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Graph Traversals and Properties: Concepts, Algorithms, and Implementations

This document elaborates on Graphs, focusing on traversal algorithms (Depth-First Search (DFS) and Breadth-First Search (BFS)), and properties like connected components and cyclic vs. non-cyclic graphs. Each section includes explanations, algorithms visualized with Mermaid diagrams (using correct syntax), and JavaScript implementations with examples.

Graphs Overview

A Graph is a data structure consisting of nodes (vertices) connected by edges. Graphs can be:

• Directed (edges have direction) or Undirected (edges are bidirectional).
• Weighted (edges have weights) or Unweighted (edges have no weights).
• Cyclic (contains cycles) or Acyclic (no cycles).

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Binary Tree Traversals: Concepts, Algorithms, and Implementations

This document explains Binary Tree Traversals, including Breadth-First Traversal (BFT) and Depth-First Traversal (DFT) variants: Pre-Order, In-Order, and Post-Order. Each traversal is described with its algorithm, visualized using Mermaid diagrams, and implemented in JavaScript with example usage.

Binary Tree Traversals Overview

Binary Tree Traversals are systematic ways to visit all nodes in a binary tree. They are categorized into:

• Breadth-First Traversal (BFT): Visits nodes level by level, from left to right.
• Depth-First Traversal (DFT): Explores as far as possible along each branch before backtracking, with three variants:
• Pre-Order: Visit root, then left subtree, then right subtree.

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Advanced Binary Trees: Variations and Balancing

This document elaborates on Binary Trees, focusing on advanced variations such as Red-Black Trees and techniques for balancing. It explains the concepts, properties, and JavaScript implementations for Binary Search Trees (BSTs), AVL Trees, and Red-Black Trees, including balancing mechanisms.

Binary Search Trees (BST)

Concept

A Binary Search Tree is a binary tree where each node has a value, and for any node, all values in the left subtree are less than the node's value, and all values in the right subtree are greater. BSTs enable efficient searching, insertion, and deletion with an average time complexity of O(log n) when balanced, but O(n) in the worst case (e.g., skewed trees).

Implementation

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Sorting Algorithms: JavaScript Implementations

This document provides implementations of common sorting algorithms in JavaScript, including Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, Quick Sort, and Heap Sort. Each section explains the algorithm briefly and provides a JavaScript class implementation with example usage.

Bubble Sort

Concept

Bubble Sort repeatedly steps through the list, compares adjacent elements, and swaps them if they are in the wrong order. It continues until no swaps are needed. Time complexity: O(n²).

Implementation

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Data Structures and Algorithms: Core Concepts and JavaScript Implementations

This document covers fundamental data structures and algorithms, including Binary Trees, Graphs, Stacks, Queues, Linked Lists, Hash Tables, Sorting, and an introduction to Recursion. Each section explains the concept and provides a JavaScript class implementation with example usage.

Binary Trees

Concept

A Binary Tree is a hierarchical data structure where each node has at most two children, referred to as the left child and the right child. It is commonly used for searching, hierarchical data representation, and efficient insertion/deletion operations. Key properties include:

• Root: The topmost node.
• Leaf: A node with no children.

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Bandersnatch Game Plan

This document outlines the plan for adding a browser-based text adventure game to the BandersnatchStarter project (https://github.com/BloomTech-Labs/BandersnatchStarter). The game will be a dungeon crawler with minimal graphics, integrated into the existing Flask application. It will use a Player class for stats and state, a MongoDB collection for rooms, randomly spawned monsters from the existing database as enemies, a text-based input/output system, and simple battle mechanics (fight or run). The game will be implemented in a game.py file and integrated into the Flask app.

Objective

Create a text-based adventure game where players navigate dungeon rooms, encounter monsters, and engage in simple battles. Each room will display a text description and a static image, with player input driving navigation and combat. The game will leverage the existing MongoDB database for monster data and extend it with a rooms collection, integrating seamlessly with the Flask app’s structure.

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BandersnatchStarter Project Overview for TypeScript/JavaScript Engineers

This document provides an overview of the BandersnatchStarter project, tailored for a TypeScript or JavaScript engineer familiar with web development (e.g., Node.js, Express, React) but new to Python and Flask. The goal is to help you understand the project’s structure, technical stack, key files, and concepts, mapping them to JavaScript equivalents where possible. The project is a Flask-based web application for managing monster data, creating visualizations, and building machine learning models, organized into sprints.

Project Overview

BandersnatchStarter is a Python web app that combines data science and machine learning to work with "monster data." It involves setting up a MongoDB database, rendering interactive visualizations with Altair, and building a scikit-learn machine learning model. For a JavaScript engineer, think of it as a Node.js/Express app with MongoDB, but using Python/Flask and Python-specific libraries for da

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BandersnatchStarter Project Overview

This document provides an overview of the BandersnatchStarter project, designed for someone with minimal Python experience, primarily familiar with Jupyter notebooks in a data science context. The goal is to help you understand the project’s structure, technical stack, key files, and concepts, along with pointers to resources for learning. The project is a Flask-based web application for working with monster data, creating visualizations, and building machine learning models. It’s structured as a series of sprints to guide you through the development process.

Project Overview

The BandersnatchStarter project is a data science and machine learning application focused on "monster data." It involves setting up a database, creating interactive visualizations, and building a machine learning model. The project is beginner-friendly for those with notebook experience, as it uses familiar Python libraries like pandas and scikit-learn, but introduces web development concepts

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AWS RDS Instance Provisioning Guide

This guide provides step-by-step instructions to provision an Amazon RDS instance in AWS for MySQL or PostgreSQL, configure it, and prepare it for deploying applications.

Prerequisites

• An AWS account with appropriate IAM permissions (e.g., AmazonRDSFullAccess).
• Basic knowledge of AWS Management Console, VPC, and database concepts.
• AWS CLI installed and configured (optional for CLI-based setup).

Step 1: Log in to AWS Management Console

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Python Problem Sets for Learning Problem Solving and Software Development

Welcome to your Python problem sets! These exercises will help you build real-world coding skills used by software developers. Each problem focuses on a specific skill, like parsing logs or processing data, and is designed to be solved in about 20 minutes. You'll get a detailed explanation, starter code, and test cases to guide you. Let's get started!

Problem Set 1: Log File Parser

Why This Matters

Logs are like a diary for apps, recording what happens (e.g., errors or user actions). Developers often need to parse log files to extract useful information, like timestamps or error messages.

Your Task

Write a function parse_log_entry that parses a log entry string with the format "[TIMESTAMP] LEVEL: MESSAGE". Extract the timestamp, level, and message into a dictionary. Return None if the entry is invalid (e.g., wrong level or format).