UC Berkeley | Data Science
Graduated December 2025. Data Engineering, Blockchain for Developers, and Intro to Poker. Game theory meets quantitative analysis.
The Problem
Data science curriculum focuses heavily on analysis but less on engineering. I wanted exposure to production-grade systems work alongside the theoretical foundations.
Relevant Coursework
- Data Engineering: Large-scale data pipelines with Spark, dbt, and Airflow. Built ETL pipelines processing millions of records.
- Blockchain for Developers: Solidity smart contracts, DeFi protocols, and EVM internals. Built a prediction market contract as the final project.
- Intro to Poker: Game theory, expected value calculations, and decision making under uncertainty. Connected directly to my interest in prediction markets.
What I Got Out of It
The poker course was surprisingly relevant to quantitative work. EV calculations, range thinking, and Bayesian updating transfer directly to sports modeling and prediction markets.
Data Engineering shifted how I think about ML systems. Feature stores, data quality monitoring, and pipeline orchestration are as important as the models themselves.
Key Features
- -Data Engineering - large-scale data pipelines
- -Blockchain for Developers - Solidity smart contracts
- -Intro to Poker - game theory and decision making under uncertainty