Data — the one-page version

Every key term and every exam tip from the 4 topics in this big idea. Print it, fold it, read it on the bus.

2.1Binary Numbers

Bit: a single binary digit, 0 or 1
Byte: 8 bits
Binary (base 2): a number system with two digits, where each place is a power of 2
Decimal (base 10): the everyday number system, where each place is a power of 10
Overflow error: a value exceeds what its fixed number of bits can hold
Sampling: measuring an analog signal, like sound, at regular intervals to store it digitally

Tip: Write the place values above the bits (…32 16 8 4 2 1) before converting — it removes almost every arithmetic slip. For "how many values" questions, the answer is always a power of 2 equal to 2 raised to the number of bits, not the number of bits itself.

2.2Data Compression

Data compression: reducing the number of bits needed to represent data
Lossless compression: compression from which the original data can be completely reconstructed
Lossy compression: compression that permanently discards some data for greater size reduction

Tip: If the stem says the file must be "exactly" or "perfectly" restored, or involves text/code/financial records, answer lossless. If it emphasizes minimizing size or transmission time and the data is an image, video, or audio where small quality loss is acceptable, answer lossy. Also remember: compression is about size and speed — not security.

2.3Extracting Information from Data

Information: facts and patterns extracted from data through processing and interpretation
Metadata: data that describes other data, such as a file's creation date or location
Correlation: two variables tending to change together, which does not by itself prove one causes the other
Filtering: selecting only the records that meet a condition
Cleaning data: fixing or removing incomplete, invalid, or inconsistent records

Tip: When a question presents a dataset and a conclusion, ask: does the data actually support that, or could something else explain it? Options that claim causation from a correlation are wrong. Options that say more data or a controlled comparison is needed are usually right.

2.4Using Programs with Data

Data cleaning: making data uniform and valid without changing its meaning
Data transformation: reformatting or deriving values from data to enable analysis
Visualization: representing data graphically to make patterns easier to interpret

Tip: A question about "making data uniform so a program can process it" wants the answer cleaning. Cleaning never changes what the data means — if an option describes altering values to get a desired result, that's data manipulation, not cleaning, and it's wrong.