Data Analysis
Data analysis aids companies in enhancing their products and services to better meet the needs of their customers. Data Analysis is the collection and organization of large amounts of data in order to extract usable information that can be used to make important business choices. Data Analysis can uncover critical factors, forecast patterns and trends, and increase business productivity as a result of technological advancements. As a result, it enhances the value of business processes by providing a deeper understanding of numbers and figures.
Understanding the Different Methods of Data Analysis
Analyzing data is a complex procedure that requires the careful evaluation of many parameters. To summarize, there are four forms of data analysis:
● Statistical Analysis
● Diagnostic Analysis
● Predictive Analysis
● Prescriptive Analysis

  1. Statistical Analysis
    Data can be described and compared using statistical analysis, revealing patterns and trends. Key performance indicators can be identified by using descriptive and inferential statistical analysis.
  2. Diagnostic Analysis
    When a corporation has a problem, the Diagnostic analysis seeks to determine the root reason by looking at the positive and bad repercussions of earlier decisions. With Diagnostic analysis, organizations discover the real-world basis for the data discrepancies they see.
  3. Predictive Analysis
    Predictive analysis aids in gaining a sense of what is likely to happen in the future by using historical data to predict future patterns. To put it another way, Predictive analysis encourages the organization to align itself with the targeted business outcomes.
  4. Prescriptive Analysis
    In the Prescriptive analysis, a plan of action for the organization is drawn from the findings of the previous three analyses. Companies can handle potential challenges using ML models trained with prior knowledge of tactics thanks to Prescriptive analysis.

Now that we know all about analyzing data, let’s hop right to the topic as to how one can modify or analyze data using SQL.
Modifying and Analyzing Data using SQL
In SQL, we have the VIEW statements, which allow the users to organize the data in a way that is more relevant to their needs. You may have a professor who prefers that each student only see their own grades and not the grades of their peers. As a result, your professor will design a view that allows each student to only see their own individual results. How views are created and updated and dropped if necessary will be explained in this blog. Let’s get started, shall we?
It is possible to create “virtual” tables in SQL, but these tables do not exist in the database. SQL queries that join multiple tables are used to construct these views. Rows and columns can be seen in the views. One or more tables of the database may be used to create a view. The views can also be updated and dropped to suit our needs.
Create SQL View
This command is used to build views in a database. You can design a view using a single table or several tables.
The basic Syntax for creating VIEW:
CREATE VIEW view_name AS
SELECT column1, column2, column3…
FROM table_name
WHERE [condition];

The name of the VIEW you want to construct is view_name. Also, the WHERE conditions might be omitted. For the records to be included in the VIEW, these conditions must be met.
Creating a view from a single Table:
CREATE VIEW Customer_view AS
SELECT Customer_id, Name, Address
FROM Customer_Details
WHERE Address = “Miami”;

As you can see, this CREATE VIEW statement uses the results of the previous SELECT statement to construct a virtual table. To view the output, you can now query the SQL VIEW as follows:

SELECT * FROM Customer_view;
Creating View from Multiple Tables
SELECT Customer_Details.Name, Customer_Details.Address, Customer_Order.Product
FROM Customer_Details, Customer_Order
WHERE Customer_Details.Name = Customer_Order.Name;

As you can see, this CREATE VIEW statement uses the results of the previous SELECT statement to construct a virtual table. To view the output, you can now query the SQL VIEW as follows:

SELECT * FROM Order_view;
Updating The SQL View
It is also possible to make changes to the SQL view that has been established. The SQL VIEW allows us to perform the following actions.

● However, not all views can be updated. If the following criteria are met, then a SQL view can be updated.
● Only one table is used to define the view.
● An aggregate function field should not be present in the view.
● Group by, HAVING, or Distinct clauses are not allowed in the definition of the view.
● No nested queries should be used to construct the view.
● A constant, string, or value expression cannot be used in the view’s output fields.
● The view that you want to update depending on another view should be updatable.
Updating a SQL View
CREATE OR REPLACE VIEW statements can be used to change the SQL view.
SELECT column1,column2,..
FROM table_name
WHERE condition;
SELECT Customer_id, Name, Address, Age
FROM Customer_Details
WHERE Address = “Miami”;

A virtual table would be created based on the results of the SELECT statement in the above CREATE OR REPLACE VIEW statement. To view the output, you can now query the SQL VIEW as follows:

SELECT * FROM Customer_view;

Drop the SQL View
The DROP VIEW statement can be used to remove a view that has been created. As long as we don’t have any previously built views, this is necessary.
DROP VIEW view_name;
In this case, view name denotes the name of the VIEW you wish to remove.

If you wish to remove the created view Customer view, the query is:
DROP VIEW Customer_view;
Data Analysis using SQL: Understanding the Advantages
● SQL for Data Analysis is a user-friendly language since it is simple to grasp and master.
● SQL for Data Analysis is a fast query processor and a powerful tool for retrieving large amounts of data from a variety of databases.
● As SQL for Data Analysis gives standard documentation to users, it supports exceptional handling.

Leave a Reply