AP Computer Science Principles
One-page, printable cheatsheet
Table of Contents
⌨️ap computer science principles review
Big Idea 2 Overview: Data
Verified for the 2025 AP Computer Science Principles exam•Citation:MLA
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
2.2 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
2.3 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.
Key Terms to Review (15)
Abstraction: Abstraction is the process of simplifying complex systems by focusing on the essential details and hiding unnecessary complexities.
Analog Data: Analog data refers to continuous, real-world information that is represented by a range of values. It can take on any value within a given range and is often used to describe physical quantities like temperature or sound.
Binary Numbers: Binary numbers are a base-2 number system that uses only two digits, 0 and 1, to represent all values. Each digit in a binary number is called a bit.
Byte: A byte is a unit of digital information that consists of 8 bits. It can represent a single character or a small amount of numerical data.
Cleaning Data: Cleaning data is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It involves tasks like removing duplicate entries, handling missing values, and standardizing formats to ensure data quality.
Data Filtering: Data filtering is the process of selectively extracting or removing specific pieces of data from a larger dataset based on certain criteria or conditions. It allows you to focus on relevant information while excluding irrelevant or unwanted data.
Data Compression: Data compression is the process of reducing the size of data files while maintaining as much information as possible. This allows for more efficient storage and transmission of data.
Data Transformation: Data transformation refers to the process of converting data from one format or structure to another. It involves modifying, reorganizing, or manipulating data to make it more suitable for analysis or other purposes.
Digital Data: Digital data refers to information that is represented using discrete, binary values (0s and 1s). It is commonly used in computers and other digital devices because it can be easily stored, processed, and transmitted.
Hexadecimal: Hexadecimal is a number system that uses base-16 instead of base-10. It uses digits from 0 to 9 and letters from A to F to represent values from 0 to 15.
Lossless Compression Algorithms: Lossless compression algorithms are methods used to compress data files without losing any information. The compressed file can be fully restored to its original form without any loss of data.
Lossy Compression Algorithms: Lossy compression algorithms are methods used to reduce the size of a file by permanently removing some data. These algorithms achieve high compression rates but result in a loss of quality or detail in the compressed file.
Metadata: Metadata refers to descriptive information about data, such as its format, location, authorship, and creation date. It provides context and additional details that help organize and manage data effectively.
Overflow Error: An overflow error occurs when a computer program or system tries to store a value that is too large to be represented within the available memory or data type. This can lead to unexpected and incorrect results.
Rounding Error: Rounding error occurs when a number is approximated or rounded to a certain decimal place, resulting in a small discrepancy between the rounded value and the actual value.