♻️ Python for Finance & Data Science: My Learning Journey

A comprehensive learning archive that documents my progression from Python fundamentals to advanced data science and machine learning applications.

💡 Why this project matters:

Python has become the backbone of modern data analytics. This repository is more than just coursework — it's my curated journey of transforming from finance-focused programming basics into a data science practitioner capable of handling complex datasets, building predictive models, and automating workflows.

What's Inside

• Core Python Foundations: Syntax, control flow, data structures, functions, and object-oriented programming — the building blocks that underpin every project.
• Data Science Stack: Mastery of pandas, NumPy, matplotlib, seaborn for analysis, cleaning, and visualisation, giving me the ability to handle raw datasets and turn them into insights.
• Applied Projects: Hands-on analysis of real-world datasets such as Netflix (trends in film and series) and NYC Schools (education performance and demographics).
• Machine Learning Foundations: Early experiments with scikit-learn and OpenML datasets to understand workflows in predictive analytics, classification, and regression.
• Workflow Automation: ETL pipelines, structured repositories, and GitHub integration to show reproducible, scalable approaches to working with data.

Key Highlights

• 📺 Netflix Data Analysis: Cleaned and explored data spanning multiple decades, visualising production trends, content growth, and audience shifts.Skills: data cleaning, exploratory data analysis, storytelling with visuals.
• 🏫 NYC Schools Analysis: Analysed education data to highlight disparities in performance and demographics across New York's public schools.Skills: statistical analysis, variance exploration, insight reporting.
• 🤖 Machine Learning Experiments: Implemented supervised models on OpenML datasets, testing classification and regression approaches for predictive workflows.Skills: scikit-learn basics, model training, evaluation metrics.
• 🛠️ Personal Projects: Built OOP-based programs, custom class definitions, and data pipelines that demonstrate my ability to go beyond coursework into practical experimentation.

Why It Matters

This project demonstrates more than just technical skill — it shows:

• My ability to translate structured learning into applied solutions
• A consistent, documented track record of growth from beginner → practitioner
• My approach to combining finance domain knowledge with Python-driven data science
• A commitment to continuous learning and building a portfolio of reproducible, transparent projects

By following this journey, anyone can see how I learn, apply, and deliver — not just the end result.

🛠️ Tools & Technologies

• Programming & Data Science: Python 3.x, pandas, NumPy, matplotlib, seaborn, scikit-learn
• Development: Jupyter Notebooks, GitHub version control
• Data Formats: CSV, Excel, SQLite, HDF5, SAS, Stata, MATLAB
• Workflow: Structured repositories, automation pipelines, reproducible experiments

📊 Progress Tracking

This repository also integrates with WakaTime to track coding activity, showing how I invest consistent time into Python practice and projects. It provides a transparent log of my effort and progress in real-time.

Explore the Full Repository on GitHub → | 📈 View My WakaTime Progress