Big Idea 2: Data

• Abstraction: The process of reducing complexity by focusing on only the ideas that a user needs to know. This is done mainly by hiding irrelevant details from the user.
• Analog Data: Data that is measured continuously. Its key characteristic is that the values change smoothly, rather than in discreet intervals.
• Bias: This can unintentionally be present in data. It occurs when the data collected does not represent all the possibilities in the pool of available options. Bias in data does not go away by collecting larger amounts of data to analyze.
• Binary Number System: A base two system of only using 0 and 1; used by computing devices to represent data digitally.
• Bit 


  Short for binary digit, 0 or 1.
• Byte: If you put 8 bits together, you get a byte.
• Classifying Data: Computers can help make meaning of large datasets by grouping data with common features and values.
• Cleaning Data: Includes removing corrupt data, removing or repairing incomplete data, and verifying ranges of dates.
• Filtering Data: Different subsets can be identified and extracted to help people make meaning of the data.
• Lossless Data Compression: Allows the original image to be restored. No data is lost, but the file size cannot be as compressed.
• Lossy Data Compression: Lose some data in the compression process. The original can never be restored, but the compression is greater than with lossless techniques.
• Metadata: Data that describes data and can help others find the data and use it more effectively.
• Patterns In Data: Computers are able to identify patterns in data that people are either unable to recognize or cannot process enough data to see the pattern. This process is known as "data mining." New discoveries and understandings are often made this way. When new or unexpected patterns emerge, the data has been transformed into information for people to begin to interpret. Computers make processing huge amounts of data possible so people can make sense of it.
• Scalability: The ability to increase the capacity of a resource without having to go to a completely new solution, and for that resource to continue to operate at acceptable levels when the increased capacity is being added.
• Sampling: Used to convert analog data to digital by taking samples of the analog data at intervals to create a digital representation of those values.