# Employee Database: A Mystery in Two Parts  ## 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: 1. Data Modeling 2. Data Engineering 3. Data Analysis ## 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](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: ```sql from sqlalchemy import create_engine engine = create_engine('postgresql://localhost:5432/<your_db_name>') connection = engine.connect() ``` * Consult [SQLAlchemy documentation](https://docs.sqlalchemy.org/en/latest/core/engines.html#postgresql) for more information. * If using a password, do not upload your password to your GitHub repository. See [https://www.youtube.com/watch?v=2uaTPmNvH0I](https://www.youtube.com/watch?v=2uaTPmNvH0I) and [https://martin-thoma.com/configuration-files-in-python/](https://martin-thoma.com/configuration-files-in-python/) for more information. 2. Create a bar chart of average salary by title. 3. You may also include a technical report in markdown format, in which you outline the data engineering steps taken in the homework assignment. ## 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.

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: Data Modeling Data Engineering Data Analysis Before You Begin Create a new folder in your homework repository called sql-challenge. Inside your local git repository, create a directory for the SQL challenge. Use a folder name to correspond to the challenge: EmployeeSQL. Add your files to this folder. 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: List the following details of each employee: employee number, last name, first name, gender, and salary. List employees who were hired in 1986. 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. List the department of each employee with the following information: employee number, last name, first name, and department name. List all employees whose first name is "Hercules" and last names begin with "B." List all employees in the Sales department, including their employee number, last name, first name, and department name. List all employees in the Sales and Development departments, including their employee number, last name, first name, and department name. 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: 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. 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. Create a histogram to visualize the most common salary ranges for employees. 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.

The agricultural industry is important to a country's economic prosperity. Fruit grading is an important duty in the agriculture industry since there is a strong demand for high-quality fruits and vegetables. Agricultural activity feeds the people not only in a single country but also in most of the countries. Whether it’s rice, wheat, or exotic fruits and vegetables, everything is being transported from one country to another. A country like India transports different kinds of rice to a lot of countries. Due to its enormous potential for value addition, notably in the food processing industry, the Indian food industry is poised for massive expansion, increasing its contribution to world food commerce every year. The Indian food and grocery market are the sixth-largest in the world, with retail accounting for 70% of total sales. The Indian food processing sector, which accounts for 32 percent of the country's overall food market and is rated fifth in terms of production, consumption, export, and predicted growth, is one of the country's largest industries. In FY21, total agriculture and allied product exports totaled $41.25 billion. Nowadays agriculture has been the greatest source of economy. Technology has advanced a lot in this field, there are many new pieces of equipment introduced in this field for farming. New techniques have been developed to grow plants in a different and quick way. But the main question arises when we search for skilled manpower to do farming. Farming definitely looks easy but is not, it needs a lot of hard work, skills, and patience to grow healthy fruit, flowers and crops. The agriculture department recruitment agency solves the issue of recruitment of manpower. There are many farms that need skilled manpower for farming but Farm earnings are relatively low, owing to low pricing at the farm gate, despite trained farmers, excellent equipment, fertile soil, and subsidies. Low wages, expensive land prices, and a scarcity of available farmland deter young people from entering the profession. The agriculture department recruitment agencies act as a savior by recruiting young men and young agriculture officers who can save the process of farming by introducing a variety of new techniques so that there would be maximum growth of fruits and vegetables. There are different agriculture recruitment agencies that are recruiting skilled workforce nationwide and internationally. The candidates who find it hard to get a job in the agricultural field should definitely link up with agriculture recruitment agencies. The agencies will guide them up in their career and will be able to help them in getting placed in the developed countries of the Middle East, Far East, Europe, North America, and the Asia Pacific. The companies may get good help from agriculture recruitment agencies as they provide skilled manpower like Farm Adviser, Farm Apprenticeship, Farm Director Jobs, Farm Driver Jobs / HGV Farm Driver Jobs, Farm Engineering and Mechanical Jobs, including; Farm Technician Jobs, Farm Management posts, including; CEO, CFO, MD and Operations, Farm Project Manager, Farm Sales and Retails, Farm Trading and Farm Purchasing, Farm Operator, Farm Sprayer Operator Agronomist, Tractor Driver, General Farm worker, Herdsperson posts, including; Herdsman and Herdswoman, Dairy Milker, Farm Crop Trials, Farm Workshop, Calf Rearing, Slaughterhouse Manager, Farm Picking, and Farm Packing, Farm Building, Games Keeping, Artificial Insemination Jobs including; AI Jobs and Genetics Jobs. Benefits of a recruiting agency Agriculture department recruitment services assist individuals in narrowing their search. They conduct a thorough search and attempt to comprehend what the candidates’ desire. Recruiting agencies, on the other hand, form alliances with the greatest companies in other countries. Agriculture recruitment agencies assist businesses in obtaining trained, unskilled, and semi-skilled human labor to meet their demands. Good Communication The agriculture recruitment agency bridges the gap between the employee and employer. Agriculture department recruitment agencies form partnerships with the best companies and provide them with the greatest candidates from all around the world. Expert Advice Agriculture recruitment services have their own experts who assist in the recruitment of applicants. Experts go through thousands of applications to find the finest ones. They conduct interviews in many rounds before selecting the finest candidates. The applicants are trained and deployed by professionals. Compensation Package When it comes to wages, it is always tough for an employer and an employee to be on the same page. Agriculture recruitment agencies assist with compensation and wage negotiations. Farm Advisor, Farm Apprenticeship, Farm Director Jobs, Farm Driver Jobs / HGV Farm Driver Jobs, Farm Engineering and Mechanical Jobs, including Farm Technician Jobs, Farm Management posts, including CEO, CFO, MD and Operations, Farm Project Manager, and so on are among the positions that the agencies recruit for. Agricultural engineers earn between 40,000 and 60,000 every month. Agricultural officers are paid between 60,000 and 1 lakh per month. A candidate's compensation is determined by his or her experience and talents. Extensive Database These firms have access to huge databases including millions of candidate profiles, which they may utilize to fill any of the vacancies. This database is designed to quickly locate and hire people. Conclusion Farming is a major source of employment around the world, with farm workers making a significant contribution to our daily lives and the global economy. The Agricultural & Farming Jobs specialized team draws on our extensive industry knowledge and years of combined recruitment experience to find qualified individuals for a variety of Farming Jobs. There is huge competition in the market and in this competition, agriculture saves a lot of human life and cannot be neglected a bit. The agriculture recruitment agencies are serving in the best possible way by recruiting skilled and unskilled manpower from different undeveloped and semi-developed countries like India, Nepal, Bangladesh, Srilanka, Philippines, Kenya and Uganda. Definitely, Agriculture is a boon to all of us.

Walmart Data Cleaning, Analysis & MySQL Integration A complete ETL pipeline project where raw Walmart sales data is cleaned using Python (pandas), enriched with feature engineering, and stored in a MySQL database. The project includes SQL queries to analyze customer behavior, sales performance, profitability, and branch-level insights.

In this Database capstone project, I'll be: creating an ER Diagram data model and implement it in MySQL Workbench using forward engineering, Using SQL to create virtual tables, stored procedures and prepared statements for Little Lemon restaurant, Visualizing the sales data of a restaurant and implement a database client in Python.

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: Data Modeling Data Engineering Data Analysis 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: List the following details of each employee: employee number, last name, first name, gender, and salary. List employees who were hired in 1986. 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. List the department of each employee with the following information: employee number, last name, first name, and department name. List all employees whose first name is "Hercules" and last names begin with "B." List all employees in the Sales department, including their employee number, last name, first name, and department name. List all employees in the Sales and Development departments, including their employee number, last name, first name, and department name. In descending order, list the frequency count of employee last names, i.e., how many employees share each last name.

## 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](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. * For the primary keys check to see if the column is unique, otherwise create a [composite key](https://en.wikipedia.org/wiki/Compound_key). Which takes to primary keys in order to uniquely identify a row. * Be sure to create tables in the correct order to handle foreign keys. * Import each CSV file into the corresponding SQL table. **Note** be sure to import the data in the same order that the tables were created and account for the headers when importing to avoid errors. #### 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, sex, and salary. 2. List first name, last name, and hire date for 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. 4. List the department of each employee with the following information: employee number, last name, first name, and department name. 5. List first name, last name, and sex for 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: ```sql from sqlalchemy import create_engine engine = create_engine('postgresql://localhost:5432/<your_db_name>') connection = engine.connect() ``` * Consult [SQLAlchemy documentation](https://docs.sqlalchemy.org/en/latest/core/engines.html#postgresql) for more information. * If using a password, do not upload your password to your GitHub repository. See [https://www.youtube.com/watch?v=2uaTPmNvH0I](https://www.youtube.com/watch?v=2uaTPmNvH0I) and [https://help.github.com/en/github/using-git/ignoring-files](https://help.github.com/en/github/using-git/ignoring-files) 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. * Ensure your repository has regular commits and a thorough README.md file

A complete sales engineering database project built for INSC 384. Includes a relational database schema, sample data, queries, and a written design report. The project demonstrates requirements analysis, entity relationship modeling, and database design for a real world sales operations environment.

mysql sql data-analysis walmart sales-analysis EDA feature-engineering database

SQL project analyzing Walmart sales data, including database setup, feature engineering, and data exploration

Designed an Inventory Model Database using data model in My SQL and forward engineering. Forecasted next year sales based on the monthly sum total of sales and dividing into monthly chunks using Time series model in Excel.

An end-to-end analysis of Walmart sales data using Python and SQL. Tasks include data cleaning, feature engineering, and database integration, culminating in actionable insights on sales trends, profitability, and customer behavior to solve critical business problems.

Build a complete data pipeline: Use Apache Airflow to automate an end-to-end data pipeline that analyzes daily sales revenue from a PostgreSQL database. A hands-on project covering key data engineering skills.

This project is a robust data engineering task that implements database setup, and analytics of a company sales data. It enables management have both an overview of and insights into the key performance indicators.

Airflow: Automated E-commerce Sales Analysis Pipeline. This data engineering project focuses on automating the process of downloading and integrating incremental sales data from an API into a normalized PostgreSQL database, with daily update of an analytical sql view that showcases the profitability and popularity of different product categories.

ETL Sales Pipeline is a mini data engineering project that extracts raw sales data from a CSV file, transforms it with cleaning and total calculations, and loads it into a SQLite database. It provides a REST API for real-time CRUD operations and a CLI tool for easy navigation, updates, exports, and reporting.

ETL pipeline project for processing sales data. Extracts data from CSV and JSON files, transforms it by cleaning and calculating totals, and loads it into a PostgreSQL database. Utilizes PySpark for efficient data handling. Ideal for showcasing data engineering skills.

A comprehensive end-to-end data analysis project using Python and PostgreSQL to uncover business insights from Walmart sales data. The project covers data cleaning, feature engineering, database integration, and advanced SQL analytics—demonstrating key skills for data analyst roles.

This project demonstrates a data engineering pipeline using FastAPI, MySQL, and Jupyter Notebook. It processes raw transactional data, cleans and transforms it using Pandas, and loads it into a structured MySQL database. A FastAPI-based REST API allows querying customer summaries and product sales data efficiently.

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: Data Modeling Data Engineering Data Analysis Before You Begin 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. Inside your local git repository, create a directory for the SQL challenge. Use a folder name to correspond to the challenge: EmployeeSQL. Add your files to this folder. 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: List the following details of each employee: employee number, last name, first name, gender, and salary. List employees who were hired in 1986. 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. List the department of each employee with the following information: employee number, last name, first name, and department name. List all employees whose first name is “Hercules” and last names begin with “B.” List all employees in the Sales department, including their employee number, last name, first name, and department name. List all employees in the Sales and Development departments, including their employee number, last name, first name, and department name. In descending order, list the frequency count of employee last names, i.e., how many employees share each last name.

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: Data Modeling Data Engineering Data Analysis Before You Begin 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. Inside your local git repository, create a directory for the SQL challenge. Use a folder name to correspond to the challenge: EmployeeSQL. Add your files to this folder. 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: List the following details of each employee: employee number, last name, first name, gender, and salary. List employees who were hired in 1986. 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. List the department of each employee with the following information: employee number, last name, first name, and department name. List all employees whose first name is “Hercules” and last names begin with “B.” List all employees in the Sales department, including their employee number, last name, first name, and department name. List all employees in the Sales and Development departments, including their employee number, last name, first name, and department name. In descending order, list the frequency count of employee last names, i.e., how many employees share each last name.

have just started university, specifically systems engineering, and I am the group of the teacher more blade in programming, I have no idea of programming and I have to do the following work: A software for a company that can add, delete edit; sellers (calculate commissions for sales), products, sales and without the use of a database, it must be for flat files, I was really trying to do it for a week watching tutorials on YouTube but it was impossible for me to really frighten me so much that I tried I made it through access that was forbidden but I could not, so I turn to you so they can help me, the only teacher that left us was a project of yours that also attached (BancoFinal2.0) and said something related to programming oriented or something like that but I really did not understand ... //Acabo de iniciar la universidad, especificamente ingenieria de sistemas, y me toco el grupo del profesor mas cuchilla en programacion, no tengo ni idea de programacion y tengo que realizar el siguiente trabajo: Un software para una empresa que pueda agregar, eliminar editar; vendedores (calcular comisiones por ventas), productos, ventas y sin el uso de una base de datos, tiene que ser por archivos planos, la verdad estuve intentando realizarlo por una semana viendo tutoriales en youtube pero me fue imposible realmente me fruste tanto que intente realizarlo por medio de access que estaba prohibido pero tampoco pude, asi que recurro a ustedes para que me puedan ayudar, el profesor el unico soporte que nos dejo fue un proyecto suyo que tambien les adjunto (BancoFinal2.0) y dijo algo relacionado con programación orientada o algo asi pero la verdad no le entendi...

Data engineering of a sales database using SQL. Have uploaded the instructions, the initial database and the solution query

A full-stack data project: SQL database engineering & Python business intelligence analysis for car sales data.

SQL analytics and data engineering project focused on sales and revenue analysis using a relational database.

My implementation of Little Lemon Sales Report in Meta Database Engineering using Jupyter, MySQL and Python

This project simulates a real-world retail sales database using SQL. It includes database design, data cleaning, feature engineering, and business analytics queries.

To analyze movie sales data by reverse engineering the existing database schema and utilizing SQL queries to derive actionable insights into sales performance, customer preferences, and product trends

This project is a data engineering assessment focused on the sales domain. It demonstrates the process of loading sales data into a database, creating tables and running analytical queries.

This project analyzes Walmart's sales data from branches in 3 cities. It involves product, sales, and customer analyses to understand factors influencing sales. Through data wrangling, database creation, feature engineering, and exploratory data analysis, insights are derived.