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Definition

Database design in data science is the process of creating a database that is optimized for storing and managing the specific data that a data scientist needs to work with. This involves identifying the entities that need to be stored in the database, defining the relationships between those entities, and choosing the appropriate data types for each entity.

Good database design is important for data science because it allows data scientists to efficiently query and analyze their data. A well-designed database will also be more scalable and easier to maintain as the data set grows.

There are a number of different database design methodologies, but all of them follow a similar set of steps:

1. Identify the entities that need to be stored in the database. Entities are the objects that the data scientist needs to store information about, such as customers, products, or orders.
2. Define the relationships between the entities. Relationships define how the entities in the database are related to each other. For example, a customer might have multiple orders, and an order might have multiple products.
3. Choose the appropriate data types for each entity. Data types define the format of the data that is stored for each entity. For example, a customer’s name might be stored as a string, and a product’s price might be stored as a number.
4. Normalize the database. Normalization is a process of organizing the data in the database to reduce redundancy and improve data integrity.
5. Choose a database management system (DBMS). A DBMS is a software application that is used to manage and store data in a database. There are many different DBMSs available, each with its own strengths and weaknesses.

Here are some examples of database design in data science:

• A data scientist designing a database for an e-commerce company might identify the following entities: customers,products, orders, and order items. The relationships between these entities would be defined as follows: a customer can have multiple orders, an order can have multiple order items, and an order item can be associated with a single product. The data types for these entities would be chosen based on the specific needs of the e-commerce company.
• A data scientist designing a database for a social media company might identify the following entities: users, posts,comments, and likes. The relationships between these entities would be defined as follows: a user can create multiple posts, a post can have multiple comments, and a comment can be liked by multiple users. The data types for these entities would be chosen based on the specific needs of the social media company.

Database design is an important skill for data scientists to have. By understanding the principles of database design, data scientists can create databases that are efficient, scalable, and easy to maintain.

More specifically

Physical and logical database design

A physical database design is a blueprint for how the data will be stored on a physical storage device, such as a hard drive or SSD. It specifies the layout of the data on the storage device, the data types used to store the data, and the indexes used to speed up data retrieval.

A logical database design is a conceptual model of the data that is independent of the physical storage device. It specifies the entities, relationships, and attributes that make up the database.

Normalization

Normalization is a process of organizing the data in a database to reduce redundancy and improve data integrity. It involves dividing the data into smaller tables and then defining the relationships between those tables.

There are five levels of normalization, each of which builds on the previous level:

• First normal form (1NF): All attributes of an entity must be atomic, meaning that they cannot be subdivided into smaller parts.
• Second normal form (2NF): All non-key attributes must be fully functionally dependent on the primary key.
• Third normal form (3NF): All non-key attributes must be directly dependent on the primary key, not on another non-key attribute.
• Boyce-Codd normal form (BCNF): Every non-key attribute must be dependent on the whole primary key, not just a part of it.
• Fifth normal form (5NF): All non-key attributes must be dependent on the primary key, and there must be no join anomalies.

Example

Consider a database for an e-commerce company. The database might have the following tables:

• Customers: This table would store customer information, such as name, address, and email address.
• Products: This table would store product information, such as product name, description, and price.
• Orders: This table would store order information, such as the customer who placed the order, the products ordered,and the total price of the order.
• Order items: This table would store information about the individual products ordered, such as the quantity ordered and the price per unit.

This database design would be in third normal form (3NF). The primary key of the customers table is the customer ID,and all of the attributes in the table are fully functionally dependent on the primary key. The primary key of the products table is the product ID, and all of the attributes in the table are fully functionally dependent on the primary key. The primary key of the orders table is the order ID, and all of the attributes in the table are fully functionally dependent on the primary key. The primary key of the order items table is a composite key consisting of the order ID and the product ID,and all of the attributes in the table are fully functionally dependent on the primary key.

Benefits of normalization

Normalization has a number of benefits, including:

• Reduced redundancy: Normalization helps to reduce redundancy in the database, which can save storage space and improve performance.
• Improved data integrity: Normalization helps to improve data integrity by ensuring that all data is stored in a consistent way.
• Increased flexibility: Normalization makes the database more flexible and adaptable to change.

Conclusion

Physical and logical database design and normalization are important concepts for data scientists to understand. By understanding these concepts, data scientists can create databases that are efficient, scalable, and easy to maintain.