High School Computer Science Vocabulary

Understanding High School Computer Science Vocabulary

High school computer science vocabulary covers the ideas behind programs, data, networks, and modern computing systems. The terms are not separate facts to memorize. They describe layers of one larger system.

At the lowest layer, bits represent information. Base systems, place value, binary, octal, and hexadecimal show different ways computers write the same values. These ideas matter when reading memory addresses, colors, permissions, or low level data.

A packet carries bits across a network. An IP address helps the packet reach the right device. DNS turns human friendly site names into addresses that computers can use.

Programs sit on top of these foundations. An algorithm is a precise method for solving a problem. Input size describes how much data that method must handle.

Time complexity helps predict how the running time changes as the input grows. Space complexity does the same for memory use. Big O notation gives a practical way to compare growth rates without focusing on one particular computer.

This matters because a program that works on ten items may fail badly on ten million. Big data and distributed computing appear when one machine cannot store or process everything efficiently. Cloud computing, data centers, virtualization, scalability, and SaaS describe ways computing resources are organized for many users.

Networks make sharing possible, but they create risks. Routers choose paths for packets, while protocols set rules for communication. TLS and encryption protect data while it travels.

Cybersecurity is the broader work of protecting systems, accounts, and information. Malware harms or takes control of devices. Phishing tries to trick people into giving away passwords or private details.

A firewall filters network traffic according to rules. These words matter together because strong security is not one tool. It depends on careful software, safe network settings, encrypted communication, and alert human choices.

Machine learning and data science use computing to find patterns in data. Data visualization helps people see patterns before they make claims. In a neural network, neurons combine information using weights and a bias.

An activation function helps the network model more complicated patterns. During training, backpropagation helps adjust the weights after the system measures its errors. Learn these terms as parts of a process rather than as isolated definitions.

Practice by tracing a small example from input to output. For software teamwork, use a repository to hold project history. Make a commit for a meaningful change.

Use a branch to work safely, then merge the finished work. A remote lets a team share the repository. Group terms by system, trace how information moves, and explain each connection in your own words.