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Name: itsmonterey
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Name: itsmonterey
Type: User
βοΈπ Consumer Demographic Analytics - Jupyter Notebook
Exercises on mapping, filtering, lambda, reduce and recursion.
# Employee Database: A Mystery in Two Parts ![sql.png](sql.png) ## 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.
In this repository, I upload my 100 Days ML Code which I have learned from different courses(Coursera, udemy, edx, udacity), different websites blogs, different tutorials from YouTube, books, and research papers. And this code is basically Siraj Ravalβs 100 Days of ML Code Challenge! Which I completed in 100 days from November 2018 to February 2019. On the basis of my past one and half years of experience, I have done different projects in 100 Days related to Machine Learning, Deep Learning, Computer Vision, Natural Language Processing.
Python-based spatial data analysis and visualization of the GPS location data from my 2014 summer travels.
Public material for CS109
Data, analytic code, and findings related to the BuzzFeed News article, "Inside The Partisan Fight For Your News Feed," published August 8, 2017.
Lecture content for Intro to Data Science 2018
Source code of the winning method in Track 1 and Track 3 at the AI City Challenge Workshop in CVPR 2018.
https://harvard-iacs.github.io/2019-CS109A/
Analysis of 911 calls made to Montgomery County, PA
A pretty straightforward exercise in Exploratory Data Analysis
Exploration of different solutions to action recognition in video, using neural networks implemented in PyTorch.
AdminLTE - Free admin dashboard template based on Bootstrap 4 & 3
Aether client app with bundled front-end and P2P back-end
Code for Agile Data Science 2.0, O'Reilly 2017, Second Edition
A python chatbot framework with Natural Language Understanding and Artificial Intelligence.
Create an AI clone of yourself from your WhatsApp chats (using Llama 3)
To make it easy to benchmark AI accelerators
A tutorial for students that surveys basic ML techniques in ipython notebook format.
Play games without touching keyboard
Alibaba Natural language Processing Hackathon - Sentiment Analysis
An open-source NLP research library, built on PyTorch.
A rugged, minimal framework for composing JavaScript behavior in your markup.
This is my documentation to learn Alpine JS.
Automatic license plate recognition for Indonesian plate (White on black)
Anti_money_laundering
The most recent version of the Applied Machine Learning notes
The goal of the following exercise is to learn the basic Pandas functionality and data visualization techniques. We'll load data from a CSV file, clean it up, answer some exploratory questions and plot a subset of our data.
This repository contains 2nd place solution for the Computer Vision Contest "Game of Deep Learning" organised by Analytics Vidhya
A declarative, efficient, and flexible JavaScript library for building user interfaces.
π Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. πππ
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google β€οΈ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.