Rolling Average Findings Using Window Functions.

In This video, we will discuss the rolling average of salaries organization-wise and also department-wise with different use cases.
You can get many SQL case studies and projects from my GitHub and medium page links mentioned below.
/ mahendraee204
github.com/mahendra204
Here are the table create and insert statements:
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create table employee (
employee_id int,
employee_name varchar(30) ,
age int ,
department VARCHAR(50) ,
salary int
);
insert into employee values(
(1, 'Amit Sharma', 30, 'Engineering', 75000),
(2, 'Neha Verma', 27, 'Marketing', 65000),
(3, 'Ravi Kumar', 35, 'Sales', 80000),
(4, 'Priya Singh', 29, 'HR', 60000),
(5, 'Suresh Gupta', 40, 'Finance', 90000),
(6, 'Anita Joshi', 32, 'Engineering', 70000),
(7, 'Rahul Patil', 26, 'Marketing', 62000),
(8, 'Sneha Desai', 38, 'Sales', 85000),
(9, 'Vikas Nair', 31, 'HR', 61000),
(10, 'Pooja Iyer', 45, 'Finance', 95000),
(11, 'Karan Mehta', 28, 'Engineering', 68000),
(12, 'Alok Pandey', 34, 'Marketing', 67000),
(13, 'Geeta Menon', 36, 'Sales', 82000),
(14, 'Nisha Pillai', 41, 'HR', 62000),
(15, 'Vivek Reddy', 37, 'Finance', 88000),
(16, 'Sanjay Kulkarni', 33, 'Engineering', 72000),
(17, 'Rekha Bhatt', 29, 'Marketing', 64000),
(18, 'Manoj Yadav', 43, 'Sales', 83000),
(19, 'Deepa Rao', 39, 'HR', 63000),
(20, 'Ramesh Chandra', 42, 'Finance', 91000),
(21, 'Mahendra' , 30, 'Engineering', 75000),
(22, 'Mahi', 32, 'Marketing', 67000);