100xdevs Bootcamp 1.0 @hkirat Contains All the Assignments and Notes. ( WebDev + DevOps, AI and ML, Web3, DSA )

GStar Bootcamp - Assignment 1

# 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.

This sample code for CodePath week 1 assignment. 這份sample code是用於CodePath與Girls in Tech Taiwan舉辦的Bootcamp的第一週Assignment。

A collection of assignments, exercises, and mini-projects from Bootcamp 1.0. The goal of this repository is to practice foundational concepts, improve problem-solving skills, and track learning progress throughout the bootcamp.

html practice. Jitu bootcamp assignment 1

Rice Bootcamp Challenge Assignment 1: Code Refactor

At Coding Dojo, weeks 1 and 2 are focused on learning HTML, CSS, and Javascript for beginning front-end development. These are several of the assignments I completed throughout the Web Fundamentals section of the bootcamp.

This Personal Income & Expense Tracker is designed to help individuals manage their finances by tracking income sources and expenses on a monthly basis. Created using Excel by Shaikh Minhaj as part of Assignment 1 from the Codebasics Data Analytics Bootcamp

# All the News That's Fit to Scrape ### Overview In this assignment, you'll create a web app that lets users view and leave comments on the latest news. But you're not going to actually write any articles; instead, you'll flex your Mongoose and Cheerio muscles to scrape news from another site. ### Before You Begin 1. Create a GitHub repo for this assignment and clone it to your computer. Any name will do -- just make sure it's related to this project in some fashion. 2. Run `npm init`. When that's finished, install and save these npm packages: 3. express 4. express-handlebars 5. mongoose 6. body-parser 7. cheerio 8. request 9. **NOTE**: If you want to earn complete credit for your work, you must use all six of these packages in your assignment. 10. In order to deploy your project to Heroku, you must set up an mLab provision. mLab is remote MongoDB database that Heroku supports natively. Follow these steps to get it running: 11. Create a Heroku app in your project directory. 12. Run this command in your Terminal/Bash window: * `heroku addons:create mongolab` * This command will add the free mLab provision to your project. 13. When you go to connect your mongo database to mongoose, do so the following way: ```js // If deployed, use the deployed database. Otherwise use the local mongoHeadlines database var MONGODB_URI = process.env.MONGODB_URI || "mongodb://localhost/mongoHeadlines"; // Set mongoose to leverage built in JavaScript ES6 Promises // Connect to the Mongo DB mongoose.Promise = Promise; mongoose.connect(MONGODB_URI); ``` * This code should connect mongoose to your remote mongolab database if deployed, but otherwise will connect to the local mongoHeadlines database on your computer. 14. [Watch this demo of a possible submission](mongo-homework-demo.mov). See the deployed demo application [here](http://nyt-mongo-scraper.herokuapp.com/). 15. Your site doesn't need to match the demo's style, but feel free to attempt something similar if you'd like. Otherwise, just be creative! ### Submission on BCS * Please submit both the deployed Github.io link to your homework AND the link to the Github Repository! ## Instructions * Create an app that accomplishes the following: 1. Whenever a user visits your site, the app should scrape stories from a news outlet of your choice and display them for the user. Each scraped article should be saved to your application database. At a minimum, the app should scrape and display the following information for each article: * Headline - the title of the article * Summary - a short summary of the article * URL - the url to the original article * Feel free to add more content to your database (photos, bylines, and so on). 2. Users should also be able to leave comments on the articles displayed and revisit them later. The comments should be saved to the database as well and associated with their articles. Users should also be able to delete comments left on articles. All stored comments should be visible to every user. * Beyond these requirements, be creative and have fun with this! ### Tips * Go back to Saturday's activities if you need a refresher on how to partner one model with another. * Whenever you scrape a site for stories, make sure an article isn't already represented in your database before saving it; we don't want duplicates. * Don't just clear out your database and populate it with scraped articles whenever a user accesses your site. * If your app deletes stories every time someone visits, your users won't be able to see any comments except the ones that they post. ### Helpful Links * [MongoDB Documentation](https://docs.mongodb.com/manual/) * [Mongoose Documentation](http://mongoosejs.com/docs/api.html) * [Cheerio Documentation](https://github.com/cheeriojs/cheerio) ### Reminder: Submission on BCS * Please submit both the deployed Github.io link to your homework AND the link to the Github Repository! --- ### Minimum Requirements Attempt to complete homework assignment as described in instructions. If unable to complete certain portions, please pseudocode these portions to describe what remains to be completed. Hosting on Heroku and adding a README.md are required for this homework. In addition, add this homework to your portfolio, more information can be found below. --- ### Hosting on Heroku Now that we have a backend to our applications, we use Heroku for hosting. Please note that while **Heroku is free**, it will request credit card information if you have more than 5 applications at a time or are adding a database. Please see [Heroku’s Account Verification Information](https://devcenter.heroku.com/articles/account-verification) for more details. --- ### Create a README.md Add a `README.md` to your repository describing the project. Here are some resources for creating your `README.md`. Here are some resources to help you along the way: * [About READMEs](https://help.github.com/articles/about-readmes/) * [Mastering Markdown](https://guides.github.com/features/mastering-markdown/) --- ### Add To Your Portfolio After completing the homework please add the piece to your portfolio. Make sure to add a link to your updated portfolio in the comments section of your homework so the TAs can easily ensure you completed this step when they are grading the assignment. To receive an 'A' on any assignment, you must link to it from your portfolio. --- ### One Last Thing If you have any questions about this project or the material we have covered, please post them in the community channels in slack so that your fellow developers can help you! If you're still having trouble, you can come to office hours for assistance from your instructor and TAs. That goes threefold for this week: MongoDB and Mongoose compose a challenging data management system. If there's anything you find confusing about these technologies, don't hesitate to speak with someone from the Bootcamp team. **Good Luck!**

Background Over a period of nine years in deep space, the NASA Kepler space telescope has been out on a planet-hunting mission to discover hidden planets outside of our solar system. To help process this data, you will create machine learning models capable of classifying candidate exoplanets from the raw dataset. In this homework assignment, you will need to: Preprocess the raw data Tune the models Compare two or more models Instructions Preprocess the Data Preprocess the dataset prior to fitting the model. Perform feature selection and remove unnecessary features. Use MinMaxScaler to scale the numerical data. Separate the data into training and testing data. Tune Model Parameters Use GridSearch to tune model parameters. Tune and compare at least two different classifiers. Reporting Create a README that reports a comparison of each model's performance as well as a summary about your findings and any assumptions you can make based on your model (is your model good enough to predict new exoplanets? Why or why not? What would make your model be better at predicting new exoplanets?). Resources Exoplanet Data Source Scikit-Learn Tutorial Part 1 Scikit-Learn Tutorial Part 2 Grid Search Hints and Considerations Start by cleaning the data, removing unnecessary columns, and scaling the data. Not all variables are significant be sure to remove any insignificant variables. Make sure your sklearn package is up to date. Try a simple model first, and then tune the model using GridSearch. When hyper-parameter tuning, some models have parameters that depend on each other, and certain combinations will not create a valid model. Be sure to read through any warning messages and check the documentation Submission Create a Jupyter Notebook for each model and host the notebooks on GitHub. Create a file for your best model and push to GitHub Include a README.md file that summarizes your assumptions and findings. Submit the link to your GitHub project to Bootcamp Spot. Ensure your repository has regular commits (i.e. 20+ commits) and a thorough README.md file

Bootcamp Assignment 1

GStar Bootcamp - Assignment 1

Bootcamp Assignment #1 Repo

Codepath Bootcamp Assignment 1

JavaScript BootCamp - Assignment 1

Chainshot bootcamp Assignment 1

Assignment 1.0 PHP BOOTCAMP

UCI Bootcamp Homework Assignment #1

Bootcamp homework 1 - easier assignment

This repo which contains all the assignments assigned to me by 100xSchool Bootcamp-2026.

Data Bootcamp Assignment - 1 (Excel)

AI Bootcamp Week 1 Day 3 assignments

MIE Mobile iOS Bootcamp Assignment 1

All assignments for the CodePath bootcamp, weeks 1 - 6

Problem 1 in GStar Bootcamp Assignment 2

Rice Bootcamp Module Assignment 1: Run Buddy

Kodluyoruz Veri Bilimi 102 Bootcamp eğitiminde verilen ödev ve temel uygulamalar yer almaktadır.

repository untuk memenuhi assignment sesi 1 dari bootcamp QA TestAplikasiKamu

This is week 1 hw assignment for Rutgers coding bootcamp.This is a basic wireframe with HTML and CSS