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⌨️AP Computer Science Principles Unit 2 Review

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Big Idea 2 Overview: Data

Big Idea 2 Overview: Data

Written by the Fiveable Content Team • Last updated August 2025
Verified for the 2026 exam
Verified for the 2026 examWritten by the Fiveable Content Team • Last updated August 2025
⌨️AP Computer Science Principles
Unit & Topic Study Guides

AP Computer Science Principles Exam

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The One Thing You Need to Know About this Big Idea:

This unit is all about how computers represent data, and how they can store and process ever-increasing quantities of it.

2.1 Binary Numbers

Learning Objective: Explain how data can be represented using bits.

Learning Objective: Explain the consequences of using bits to represent data.

**Learning Objective: For binary numbers, calculate the binary (base 2) equivalent of a positive integer (base 10) and vice versa; compare and order binary numbers. **

Key Ideas

  • computers store data in bits
  • computers use machine code, which operate on the binary system (digits are either 0 or 1)
  • any decimal number can be expressed as a binary number and vice versa
  • the same sequence of bits can represent different types of data depending on the context
  • abstraction hides irrelevant details from users
  • analog / digital representation of data
  • overflow and rounding errors result from using bits to represent data

Vocabulary

  • data
  • bits
  • number base
  • machine code
  • binary system
  • byte
  • hexadecimal
  • abstraction
  • analog data
  • digital data
  • sampling technique
  • overflow error
  • rounding error

Resources

🔗 2.1: Binary Numbers

2.2 Data Compression

Learning Objective: Compare data compression algorithms to determine which is best in a particular context.

Key Ideas

  • data compression can reduce the number of bits when transmitting or storing data
  • fewer bits doesn't necessarily mean less information
  • lossless data compression is preferred if your main concern is the quality of your file or if you need to be able to reconstruct your original file
  • lossy data compression is preferred if your main concern is minimizing how big your file is or how long it'll take to send or receive it

Vocabulary

  • lossless compression algorithms
  • lossy compression algorithms

Resources

🔗 2.2: Data Compression

2.3 Extracting Information from Data

Learning Objective: Describe what information can be extracted from data.

**Learning Objective: Describe what information can be extracted from metadata. **

Learning Objective: Identify the challenges associated with processing data.

Key Ideas

  • by examining data closely, we can identify trends, make connections and address problems
  • metadata allow data to be structured and organized
  • changes and deletions to metadata don't change the primary data
  • cleaning data is a process that makes the data uniform without changing their meaning
  • problems of bias are often created by the type or source of data being collected; just collecting more data won't make this problem go away

Vocabulary

  • information
  • metadata
  • cleaning data

Resources

🔗 2.3: Extracting Information from Data

2.4 Using Programs with Data

Learning Objective: Extract information from data using a program.

Learning Objective: Explain how programs can be used to gain insight and knowledge from data.

Key Ideas

  • data processing programs can help you acquire information from data
  • data filtering systems help with finding information and recognizing patterns
  • manipulating data by combining, clustering or classifying it can bring out new information and patterns previously unseen in the raw data, making it a helpful tool for data analysis

Vocabulary

  • data transformation
  • data filtering

Resources

🔗 2.4: Using Programs with Data

Exam Weighing

  • 17-22% of the AP Exam
  • Practically, this translates to about 20 questions on the test.

Vocabulary

The following words are mentioned explicitly in the College Board Course and Exam Description for this topic.

Term

Definition

abstraction

The process of reducing complexity by focusing on main ideas and hiding irrelevant details to allow focus on the essential concept.

analog data

Data that have values changing smoothly and continuously over time, such as pitch, volume, or position.

binary

A base-2 number system that uses only the digits 0 and 1 to represent data.

bit

Shorthand for binary digit; the smallest unit of data in computing, represented as either 0 or 1.

byte

A unit of digital data consisting of 8 bits.

constants

Fixed data values that do not change during program execution.

data

Information represented in a form that can be processed by a program, such as numbers, text, or records.

decimal

A base-10 number system that uses the digits 0-9 to represent data.

fixed number of bits

A predetermined, limited quantity of bits allocated to represent a data value in programming languages, which constrains the range of representable values.

integers

Whole numbers (positive, negative, or zero) that are represented in programming languages using a fixed number of bits.

number bases

Different systems for representing numerical values, such as binary (base 2) and decimal (base 10).

overflow

An error that occurs when a computed value exceeds the maximum value that can be represented by a fixed number of bits.

place value

The numeric value assigned to a digit's position in a number, determined by the base raised to the power of the position.

real numbers

Numbers that include both integers and decimal values, represented in programming languages with a fixed number of bits as approximations.

roundoff error

An error that occurs when real numbers are approximated in computer storage due to limitations in the fixed number of bits used to represent them.

sampling

A technique for approximating analog data digitally by measuring values of an analog signal at regular intervals.

variable

A named container in a program that stores a value which can be changed through assignment.

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