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                            Data Science 

     This article tells about the data science introduction, descriptive, diagnostic, predictive and perspective, database management system, data models -conceptual data model, logical data model, physical data model, object-oriented data model, relational data model, and history of data models , hierarchical database management system, network database management system, relational database management system 



    Data 
  •  Data is nothing but a piece of information (OR) we can say data is a collection of record 
  •  Data can be of different types, such as numerical, categorical, text, image, audio, and video data.   Data can be collected through various sources such as surveys, experiments, observations, sensors, and social media platforms       
       Science
  •  Science is all about acquiring knowledge


Every piece of information can be considered as data in this present generation  each and every piece of information will be viewed as valuable so every business can be developed because of data if you have a business then only we  have the technology if we have technology means then only we have jobs soo in this article you can understand how data science is used in the business world

Data science in 4 Parts



Descriptive
  • It means what exactly happened?
  • “What has happened?”. This type of analytics analyses the data coming in real-time and historical data for insights on how to approach the future.
  • The main objective of descriptive analytics is to find out the reasons behind precious success or failure 
Diagnostic
  • It means why did it happen?
  •  Diagnostic analytics helps identify anomalies and determine casual relationships in data. For example, eCommerce giants like Amazon can drill the sales and gross profit down to various product categories like Amazon Echo to find out why they missed their overall profit margins
Predictive
  • What is going to have happened in the future is analyzed based on the past experience
  • The purpose of predictive analytics isn’t to tell you what will happen in the future. It cannot do that, and none of the analytics is capable of doing that. Predictive analytics can only forecast what might happen in the future because all predictive analytics are probabilistic."
Prescriptive
  • What is the best course of action, it means it will check bugs in already existing information
    • Prescriptive analytics is the next step of predictive analytics that adds the spice of manipulating the future. Prescriptive analytics advises on possible outcomes and results in actions that are likely to maximize key business metrics

    DBMS

    •  A database management system is a software system that is used to store and retrieve the data in the database
    •  It is a software system designed to allow users to define, create, manipulate, and manage databases.
    • A DBMS is an essential tool for storing and managing large amounts of data efficiently and securely.
    • Relational DBMS is the most commonly used type of DBMS, which stores data in tables with columns and rows, and allows users to create relationships between different tables. Examples of relational DBMS include MySQL, Oracle, Microsoft SQL Server, and PostgreSQL
    • previously we don't have an accessible option in DBMS so CODDs have overcome this DBMS and invented DATA MODELS

    DATA MODELS

    1. It describes how data is stored and accessed and updated in a database management system
    2. Data models are designed in the database, and software engineers ensure how data is stored is organized in a way that is efficient, accurate, and consistent.

    DATA MODEL STAGES

    Conceptual data model: 
    • This model represents the high-level concepts and relationships between them. It does not include implementation details and is usually created during the early stages of a project.

    Logical data model: 

    • This model provides more detail than the conceptual model, including the attributes of each entity and the relationships between them. It is used to design the database schema and is often expressed using entity-relationship diagrams (ERDs)

    Physical data model: 

    • This model describes how data is stored in the database including data types, indexes, and constraints to ensure data is organized in a way efficient and accuracy consistent.

    Object-oriented model:

    •  It is used in object-oriented programming and is often expressed using Unified Modeling Language (UML) diagrams.

    • UML is a visual language that is used to model object-oriented systems and is widely used in software engineering to design and document software systems

    Relational-data model:

    • It describes how data tables are stored in the form of the relational model
    • data is stored in the form of rows and columns in the table 

    NOTE: 

    • In data models concept coadd's has invented one hierarchical model called as HDBMs
    • HDBMS stands for hierarchal database management system
    • A hierarchical database model is a data model in which the data is organized into a tree-like structure. The data is stored as records that are connected to one another through links. 



    • In HDBMS there is no physical data independence so they have introduced an upgraded version called NDBMS 
    • NDBMS stands for network database management system



    • In NDBMS there is no logical dependence  and data is stored in the form of blocks if anyone of the block is braked entire data is lost so they have introduced one upgraded version called the Relation model
    • The codd's has come up with some rules and introduced some relational database management system









    I hope you like this article.....


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