HALF-YEARLY TECH
Call Kai
Learn
Practice Test
Spaced Repetition
Match
Flashcards
Knowt Play1/51
There's no tags or description
Looks like no tags are added yet.
Name | Mastery | Learn | Test | Matching | Spaced | Call with Kai |
|---|
No analytics yet
Send a link to your students to track their progress
52 Terms
Data
Raw, unprocessed facts with no context.
Example of data
65, 72, 91, 60
Information
Data that has been processed to give it meaning
Example of information
Average: 77
Knowledge/Insight
A conclusion drawn from information
Example of knowledge or insight
“Students need help with algebra”
Input
Data entered into system
Example of input stage
Typing scores into cells
Storage
Data saved for later use
Example of Storage stage
Excel file saved on drive
Processing
Transforming data into operations or formulas
Example of processing stage
Using formula =AVERAGE (B2:B30)
Output
The result presented to the user
Example of Output stage
A chart or printed report
Purpose of data analysis
Identify trends and patterns that are invisible in raw data.
Support better decisions based on evidence, not guesswork
Monitor performance over time, e.g. sales, student results, hospital outcomes
Predict future outcomes using historical data.
Main idea of data analysis
Data alone is useless. It must be processed into information for humans to act on it. The purpose of data analysis is to turn data into decisions.
Requirements
Guidelines that describe what a system or dataset must do and how well it must perform. Written before a system is built so developers know what to create.
Functional Requirement
Describes the features of a system, or what it must do. Includes specific actions and behaviours.
Non-Functional Requirement
Describes the quality of a system, or how well it must perform. Includes quality standards and contraints.
Example of Functional Requirement
“System must sort records by date”
Example of Functional Requirement
“The data must reject duplicate IDs”
Example of Functional Requirement
“Users must be able to filter by year group”
Validation
The automatic process performed by the system which checks that data follows the correct format. Validation cannot guarantee accuracg, only rule-compliance.
What validation can catch
Missing field, wrong data type, out of range
Example of validation
Rejecting ‘31/02/2002’ as in invalid date
Verification
Process in which human compares data against the source to make sure that it is actually correct and true. Verification is slower but confirms real-world truth.
What verification can catch
Right data type, wrong value
Example of verification
Double entry— entering data twice to confirm
Example of distinction between Verification and Validation
Validation can only check that the date is formatted correctly, but cannot discern whether the date of birth is actually correct. A person could enter the wrong but valid date— only verification catches this.