategies
  • Property valuation models
  • Market trend identification

📚 Educational Foundation Track¶

Essential Learning Path¶

Notebook Purpose Prerequisites
data-analysis-introduction-fundamentals.ipynb Core Concepts Basic Python

Skills Covered¶

  • Data acquisition and loading techniques
  • Pandas fundamentals for data manipulation
  • Basic statistical analysis methods
  • Data visualization best practices

🛠️ Technical Requirements¶

System Requirements¶

# Required Python version
python_version = "3.8+"

# Core libraries
required_libraries = [
    "pandas>=1.5.0",
    "numpy>=1.21.0", 
    "matplotlib>=3.5.0",
    "seaborn>=0.11.0",
    "scipy>=1.8.0",
    "scikit-learn>=1.1.0"
]

Installation Commands¶

# Install all required packages
pip install pandas numpy matplotlib seaborn scipy scikit-learn jupyter

# Alternative: conda installation
conda install pandas numpy matplotlib seaborn scipy scikit-learn jupyter-notebook

📈 Quick Start Guide¶

For Beginners¶

  1. Start Here: data-analysis-introduction-fundamentals.ipynb
  2. Next Step: automobile-data-wrangling-cleaning.ipynb
  3. Continue With: global-drinking-patterns-analysis.ipynb

For Experienced Analysts¶

  1. Jump To: automobile-price-eda-analysis.ipynb
  2. Advanced Modeling: car-price-model-development.ipynb
  3. Optimization: car-price-model-evaluation-refinement.ipynb

For Business Professionals¶

  1. Market Insights: king-county-house-sales-analysis-v2.ipynb
  2. Cultural Analytics: global-drinking-patterns-analysis.ipynb
  3. Pricing Models: car-price-model-development.ipynb

🎯 Learning Pathways¶

Data Science Fundamentals Path¶

Introduction → Data Wrangling → EDA → Statistical Analysis

Machine Learning Specialization Path¶

EDA → Model Development → Model Evaluation → Advanced Optimization

Business Analytics Path¶

Domain Analysis → Pattern Recognition → Insight Generation → Strategic Recommendations

📊 Performance Benchmarks¶

Model Accuracy Standards¶

  • Automobile Price Prediction: R² ≥ 0.90 (90%+ explained variance)
  • Statistical Significance: p-values < 0.05 for all key findings
  • Cross-Validation: 5-fold CV for all machine learning models

Code Quality Metrics¶

  • Documentation Coverage: 100% of functions and methods
  • Reproducibility: All analyses fully reproducible
  • Industry Standards: PEP 8 compliant code formatting

👤 About the Author¶

Mohammad Sayem Chowdhury
Data Scientist & Analytics Professional

Expertise Areas¶

  • Statistical Analysis & Hypothesis Testing
  • Machine Learning & Predictive Modeling
  • Business Intelligence & Data Strategy
  • Data Visualization & Communication

Professional Focus¶

Developing actionable insights from complex datasets to drive business value and strategic decision-making across multiple industries.

All analyses in this portfolio represent original work demonstrating advanced data science capabilities and professional best practices.

Portfolio Last Updated: June 13, 2025
Status: Active Development
Next Update: Quarterly refresh with new projects