Binary Representation, Compression, and Data Analysis

Vocabulary flashcards defining fundamental computer science concepts in data representation, compression methods, metadata properties, and big data analysis limitations.

Bit

The smallest unit of data; a single binary digit, either 0 or 1.

Byte

8 bits; the standard unit for measuring file and memory sizes.

ASCII

A text encoding scheme that covers basic Latin characters using 7 or 8 bits per character.

Unicode

A text encoding scheme that uses more bits to represent characters from virtually every written language.

Sampling

Measuring the amplitude of a sound wave at regular intervals to convert it to digital data.

Overflow Error

Occurs when a calculation produces a number too large to be stored in the available bits, exceeding the maximum value representable (such as exceeding $2^8 - 1$ in an 8-bit system).

Roundoff Error

Occurs when a number cannot be represented exactly in binary and is stored as the closest possible approximation.

Lossless Compression

Reduces file size by removing redundancy without discarding any data, allowing perfect reconstruction of the original data.

Lossy Compression

Reduces file size by permanently discarding data the human senses struggle to perceive, meaning the original data cannot be fully recovered.

Run-Length Encoding

A lossless compression technique that replaces repeated values with a count and a single value.

Metadata

Data that describes other data; properties of a file rather than the file's content itself.

Resolution

The number of pixels in an image; higher resolution means more detail and a larger file size.

Correlation

A statistical relationship where two variables tend to change together, which does not prove that one causes the other.

Causation

A relationship where one variable directly causes changes in another; a stronger claim than correlation.

Bias

A systematic error in data collection or analysis that skews results toward a particular outcome.

Collection Bias

A type of bias occurring when the data collection method excludes certain groups.

Incomplete Data

A dataset limitation occurring when important variables are missing from the dataset.

Algorithmic Bias

A type of bias occurring when a model trained on biased historical data reproduces and amplifies those biases.

Confirmation Bias

A type of bias occurring when analysts search only for evidence that confirms existing beliefs.