MTH522 Course

Financial Insights in Public Safety_organized (2)

In this update, we will provide answers to these queries.

Which department pays its employees the most on average?
Which job titles are the most popular, and what is the usual salary range for them?
Do jobs with the same title in different departments pay significantly differently from one another?

With an average income of almost $165,516 per year, “Superintendent” is the department with the highest average compensation.

The job titles “teacher,” “paraprofessional,” “police officer,” “fire fighter,” and others are the most often occurring ones in the dataset.

The graphics up top display

The mean income for the most popular job titles and

Sector with the highest mean pay

 

What is the mean annual salary for each department’s staff members?
What is the difference in overtime compensation between various job titles?
What percentage of the overall salary is comprised of perks, overtime, and regular pay?
These will be resolved in this update.These were the results.

Here are the codes for the output:

In brief:

Departmental averages for total pay differ considerably from one another.
The average overall salary for the “ASD Human Resources” department is around $66,849, while the average compensation for the “Accountability” department is approximately $90,403.
Because different departments have different pay scales and responsibilities within them, each department displays a different average compensation value.

The amount paid for overtime varies depending on the job title. Nonetheless, a lot of job titles lack overtime numbers, which could indicate that they are positions that don’t usually earn overtime.
Average overtime compensation varies widely, when data is available. For example, the typical overtime compensation for a “Youth Worker” is around $757, while the average overtime pay for a “Yth Programs Librarian III” is approximately $585.

At around 82.91% of total compensation, regular pay makes up a sizable portion of it.
Pay for overtime compensates around 7.33% of the entire salary.
The remaining compensation and perks make up around 3.86%.

examining the data set that will comprise the problems part of our report, we may ask the following questions:

Which department pays its employees the most on average?
Which job titles are the most popular, and what is the usual salary range for them?
Do jobs with the same title in different departments pay significantly differently from one another?
Does regular salary have any bearing on overtime compensation?
What effect does the “OTHER” pay category have on the gross total compensation?
Are there any pay patterns that may be seen in various postal code regions?
Which workers receive disproportionately large or small total remuneration, and what variables influence this?
Are there any pay disparities in particular job titles or departments?
Is it possible to forecast an employee’s entire pay based on elements such as division, title, work schedule, and overtime?
What important factors are associated with large overtime pay?
Which divisions have the greatest overall payroll expenses?
How can the data help with resource allocation and budgeting for various departments?
What is the mean annual salary for each department’s staff members?
What is the difference in overtime compensation between various job titles?
What percentage of the overall salary is comprised of perks, overtime, and regular pay?

The data set that we selected did have missing values in addition to other data kinds. Upon evaluating the data, we discovered that the missing values in each category are as follows.

RETRO: 20,112 values are missing.
OTHER: 7,378 values are missing.
16,392 values are missing across time.
21,983 missing data indicate injury.
DETAIL: 21,088 values are missing.
QUINN_EDUCATION: 21,835 data are missing.
COMMON: 600 values are missing.
There were no missing entries in any columns, including “NAME,” “DEPARTMENT_NAME,” “TITLE,” “TOTAL_GROSS,” and “POSTAL.”

Having said that, there are a lot of missing entries in fields like “RETRO,” “INJURED,” “DETAIL,” and “QUINN_EDUCATION,” which probably means that not all employees get these kinds of benefits.

Unlike the previous two data sets, this one can be cleaned because at least one field is empty.

In this update, I’ll discuss the data set I’ll be using and provide definitions for each variable in the data set I’ll be utilizing.

Total items: The dataset has 23,204 items in total.

There were both strings and integers/float values in the data collection.

Columns: There are twelve columns in the dataset:

NAME: The staff members’ names.
DEPARTMENT_NAME: The titles of the divisions in which the staff members are employed.
TITLE: The staff members’ job titles.
REGULAR: Periodic pay.
RETRO: Pay back in kind.
OTHER: Alternative forms of payment.
Pay for overtime is provided.
HURT: Damages for harm sustained.
DETAIL: Extra information regarding pay.
QUINN_EDUCATION: Remuneration associated with education.
TOTAL_GROSS: The total amount of gross pay.
Postal codes are POSTAL.

In the upcoming update, I will discuss data cleansing.

This update discusses many income streams within each category (the category is the one with blocks, and the remaining are its classes).

Specifics-YDT

Earnings Details Paid Injured-YIJ

Frequent Earnings Injuries
Historical Damaged Profits

Other-YOH Incentive Bonus Earnings
earnings beyond the base pay that are connected to specialized titles, abilities, or education AND unrelated to overtime
Resentment/Agreement
Consistent Stipend Income
reimbursement costs deducted from paychecks. Mileage reimbursement, tool reimbursement, and uniform allowance are a few examples.

Overtime

Chargeable Overtime
Amounts beyond base pay that are determined by longevity, career awards, and duration of service to the City, as well as by the number of hours worked outside of regular business hours
Earnings beyond the basic pay that are tied to hours worked outside the regular weekday schedule AND dependent on particular time periods worked (night, day, weekend)
Income over the base pay that is connected to specialized titles, training, or education AS well as hours done outside of the typical weekday schedule
Earnings resulting from labor completed outside the grade level or occasionally at a higher grade, step, or pay AND connected to overtime worked.
Earnings for overtime are based on time spent on the same tasks as during the regular workday after hours. stipend income based on hours worked.

Standard-YRG

Base pay and any additional items not falling under any of the other headings
Income Advancement
Income beyond the basic pay that is determined by longevity and duration of service to the City, not by hours spent outside of the regular weekday schedule
Extra earnings that are contingent on working certain hours (night, day, or weekend) and not tied to working longer hours than the standard weekday schedule
earnings beyond the base pay that are connected to specialized titles, abilities, or education AND unrelated to overtime
Pay earned as a result of labor done outside of grade level or occasionally at a higher grade, step, or pay; not connected to overtime working.
Retro-YRT

Continual Retro profits
Historical Overtime Profits

I have selected to work with the “employee earnings report” data collection.

The City of Boston releases payroll information for staff members annually. Names, job descriptions, and information on base pay, overtime, and total compensation for City employees are all included in this collection. Visit the following URL to view the data set.-https://data.boston.gov/dataset/employee-earnings-report

I chose this since we had wrote about something comparable in the prior topic, which was the shootings. This information provides us with a rudimentary understanding of the salaries of the police and other departments.

I’ll be discussing the different kinds of profits that go under each category in the next update.

 

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