Think Like an Economist

A working understanding of the natural world — what science is, how matter and energy behave, how life persists and changes, and how Earth and the cosmos fit together. The trunk gives you a single coherent throughline across physics, chemistry, biology, and earth/space science; clusters dive into experimental design, mechanics, thermodynamics, electromagnetism, modern physics, genetics, ecology, and climate.

A working understanding of discrete mathematics — the math of countable, structural objects: sets, logic, counting, graphs, integers, algorithms. The trunk gives you the toolkit; clusters dive into combinatorics, graph algorithms, number theory, logic and computation, cryptography, and information theory.

A career-spanning roadmap from analytics-curious to mid-level Growth Engineer. The trunk gives you the complete throughline — measurement, experimentation, instrumentation, AARRR systems, monetization, GTM motions, and growth leadership — and clusters let you go deep on specific topics (Bayesian A/B testing, identity resolution, paid attribution, lifecycle email, dark patterns) when they matter.

How machine learning actually works — from learning-as-optimization through the classical toolkit to deep learning and foundation models. The depth step after AI Fundamentals: real mechanics, no code or graduate math required.

A comprehensive, 3-pillar curriculum that builds durable understanding of artificial intelligence and machine learning — from the mathematical foundations and classical algorithms, through modern deep learning architectures, to the societal implications of deploying AI in the real world. Emphasizes timeless principles over transient tools so knowledge remains valuable as the field evolves.

A working understanding of mathematics — the universal language scientists, engineers, and quants use to describe the world. The trunk gives you the throughline from how mathematicians think to how math models reality; clusters dive into proof, trig, calculus mechanics, advanced linear algebra, number theory and cryptography, probability, stochastic processes, and the foundational limits of math itself.