A collection of data science exercises I've worked through — cleaning messy data, exploring it, and turning it into charts that actually say something. This is coursework, not original research, but it's where I built the habits I now apply to real datasets.
Coding for answers — small problem-solving exercises using Python: working with structured data, conditional logic, pattern analysis.
jean-pockets-project,largest-islands-project,naming-colors-project,people-on-banknotes-project
Data Cleaning — the unglamorous but essential part: missing values, inconsistent formats, standardizing messy real-world data before you can do anything useful with it.
emoji-sentiment-project,solar-eclipses-project,top-songs-project,typing-speeds-project,volcanic-eruptions-project,whale-heart-rates-project
Data Visualization — taking cleaned data and turning it into charts that communicate a finding clearly.
granola-healthy-project,deepest-divers-project
Python, Jupyter, Pandas, Matplotlib.
This isn't a production project — it's a foundational skills archive. Before applying data science to trading and markets (see Python_trading), I wanted to be solid on the basics: cleaning data properly, exploring it honestly, and visualizing it without misleading anyone.