Big Idea 2 Overview: Data

3 min readjune 18, 2024

Milo Chang

Milo Chang

Minna Chow

Minna Chow

Milo Chang

Milo Chang

Minna Chow

Minna Chow


AP Computer Science Principles ⌨️

80 resources
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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

2.2 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

2.4 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

Exam Weighing

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