Data classification

data-types

As dicussed before, data can be split into qualitative and quantitative.

The quantitative can be classfied in 2 different types: discrete and continuous.

Discrete data vs Continuous data

Discrete data: It can be counted and have a limited number of values.
Continuous data: It can be measured using a timer, and its value can be shown as a decimal with several places.

Nominal data vs Ordinal data

Nominal data: It is a type of qualitative data that's categorized without a set order.
Ordinal data: It's a a type of qualitative data with a set order or scale.

Internal data vs External data

The internal data that lives within a company's own systems.

Structured data vs Unstructured data

Structured data: It is data that's organized in a certain format, such as rows and columns. It's easier to store, search and analyze.
Unstructured data: This is data that is not organized in any easily identifiable manner.

Data model

Data modeling is the process of creating diagrams that visually represent how data is organized and structured. These visual representations are called data models.

Data model that is used for organizing data elements and how they relate to one another.

Data models help to keep data consistent and provide a map of how data is organized.

Data element

They're pieces of information, such as people's names, account numbers, and addresses

Levels of data modeling

Conceptual data modeling gives a high-level view of the data structure, such as how data interacts across an organization.
Logical data modeling focuses on the technical details of a database such as relationships, attributes, and entities.
Physical data modeling depicts how a database operates. A physical data model defines all entities and attributes used.

Data-modeling techniques

Entity Relationship Diagram (ERD)

ERDs are a visual way to understand the relationship between entities in the data model.
Unified Modeling Language (UML)

UML diagrams are very detailed diagrams that describe the structure of a system by showing the system's entities, attributes, operations, and their relationships.

Data types

A data type is a specific kind of data attribute that tells what kind of value the data is.

Data types in the spreadsheet

Number
String
Boolean

Data types in the database

Records: rows
Fields: columns

Wide data vs Long data

Wide data

Every data subject has a single row with multiple columns to hold the values of various attributes of the subject.

Wide data lets you easily identify and quickly compare different columns.

It's used for:

Creating tables and charts with a few variables about each subject
Comparing straightforward line graphs

Long data

Long data is data in which each row is one time point per subject, so each subject will have data in multiple rows.

It's used for:

Storing a lot of variables about each subject. For example, 60 years worth of interest rates for each bank
Performing advanced statistical analysis or graphing

Data transformation

Data transformation is the process of changing the data’s format, structure.

Data transformation usually involves:

Adding, copying, or replicating data
Deleting fields or records
Standardizing the names of variables
Renaming, moving, or combining columns in a database
Joining one set of data with another
Saving a file in a different format. For example, saving a spreadsheet as a comma separated values (CSV) file.

Why transform data?

Goals for data transformation might be:

Data organization: better organized data is easier to use
Data compatibility: different applications or systems can then use the same data
Data migration: data with matching formats can be moved from one system to another
Data merging: data with the same organization can be merged together
Data enhancement: data can be displayed with more detailed fields
Data comparison: apples-to-apples comparisons of the data can then be made