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DoomDomzy/README.md

Hi there, I'm Daniel! 👋

Focus Background

👨‍💻 About Me

Economics student transitioning into Data Science & Analytics.
I combine statistical rigor, econometrics, and machine learning to solve real-world problems — with a strong passion for the intersection of economics, sports analytics, and data engineering.

  • 🔭 Currently Upskilling: Python, Advanced SQL, pandas, scikit-learn, and Data Engineering best practices.
  • 🎯 Goal: Building robust end-to-end data pipelines and predictive models with honest, production-ready validation.

🛠️ Tech Stack & Tools

Python SQL pandas NumPy scikit-learn SciPy Git GitHub

🚀 Featured Projects

⚽📊 Football Performance Persistence

Sports Econometrics & Time-Series Machine Learning

  • A sports econometrics study examining whether a football team's performance in La Liga persists year-over-year or reverts to the mean.
  • Features rigorous statistical tests (Pearson/Spearman) alongside a machine learning pipeline utilizing temporal validation (TimeSeriesSplit) to predict match outcomes, evaluated honestly against a baseline model.

Tech Stack: Python · pandas · scikit-learn · SciPy · Seaborn

🧹 Data Wrangling Pipeline

Production-Ready Data Cleaning Engine

  • An end-to-end data sanitization pipeline handling exact duplicates, text inconsistencies, mixed date formats, and extreme outliers.
  • Implements automated quality controls (assert statements) to programmatically guarantee dataset integrity before export.

Tech Stack: Python · pandas · NumPy

🚢 Titanic EDA Analysis

Exploratory Data Analysis & Statistical Hypothesis Testing

  • In-depth exploration of survival drivers on the Titanic, employing statistical hypothesis testing (Chi-Square, t-tests) to validate empirical findings.
  • Includes dynamic multi-variable visualizations in Plotly and an integrated SQL querying workflow.

Tech Stack: Python · pandas · SQL · Seaborn · Plotly · SciPy

⚡ Portfolio in active development — stay tuned for new projects! ⚡

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  1. Data-wrangling-pipeline Data-wrangling-pipeline Public

    End-to-end data cleaning pipeline handling duplicates, inconsistent text, mixed date formats and outliers — with automated data-quality validation via assertions.

    Python

  2. Football-performance-persistence Football-performance-persistence Public

    Sports economics case study: testing performance persistence vs. mean reversion in La Liga standings, plus a time-series-validated ML pipeline predicting match outcomes against a naive baseline.

    Python

  3. Titanic-EDA-analysis Titanic-EDA-analysis Public

    Exploratory data analysis of Titanic survival patterns using pandas, SQL and Seaborn — with statistical hypothesis testing (chi-square, t-tests) to validate findings.

    Python