AP® Computer Science Principles review sheet from Aim for Five (aimforfive.com/csp/units/2)
Unit 2
17–22% of examData
Everything a computer stores, from numbers and text to photos and songs, comes down to bits. In this unit you'll convert between binary and decimal by hand, see how files get compressed, and learn how people and programs pull useful information out of large data sets, along with the limits and biases of that data.
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Flashcards (29)Practice questions (60)Computer Science Principles must-know sheetFree-response questions on this unit
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- Written Response 2: Algorithms, errors and testing, and abstractionImage row compression3 points · about 45 minutes
- Written Response 2: Algorithms, errors and testing, and abstractionBinary converter3 points · about 45 minutes
- Written Response 2: Algorithms, errors and testing, and abstractionAir-quality sensor3 points · about 45 minutes
- Written Response 2: Algorithms, errors and testing, and abstractionHome internet survey3 points · about 45 minutes
Big ideas
- All digital data is stored as bits, and the same bits can mean different things
- Each extra bit doubles the number of values you can represent
- Lossless compression can be undone exactly; lossy compression trades detail for size
- Cleaning, filtering and visualizing data turns raw data into information
- A correlation in data doesn't prove that one thing causes another
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Topics
A bit is a single 0 or 1 and a byte is 8 bits, and the same bits can stand for a number, a letter, a color or a sound depending on how they're read. You'll convert between binary (base 2) and decimal (base 10) using place values (1, 2, 4, 8, 16 and so on), see how a fixed number of bits leads to overflow and round-off errors, and see how sampling turns smooth analog data like sound into digital data.
Key terms
- bit
- byte
- binary (base 2)
- overflow error
- round-off error
- analog data and sampling
A few quick questions on this topic, with the answers explained.
Compression reduces the number of bits needed to store or send data, and how much it saves depends on how much repetition the data has and on the method used. Lossless compression lets you rebuild the original exactly, while lossy compression throws away some detail for a much smaller file, so you pick lossless when you need a perfect copy and lossy when smaller size matters more.
Key terms
- data compression
- lossless compression
- lossy compression
- redundancy
- trade-off
A few quick questions on this topic, with the answers explained.
Large data sets can reveal trends and patterns, but data often needs cleaning first so it's complete and consistent, and metadata (data about data, like the date and place a photo was taken) helps you find and organize it. Data has limits: it can be incomplete or biased, and a correlation between two things doesn't prove that one causes the other.
Key terms
- information
- metadata
- data cleaning
- correlation vs. causation
- bias in data
- scalability
A few quick questions on this topic, with the answers explained.
Programs let you work with far more data than you could by hand: they can filter it (keep only what matches a condition), transform it (change every item, like converting units), combine it and turn it into tables and charts. Visualizing data makes patterns easier to spot and helps you test ideas about it.
Key terms
- filtering data
- transforming data
- combining data
- data visualization
- patterns and trends
A few quick questions on this topic, with the answers explained.