Curriculum

Software engineering school where you learn by doing

Master industry-grade software engineering with rigorous modules and a thesis project that keeps you building from day one.

Pedagogy

We believe learning should be motivated by the “why”. Unlike college CS programs where students are taught theory in isolation from real world applications, students at Vector School are tasked to build a large-scale, long-term project from scratch and will rigorously learn the necessary concepts along the way. This makes sure that all Vector School graduates have core CS competencies as well as the experience to have built a production-grade system end-to-end.

Students use the same developer tools the industry relies on every day—AWS, Vercel, OpenAI APIs, and more—to build production-grade systems.

Project-basedlearningLecture-basedlearningProject(i.e. Objective)Logical Components(i.e. Topics)Axioms(i.e. Facts & Skills)

Solving the 2 sigma problem

Bloom's famous Two Sigma study showed that one-to-one tutoring lifts average student performance by two standard deviations (≈98th percentile) over conventional classroom instruction. In comparison, purely mastery-based learning can lift student performance by one standard deviation on average.

Benjamin Bloom posed the 2 sigma problem because one-to-one tutoring was impossible to scale in 1984. With today’s AI technologies, we’re able to provide high-quality tutoring increasingly more at scale.

Relative performance lift vs. conventional classroom instruction
Tutoring
+2σ
Mastery-based
+1σ
Classroom
Baseline

Outcome to expect

Graduation requires two milestones: mastery of every module and completion of the long-term project. Though the entire program is project-based (i.e. problem first), students will need to pass rigorous assessments in order to demonstrate competency in each module.

As a result, students will graduate with high competency in every module, along with an extensive hands-on experience (i.e. green carpet in your GitHub).

Topics covered in the program

Program Design & Python

  • Problem solving, testing, and style
  • Core Python & standard library
  • Functional techniques & iterators/generators
  • Complexity & profiling basics

Fundamentals of Frontend Programs

  • Semantic HTML, accessibility, responsive design
  • CSS layout: Flexbox, Grid, core rendering
  • Modern JavaScript: scope, closures, modules, event loop
  • TypeScript: types, interfaces, generics, bundlers

Data Structures & Algorithms

  • Arrays, lists, stacks, queues, hashing
  • Trees, heaps, priority queues
  • Sorting & searching
  • Asymptotic analysis & amortization

Introduction to Browser

  • Classes, prototypes, UI components
  • DOM manipulation, event propagation, delegation
  • Promises/async-await, fetch(), CORS
  • AbortController, web workers basics

Computer Architecture & Systems Fundamentals

  • ISA, assembly, and memory hierarchy
  • C programming & pointers
  • Caches, pipelines, performance
  • Concurrency primitives (atomics, locks)

Databases & Data Modeling

  • Relational design & normalization
  • PostgreSQL architecture, extensions, and performance tuning
  • Transactions & isolation levels
  • NoSQL models and trade-offs

Operating Systems

  • Processes, threads, scheduling
  • Virtual memory & filesystems
  • Synchronization & deadlocks
  • I/O, networking stack in the OS

Networking & Internet Systems

  • TCP/IP, routing, congestion control
  • HTTP/2–HTTP/3, TLS, QUIC
  • DNS, CDNs, load balancers
  • Performance measurement & tracing

Security

  • Threat modeling & secure-by-design principles
  • Applied cryptography: encryption, signatures, key management
  • Application security testing, code review, and tooling
  • Detection engineering, incident response, and compliance basics

Web Systems Engineering

  • Modern frontend architecture & state management
  • Backend services & frameworks
  • Real-time delivery (WebSockets/EventSource)
  • Performance, accessibility, and end-to-end testing

System Design

  • Scoping requirements, SLIs/SLOs, and capacity planning
  • Service decomposition, APIs, and data contracts
  • Consistency, availability, and partitioning trade-offs
  • Resilience patterns: caching, queues, retries, chaos testing

Frontier of Applied AI

  • Modern foudation models and model architectures
  • Retrieval-augmented generation
  • Agent orchestration, tool use, and safety guardrails
  • Open source models
  • Observability, tracing, and evals

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