Data Formats
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Discrete Data: data that is counted and has a limited number of values -
Continuous Data: data that is measure and can have almost any numeric value -
Nominal Data: a type of qualitative data that is categorized without a set order- in other word, data without any sequence
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Ordinal Data: a type of qualitative data with a set order or scale -
Internal Data: data that lives within a company's own systems- usually more reliable and easier to collect
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External Data: data that lives and is generated outside of an organization -
Structured Data: data organized in a certain format such as rows and columns- spreadsheets, relational database
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Unstructured Data: data that is not organized in any easily identifiable manner- audio / video etc.
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Data Model: a model that is used for organizing data elements and how they relate to one another-
helps to keep data consistent and provide a map of how data is organized
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Concetual Data Modeling : high-level view of the data structure, such as how data interacts across an organization (doesn not contain any technical details)
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Logical Data Modeling : focuses on the technical details of a database such as relationships, attributes, and the entities (defining how individual records are uniquely idenitified in a database)
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Physical Data Modeling : depicts how a database operates (defining all entities and attributes used, table, column, data types for the database)
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Data Element: pieces of information, such as people's names, account numbers, and addresses -
Fairness Issue: the lack of structure makes unstructed data difficult to search, manage, and analyze. However, the recent advancements in AI and ML algorithms are beiggining to change this
