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United StatesComputer Science Principles

Big Idea 2: Data

4 dot points across 4 inquiry questions. Click any dot point for a focused answer with worked past exam questions where available.

How do computers represent all data using only bits, and what are the consequences of fixed-size representation?

Topic 2.1 Binary Numbers: computers represent all data with bits (binary digits); numbers, text, images and sound are encoded in binary, and fixed bit-widths cause overflow and rounding.
A focused answer to AP CSP Topic 2.1, covering bits and bytes, binary-to-decimal conversion, why all data is represented in binary, analog versus digital, fixed bit-width consequences (overflow and rounding errors), and abstraction in data representation.
10 min answer →

How can data be made smaller, and when does compression lose information?

Topic 2.2 Data Compression: compression reduces the number of bits used to store data; lossless compression preserves all information, while lossy compression discards some to save more space.
A focused answer to AP CSP Topic 2.2, covering why compression matters, lossless versus lossy compression, run-length encoding as a lossless example, the trade-offs of lossy compression for images and audio, and how to choose between them.
9 min answer →

How do we turn raw data into useful information, and what makes data meaningful?

Topic 2.3 Extracting Information from Data: information is extracted from data through processing, filtering, transforming and combining data sets, and correlation does not imply causation.
A focused answer to AP CSP Topic 2.3, covering the difference between data and information, processing data to find patterns and trends, filtering and transforming, metadata, combining data sets, and the limits of data including correlation versus causation.
9 min answer →

How do programs process large data sets efficiently to clean, search, filter and visualize data?

Topic 2.4 Using Programs with Data: programs process large data sets through cleaning, filtering, classifying and transforming data, often using lists and iteration to scale to large amounts of data.
A focused answer to AP CSP Topic 2.4, covering why programs are essential for large data sets, cleaning and classifying data, filtering with conditionals, using lists and iteration to process data at scale, and visualizing results, with worked pseudocode.
10 min answer →