Curriculum
Twelve tracks, from working software engineer to stellar engineer and data scientist. Each lesson carries a status — planned, drafted, reviewed, or published — and this page is generated from that frontmatter, so it always reflects what actually exists.
Current goal: the first vertical slice — Python Craft → Data Wrangling → Statistics → Machine Learning → a first capstone.
Algorithms & CS Fundamentals
Patterns, complexity analysis, and the discipline behind clean solutions.
Python Craft
Typed, tested, reviewable Python — beyond the notebook.
Systems & Databases
SQL depth, indexes, caching — what code actually runs on.
Math Foundations
The math the NumPy and scikit-learn stack silently assumes.
Statistics & Experimentation
Turning observations into claims that survive scrutiny.
Data Wrangling & Analysis
Messy data in, analysis-ready tables out — repeatably.
Visualization & Communication
Analysis that humans who aren't the analyst can read.
Machine Learning Fundamentals
Classical ML with rigor on both the math and the engineering.
Deep Learning & Modern AI
Neural nets from first principles through LLM applications.
Data Engineering & MLOps
Shipping data and models to production, and keeping them healthy.
Problem Framing & Judgment
Business questions into data questions — and when not to use ML.
Capstones & Portfolio
End-to-end projects that prove the transformation.