Computer Science
AP Computer Science Principles
Big Idea 2: Data
number systems
binary
numbers
abstractions
binary number conversions
conversions
converting
decimal numbers
letters
colors
computer
color chart
Machine Instructions
Overflow Errors
fixed number
Rounding or Round-off Errors
Analog Data
data
data compression
lossless
lossy
cleaning
filtering
classifying
bias
patterns
Correlations
Scalability
Metadata
sharing
communicating information
9th
lowest level
Computers read machine code, which at the ________, is made up of 0s and 1s.
Current
________ "and "no ________ "are easy conditions to detect.
similar features
Classifying: Grouping data with ________ and values helps computers make sense of large datasets.
attendees
Data collected from all types of events- such as visits, searches, inquiries, orders, returns, temperatures, scores, ________, acres planted, acres harvested, fish, birds, photos, videos, and audio files- are considered to be raw data.
Software tools
________ such as spreadsheets and databases can be used to filter, organize, and search the data.
software program
The ________ takes in the binary value and interprets it as a color, text value, or number, based on what the program is expecting.
actual data
Changing, adding, or deleting metadata does not impact or change the ________ in any way.
Binary
is the number system used in computer science.
Abstraction
is a concept that is a little hard for many students to grasp.
Letters
In addition to the numbers we just reviewed, binary numbers can also represent ______ for text fields.
colors
Computer monitors work the same way and add differing amounts of red, green, and blue to create the colors that are displayed.
Note
Use leading 0s on the left to make a byte (8 bits)
Example 1
Convert 21 to binary
Answer
2110 = 000101012
Example 1
Convert 00011011 to decimal
If we simply had a binary number
00101001, we would not know what it represented
Data Compression
Lossless and Lossy
Cleaning
Computers "clean" data
Filtering
Computers filter data easily
Classifying
Grouping data with similar features and values helps computers make sense of large datasets
Bias
This can unintentionally be present in data
Patterns
The data analysis starts with a hypothesis or question to check
Machine Instructions
It could be a number, text, color, instruction, or other representation.
Overflow errors
occur in computers when the integer to be represented needs more bits than the programming language can represent.
fixed number
of bits are assigned to hold integers in many programming languages.
Rounding errors
occur because of the way numbers with decimal points are stored in the computer.
Analog data
is a continuous stream of data values.
Lossless compression
techniques allow the original image to be restored.
Lossy compression
techniques lose some data in the compression process.
Computers
enable us to process data to turn it into information for decision making and research.
Data
collected from all types of events—such as visits, searches, inquiries, orders, returns, temperatures, scores, attendees, acres planted, acres harvested, fish, birds, photos, videos, and audio files—are considered to be raw data.
Cleaning
Computers "clean" data.
Data cleaning
can also change "dr." to "Drive" for consistency.
Filtering
Computers filter data easily.
Classifying
Grouping data with similar features and values helps computers make sense of large datasets.
Groupings
may use one or more criteria.
Bias
This can unintentionally be present in data.
Patterns
The data analysis starts with a hypothesis or question to check.
data mining
Computers are able to identify patterns in data that people are either unable to recognize or cannot process enough data to see the pattern.
correlation
may not mean one thing caused the other.
Scalability
is 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.
Metadata
is data that describes data and can help others find the data and use it more effectively.
Charts, tables, and other graphics
help summarize data visually.
Search tools and filtering systems
are needed to help analyze the data and recognize patterns.