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Why Data Modeling Is So Crucial For Data Science-Explained
Data Modelling is a process of representing data structurally in a table for a company’s database. This data model is a conceptual representation of an association between different data objects.
A Detailed Understanding Of Data Modeling-
The process of Data Modeling occurs in three different layers
- Physical Model
The physical model is more like a schema that which depicts how data is stored physically in the database
- Conceptual Model
This is simply the user view of the data i.e. the high level which the user sees
- Logical Model
This is model sits in the centre of above mentioned models & it represents the data logically, separate from its physical stores
Why Data Modeling Is So Important In Data Science?
With Data Modeling it becomes easy to visually represent the data and enforces business logic, regulations, policies, etc on data. Data Modeling is more like a guide for Data Scientists towards accurate designing and implementation of a database.
Data Modeling ensures the following benefits
- Helps in delivering accurate results with accurate representation of data sets
- It helps as stated earlier to design database at conceptual, physical and logical levels.
- Makes it easy to create relational tables, primary keys, foreign keys, etc.
- Database developers can create a better physical database with a good model as it becomes a guiding tool for them
Advantages Of Data Modeling-
There are various advantages are as follow:
- Helps in better communication of plans across their organization
- It helps to recognize the correct source of data which can be used to populate the model.
- This can be used to define relationships between different tables like primary key, foreign key, etc.
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