Data & Representation: every key term you need (+ practice quiz)

More AP Computer Science Principles guides

• Computing Systems & Networks
• Algorithms & Programming
• Programming & Modeling
• Impact of AI & Computing

Algorithm

Step-by-step procedure for solving a problem; can be represented in pseudocode or flowcharts.

Pseudocode

Simplified code-like language describing algorithms without specific programming syntax.

Sequence

Algorithm structure where steps execute in order from top to bottom.

Selection

Algorithm structure using conditionals (if/else) to choose different paths based on conditions.

Iteration

Algorithm structure using loops to repeat steps multiple times (for, while).

Conditional

Decision statement (if/else) that executes code based on whether condition is true or false.

Loop

Repetition structure that executes code multiple times; types include for loops and while loops.

Variable

Named container storing a data value that can change during program execution.

Data Type

Classification of data: integer, string, boolean, floating-point, etc.

String

Sequence of characters (letters, numbers, symbols) treated as text data.

List/Array

Ordered collection of elements accessible by index; stores multiple values in one variable.

Function

Reusable block of code performing a specific task; takes input (parameters) and may return output.

Parameter

Input variable in a function definition; specifies what data the function accepts.

Return Value

Output value that a function sends back to the calling code.

Scope

Region of code where a variable is accessible; local scope (within function) vs. global scope.

Debugging

Process of finding and fixing errors (bugs) in code.

Logic Error

Code runs without crashing but produces incorrect results due to flawed algorithm.

Syntax Error

Mistake in code structure preventing the program from running.

Flowchart

Visual diagram using symbols to represent algorithm steps and decision points.

Abstraction

Process of hiding complex details and showing only essential features.

Overflow Error

Result when a computed value exceeds the maximum that a fixed number of bits can store, such as 255 for 8 unsigned bits.

Round-off Error

Small inaccuracy that occurs because real numbers like 1/3 or 0.1 cannot be represented exactly in a fixed number of binary bits.

Lossless Compression

Encoding that reduces file size while allowing the exact original data to be reconstructed, e.g. ZIP or PNG.

Lossy Compression

Encoding that permanently discards some data to achieve much smaller files, e.g. JPEG or MP3; the original cannot be fully restored.

Metadata

Data that describes other data, such as an image's dimensions, capture date, or a file's author; it does not change the primary content.

Abstraction (Data)

Representing complex data with a simpler model, such as using a hex color code instead of three separate 8-bit values.

Analog Data

Continuous values with infinitely many possibilities, such as sound waves; must be sampled to be stored digitally.

Sampling

Measuring an analog signal at regular intervals to produce discrete digital values; higher rates approximate the original more closely.

Bit Width and Range

n bits can represent 2^n distinct values; unsigned integers range from 0 to 2^n − 1.

Hexadecimal

Base-16 numeral system (0-9, A-F); each hex digit maps to exactly 4 bits, so two hex digits equal one byte.

Data Cleaning

Fixing or removing incomplete, duplicate, or inconsistent records before analysis so results are not distorted.

Correlation vs. Causation

Two variables moving together does not prove one causes the other; a hidden third factor may drive both.

Bias in Data

Systematic error introduced by how data is collected or sampled, which can lead to unfair or wrong conclusions.

Data Visualization

Charts, graphs, and maps that reveal patterns or trends in data that are hard to see in a raw table.

Filtering and Aggregation

Filtering selects rows meeting a condition; aggregation combines many values into a summary such as a sum or average.

Scalability of Data Processing

Large data sets may require distributed or parallel processing because a single computer cannot store or scan them fast enough.