Four tracks, short lessons, real questions. Pick up where you left off or start something new.
The math that powers algorithms, models, and data analysis — taught with code, not just chalk.
Build precise arguments with propositions, truth tables, and logical operators — the foundation for formal proofs, program correctness, and SQL query logic.
From coin flips to conditional probability — learn the counting rules and distributions you need to understand everything from A/B testing to Bayesian inference.
Solve equations and manipulate variables the way a compiler does — with step-by-step exercises on linear systems, polynomials, and the algebra behind algorithms.
Vectors, transformations, and coordinate systems — the math behind computer graphics, game engines, and spatial data analysis.
Derivatives, gradients, and integrals explained with real code — understand exactly how gradient descent trains a neural network and how rates of change drive optimization.
Primes, modular arithmetic, and the math behind RSA and cryptographic hashing — the integers that secure every message you send online.
The physical principles behind the hardware and systems you build on every day.
From switches to logic gates — build the combinational and sequential circuits that every CPU and embedded system is made of, with interactive simulations.
Newton's laws, energy, and momentum — the physics that drives robotics, game physics engines, and simulations, with worked examples in code.
Oscillations, interference, and Fourier analysis — the foundations of audio processing, image filtering, and wireless communication.
Heat engines, entropy, and energy budgets — understand the physical limits of computation and why your data center needs cooling.
Algorithms, data structures, and the core ideas that make software fast, correct, and scalable.
From linear search to merge sort — learn the algorithms every developer should know, with interactive code exercises and Big-O analysis.
Arrays, linked lists, hash maps, trees, and graphs — learn which structure to reach for and why the right choice can make your code 1000x faster.
Base cases, call stacks, and the divide-and-conquer mindset — master recursion through tree traversal, backtracking, and dynamic programming patterns.
BFS, DFS, shortest paths, and minimum spanning trees — the algorithms behind routing, social networks, recommendation engines, and dependency resolution.
Statistics, machine learning, and the skills to turn raw data into models that predict and explain.
Load, clean, summarize, and visualize real datasets in Python — learn the exploratory data analysis workflow that every data scientist and analyst uses on day one.
Forward passes, backpropagation, and gradient descent explained from scratch — build a neural network in NumPy and see exactly what happens during training.
Distributions, sampling, hypothesis testing, and confidence intervals — the inferential toolkit you need to run experiments and read research papers critically.
Linear, logistic, and polynomial regression with real datasets — learn to fit models, interpret coefficients, and know when your model is overfitting.
Atomic bonds, crystal structures, and reaction kinetics — the science behind semiconductors, batteries, and the materials your devices are built from.
Kepler's laws, orbital dynamics, and the scale of the universe — the physics behind satellites, GPS, and space missions.
Symmetric encryption, public-key crypto, and hashing — learn how TLS, digital signatures, and password storage actually work, with hands-on implementations.
Cache hierarchies, pointers, and memory layout — understand why memory access patterns dominate performance and how to write cache-friendly code.
Maximum likelihood, Naive Bayes, and generative models — the probabilistic foundations that power everything from spam filters to modern LLMs.
Prior beliefs, likelihoods, and posterior updates — learn the Bayesian reasoning behind A/B testing, recommendation systems, and adaptive experimentation.