SQL Homework - Employee Database: A Mystery in Two Parts
Instructions
This repository works as follows. The EmployeeSQL folder holds all files and folders related to this activity. The folders contained herein are the following:
QuickDBD-employeeDB.png - this is the .png file of the ERD diagram. createTables.sql - this .sql file contains the schema for creating the table and assigning keys. queries.sql - this .sql file contains the schema for running all queries related to the employeeDB files.
employeeDBbonus.ipynb - This jupyter notebook file runs the code for the histogram and bar chart. output_files - this folder contains the histogram and bar chart created by the above file.
Conclusion
Yes, this dataset is likely fake. The name my id number pulls up is "April Foolsday."
Background
It is a beautiful spring day, and it is two weeks since you have been hired as a new data engineer at Pewlett Hackard. Your first major task is a research project on employees of the corporation from the 1980s and 1990s. All that remain of the database of employees from that period are six CSV files.
In this assignment, you will design the tables to hold data in the CSVs, import the CSVs into a SQL database, and answer questions about the data. In other words, you will perform:
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Data Modeling
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Data Engineering
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Data Analysis
Before You Begin
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Create a new repository for this project called
sql-challenge. Do not add this homework to an existing repository. -
Clone the new repository to your computer.
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Inside your local git repository, create a directory for the SQL challenge. Use a folder name to correspond to the challenge: EmployeeSQL.
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Add your files to this folder.
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Push the above changes to GitHub.
Instructions
Data Modeling
√ Inspect the CSVs and sketch out an ERD of the tables. Feel free to use a tool like http://www.quickdatabasediagrams.com.
Data Engineering
√ * Use the information you have to create a table schema for each of the six CSV files. Remember to specify data types, primary keys, foreign keys, and other constraints.
√ * Import each CSV file into the corresponding SQL table.
Data Analysis
Once you have a complete database, do the following:
√ 1. List the following details of each employee: employee number, last name, first name, gender, and salary.
√ 2. List employees who were hired in 1986.
√ 3. List the manager of each department with the following information: department number, department name, the manager's employee number, last name, first name, and start and end employment dates.
√ 4. List the department of each employee with the following information: employee number, last name, first name, and department name.
√ 5. List all employees whose first name is "Hercules" and last names begin with "B."
√ 6. List all employees in the Sales department, including their employee number, last name, first name, and department name.
√ 7. List all employees in the Sales and Development departments, including their employee number, last name, first name, and department name.
√ 8. In descending order, list the frequency count of employee last names, i.e., how many employees share each last name.
Bonus (Optional)
As you examine the data, you are overcome with a creeping suspicion that the dataset is fake. You surmise that your boss handed you spurious data in order to test the data engineering skills of a new employee. To confirm your hunch, you decide to take the following steps to generate a visualization of the data, with which you will confront your boss:
√ 1. Import the SQL database into Pandas. (Yes, you could read the CSVs directly in Pandas, but you are, after all, trying to prove your technical mettle.) This step may require some research. Feel free to use the code below to get started. Be sure to make any necessary modifications for your username, password, host, port, and database name:
from sqlalchemy import create_engine
engine = create_engine('postgresql://localhost:5432/<your_db_name>')
connection = engine.connect()-
Consult SQLAlchemy documentation for more information.
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If using a password, do not upload your password to your GitHub repository. See https://www.youtube.com/watch?v=2uaTPmNvH0I and https://martin-thoma.com/configuration-files-in-python/ for more information.
√ 2. Create a histogram to visualize the most common salary ranges for employees.
√ 3. Create a bar chart of average salary by title.
Epilogue
√ Evidence in hand, you march into your boss's office and present the visualization. With a sly grin, your boss thanks you for your work. On your way out of the office, you hear the words, "Search your ID number." You look down at your badge to see that your employee ID number is 499942.
Submission
√ * Create an image file of your ERD.
√ * Create a .sql file of your table schemata.
√ * Create a .sql file of your queries.
√ * (Optional) Create a Jupyter Notebook of the bonus analysis.
√ * Create and upload a repository with the above files to GitHub and post a link on BootCamp Spot.
Grading Rubric
Data Modeling
√ ERD:
√ Correct column names
√ Correct corresponding data types
√ Primary keys set
√ Foreign keys correctly related
Data Engineering
√ Schemas run with the following:
√ All required columns
√ Columns set to the correct data type
√ Primary key for each table
√ Foreign keys correctly related
√ NOT NULL used correctly
√ Define value length for columns
Data Analysis
√ Queries for all questions and:
√ Queries provide expected result
√ Queries run without error
Bonus
√ Queries tables from postgres db to pandas df
√ Replaces NULL values
√ Generates average salary bar chart
√ Plot has titles and axes labels