College Computer Science Vocabulary

Understanding College Computer Science Vocabulary

This deck covers several connected areas of modern computer science. It moves from designing efficient algorithms to building software services, managing code, processing very large datasets, and training machine learning models. These topics meet in real systems.

A recommendation service, for example, may use an algorithm to rank results, run on cloud computers, obtain data through an API, store code in a repository, and use a trained model to make predictions. The vocabulary matters because college computer science is not only about writing code that works. It is about explaining why code works, measuring its cost, collaborating safely, and choosing tools that fit the problem.

Algorithm terms give you a way to reason before you program. Input size is important because an approach that feels fast for ten items can fail for ten million. Time complexity describes how running time grows.

Space complexity describes how memory use grows. Big O notation gives a shared language for discussing this growth without focusing on one particular computer. Algorithm design paradigms are recurring plans for solving problems.

Dynamic programming depends on optimal substructure and overlapping subproblems. Greedy methods depend on the greedy-choice property. Recurrence relations can describe how a recursive algorithm grows.

Pruning avoids work that cannot lead to a useful answer. Sorting vocabulary adds practical details. A stable sort preserves the order of tied items.

An in-place algorithm uses little extra memory. Comparison sorts learn order by comparing values.

Machine learning vocabulary explains how programs can learn patterns from examples. A neuron combines incoming signals. Weights determine which signals matter more.

A bias shifts when the neuron responds. An activation function lets layers represent patterns that are not simple straight-line relationships. During training, backpropagation traces error backward through the network so the model can adjust its weights.

This process needs data, computing power, and careful measurement. Big data creates challenges in storage, speed, and reliability. Data science connects computing with statistics and knowledge of the real situation behind the data.

Data visualization helps people notice trends, errors, and misleading conclusions. Distributed computing divides storage or work across many machines when one machine is not enough.

Software and systems terms describe how these ideas become usable products. Cloud computing provides computing resources over a network. Data centers contain the physical machines behind many cloud services.

Virtualization allows one physical machine to act like several separate machines. Scalability means a system can handle growing demand without collapsing. SaaS delivers software through the web.

APIs let programs communicate through defined rules. An endpoint identifies a service location, while an HTTP method expresses the intended action. JSON is a common format for structured data.

Authentication checks identity before access is allowed. Git terms support teamwork. Make commits with clear purposes, use branches for separate work, merge only after checking changes, and use remotes to share work.

Study this deck by grouping terms into algorithms, learning, data, systems, web services, and collaboration. For each term, explain a small example aloud and connect it to one neighboring idea. That practice builds usable understanding rather than isolated memorization.