The ModernEncyclopedia Est. 2026 · A living curriculum · Regularly updated
CSC-31 · Sciences · Fully written

Learn Computer Science with any AI

Computation to software

Computer science is the study of computation itself — what can be computed, how efficiently, and how to build systems that do it reliably. It is far less about any one programming language than about algorithms, abstraction, and the hard limits of what machines can do at all.

Learn it well and you gain a genuinely new way of thinking about problems: decompose, abstract, and reason about cost. Set your level below.

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§01

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CSC-31 · Computer Science
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§02

A map of Computer Science

From pure theory to real systems

Roughly from the abstract to the built.

  • Theory of computation & complexity — what can be computed, and how hard problems are.
  • Algorithms & data structures — the core craft of solving problems efficiently.
  • Programming languages & paradigms — the different ways of expressing computation.
  • Architecture & operating systems — how the machine actually runs your code.
  • Networks & distributed systems — computers cooperating across the world.
  • Databases, software engineering, HCI, graphics, security — the applied heart of the field.
§03

The canon

The people who defined computing

Real figures, real foundations.

  • Alan Turing (1936) — the Turing machine, and a precise account of what "computable" means.
  • Alonzo Church — the lambda calculus, and (with Turing) the limits of computation.
  • John von Neumann — the stored-program architecture nearly every computer still uses.
  • Claude Shannon (1948) — information theory, the mathematics of the whole digital age.
  • Grace Hopper — pioneered compilers, letting us program in something like human terms.
  • Edsger Dijkstra — algorithms and the discipline of getting programs provably right.
  • Donald Knuth — The Art of Computer Programming, the field's great reference.
  • Tim Berners-Lee — invented the World Wide Web.
§04

The live debates

The field's open questions

Real debates, one of them worth a million dollars.

  • P vs NP. Are problems whose answers are easy to check also easy to solve? The biggest open question in the field, unresolved and consequential.
  • The limits of computation. Some problems (like the halting problem) provably can't be solved by any program at all.
  • Science, maths, or engineering? Computer science is oddly all three, and argues about which.
  • Can we write correct software? Whether large systems can ever be truly bug-free, or just less broken.
  • The ethics of code. Bias, surveillance and power built into the systems that now run everything.
§05

Where to start

A route in

A route in — everything runs from the panel above.

  1. Run Orientation on "what an algorithm is" or "what computation is" — the conceptual core.
  2. Work a real algorithm problem with the Exam engine, and get the reasoning marked.
  3. Use Great Debates on P vs NP to see why it matters so much.
  4. Read a solid intro — and, crucially, build something. CS is learned in the doing.

Understand the idea before you memorise the syntax. Languages come and go; the ideas don't.