Advanced Computer Science Vocabulary

Understanding Advanced Computer Science Vocabulary

This deck covers the layers that make modern computing work. At the lowest layer, binary signals represent two states, often called zero and one. Logic gates combine these states according to fixed rules.

Truth tables are useful because they show every possible case, not just a few examples that seem to work. This habit of checking cases matters throughout computer science. A CPU processes instructions using parts such as registers and the ALU.

Registers hold small pieces of immediate data. The ALU performs calculations and comparisons.

A pipeline overlaps stages of instruction work to improve speed, though dependencies can create delays. These terms help you see that software eventually becomes precise operations in hardware.

Another group of words explains how information moves across networks and stays protected. A domain name is easier for people to use than an IP address, so DNS translates between the human friendly name and the network location. A recursive resolver asks other servers for help.

An authoritative name server provides the official answer for a domain. TTL controls how long a stored answer can be reused before it should be checked again. This process shows why a website name may work even though computers use numerical addresses.

Encryption protects data while it travels or rests in storage. Plaintext is the meaningful message before protection. Ciphertext is the transformed result.

The key is the controlled secret or credential that makes protection possible. Symmetric encryption uses a shared key, while public key encryption separates the key used to protect data from the key used to recover it.

The programming terms describe ways to organize code and solve problems clearly. Classes provide a plan for objects, and constructors set up each new object with a valid starting state. Arrays and ArrayLists store collections, while traversal means visiting items in a deliberate order.

Abstraction hides unnecessary detail so a programmer can focus on the important behavior. Algorithms are repeatable procedures, and protocols are shared rules that allow separate systems to cooperate. Recursion solves a problem by reducing it to smaller versions of itself.

The base case stops the process. The recursive case creates the next smaller task. The call stack records unfinished work, which explains why missing a base case can cause a program to fail.

Trees model branching structures with nodes and connections. Search methods use ideas such as edge cost, open sets, closed sets, and heuristics to choose promising paths without losing track of work already done.

Study these terms as connected systems rather than isolated definitions. Draw a small circuit and fill in its truth table. Trace what happens after entering a domain name into a browser, including the resolver, stored answers, and authoritative server.

Write a short recursive function on paper, then list each call as it enters and leaves the call stack. Build a tiny tree and mark which nodes belong in an open set or closed set during a search. For every term, practice stating its role, what it interacts with, and what can go wrong when it is used incorrectly.

Compare similar ideas directly, such as an algorithm with a heuristic, or symmetric encryption with public key encryption. Advanced computer science depends on careful reasoning, because small details in data, rules, and order of operations can change the result completely.