• About the Project
• Data Modeling
• Data Engineering
• Data Analysis
• Salary Analysis
• Conclusion
• Resources
• Contact

As a recent employee at Pewlett Hackard, I have been tasked to organize and analyze data for employees at the corporation in the 1980s and 1990s. I have used a database mapping tool to visualize all of the data and then PostgreSQL to analyze this information. Below you will see a step by step guide to how the information was modeled and reviewed.

To visualize my data, I used an Entity Relational Database called Quick Database Diagrams . First, I reviewed my 6 different csv files

• employees.csv
• dept_emp.csv
• dept_manager.csv
• salaries.csv
• titles.csv
• departments.csv

After reviewing the data, I was able to determine what columns would be Primary Keys for when comparing my data files. I then created tables for each csv file and included the details for the data going into each column. For example, numbers were assigned as INTEGERS and names and personal information were assigned as VARCHAR .

Once my tables were created in the mapping tool, I then linked the tables on Primary keys and other data relationships. I linked employee_id and other id information.

Below is what the code looks like in the data mapping tool. Once I completed organizing the data, I exported this into a file to use in PostgreSQL .

After exporting the schema from the data mapping tool, I used the code to create my tables in pgAdmin4 . The code exported with " " around the variables, so I deleted the quotes when transferring the code to easier manipulate the data later.

The code includes the CREATE TABLE command and then includes one line per column of data. Each column has syntax for the type of data included in the cells and the command NOT NULL to only include cells with information included and not blank cells. The end syntax refers to the Primary Key which I set to be the Employee_ID .

Employee Table:

Department Table: includes Department_ID and Department_Name

Department Manager Table: includes Department_ID and manager Employee_ID

Employee Department Table: includes Employee_ID and Department_ID

Job Title Table: includes Title_ID and Title

Salary Table: includes Employee_ID and Salary amount

By mapping my data tables in the mapping tool, I created CONSTRAINTS , PRIMARY KEYS , and FOREIGN KEYS . These criteria help my data tables interact when combining information into one queries .

1. List the following details of each employee: employee number, last name, first name, sex, and salary.

Query : To show this view, I used an INNER JOIN on the Employee and Salaries tables. I joined the data on the Employee_ID .

Data Output:

2. List first name, last name, and hire date for employees who were hired in 1986.

Query: To show this view, I used a WHERE function to only show Employees who were hired in the year 1986 - WHERE hire_data LIKE ‘%1986%’ . If you put the search value in ’% %’ it searches similarly to ‘contains’.

Data Output:

3. List the manager of each department with the following information: department number, department name, the manager's employee number, last name, first name.

Query: To display this view, I had to INNER JOIN 3 different data tables- Employee , Managers , and Departments . I joined the Manager and Employee tables on Employee_ID . I joined the Managers and Departments on Department_ID .

Data Output:

4. List the department of each employee with the following information: employee number, last name, first name, and department name.

Query: This view is similar to the view in question 3 accept the INNER JOIN is on the Employee , Employee_DepartmentandDepartmenttables instead ofManagerdepartments. I also joined the tables onEmployee_IDandDepartment_ID` .

Data Output:

5. List first name, last name, and sex for employees whose first name is "Hercules" and last names begin with "B."

Query: To show this view, I used a WHERE statement with 2 criteria- WHERE the First_Name is equal to ’Hercules’ and WHERE the Last_Name starts with the letter ’%B%” .

Data Output:

6. List all employees in the Sales department, including their employee number, last name, first name, and department name.

Query: To show this view, I had to use INNER JOIN 3 tables- Employee , Employee_Department , and Department. This is how the view is able to display the Employee information as well as the Department_Name . I also used a WHERE clause to only show Employee’s who are in the Sales department.

Data Output:

7. List all employees in the Sales and Development departments, including their employee number, last name, first name, and department name.

Query: This view is similar to the view in the above question accept in the WHERE statement, there are 2 criteria- the Department is equal to Sales OR Development .

Data Output:

8. In descending order, list the frequency count of employee last names, i.e., how many employees share each last name.

Query: To show this view, I used a COUNT function to count the number of times a Last_Name was shared by employees. I labeled this new count as ‘Frequency’ to appear as a new Column in the Data Output. To count the ‘like’ Last_Names , I used a GROUP BY function to group the same Last_Name. Then I sorted the data in descending order by using an ORDER BY command.

Data Output:

To further analyze the employee data, I imported the database into Jupyter Notebook. I had to use the create_engine and engine.connect( ) commands to import the data.

Once it was linked to notebook, I was able to use code I created in PostgreSQL to view the data in a table. When importing the code, I use 3 sets of “ “ to organize my code on separate lines (the way it is displayed in pgAdmin .

I then viewed the Salary data in a histogram . I used the code data.hist(‘salary) to display the chart.

To further display the data in a bar chart, I use a groupby function to group the data by Job Title and then used .mean( ) to calculate the average salary by Job Title. I used .plot(kind = bar) to create the bar chart.

After reviewing the data in the bar chart format, it was clear there was something strange about the information. The Bar chart shows that Sr level employees were making the same salaries as manager level and very close salaries to assistant level.

When I brought this up to my new manager, he told me to do a search in the database for my Employee ID – 499942.

I did this by using a WHERE clause to find the employee_id = 499942 . The data showed below :

Very Funny….

Link to Jupyter Notebook

Link to SQL Employee Database

Sara Simoes