Computer Science

A comprehensive collection of algorithms, interview prep, cloud/GCP utilities, data projects, and hands-on examples. This repo serves as a personal knowledge base and a set of ready-to-run references across computer science topics.

This repository contains organized submodules covering:

• Algorithms & Data Structures - Python implementations and practice problems
• Interview Preparation - CTCI, LeetCode, Codility solutions
• Data Science Projects - Visualization and mining projects with comprehensive documentation
• Cloud & DevOps - GCP, Terraform, Kubernetes, Cloud Run implementations
• Databases - PostgreSQL, NoSQL, and big data coursework
• Web Development - Django applications and utilities
• Security & Networking - VPN, infosec, and security tools
• System Design - Distributed systems and architecture patterns

• algorithms/ - Comprehensive algorithm implementations
  • leetcode/ - LeetCode problem solutions
  • codility/ - Codility coding challenges
  • ctci/ - Cracking the Coding Interview solutions
  • DynamicProgramming/ - DP algorithm implementations
  • Backtracking/ - Backtracking algorithm examples
  • dsap/ - Data Structures and Algorithms practice
  • interview/ - Interview-specific problems and tips
  • mathProblems/ - Mathematical problem solutions
  • pythonic_stuff/ - Python-specific implementations

• data-visualization/ - Data visualization course projects
  • assignments/ - Course assignments with comprehensive documentation
  • documentation/ - Course materials and guides
  • final-project/ - Adult income analysis project
  • Interactive runner script for easy execution
• data-mining/ - Machine learning projects for diabetes management
  • datasets/ - CGM and insulin data files
  • projects/ - Meal detection, time series analysis, clustering validation
  • documentation/ - Project guides and methodology
  • Interactive runner script for easy execution

• gcp/ - Google Cloud Platform implementations
  • app-engine/ - App Engine applications
  • cloudrun/ - Cloud Run services
  • cloudfunctions/ - Cloud Functions
  • kubernetes/ - GKE and Kubernetes configurations
  • iam/ - Identity and Access Management
  • networks/ - VPC, VPN, and networking
  • storage/ - Cloud Storage implementations
  • billing/ - Billing and cost management
  • security/ - Security configurations
  • profiling/ - Performance profiling tools
  • ai-ml/ - AI/ML implementations
    • vertex-ai-mlops/ - Comprehensive Vertex AI MLOps workflows and examples
• terraform/ - Infrastructure as Code
  • Infrastructure templates and modules
  • State management and dependencies
• canary-bg/ - Canary deployment implementations
  • Cloud Run deployment strategies
  • Traffic management and rollback procedures
• grafana_prometheus/ - Monitoring and observability
  • Grafana dashboards and Prometheus configurations

• databases/ - Database coursework and implementations
  • postgres/ - PostgreSQL assignments and projects
  • bigData/ - Big data analysis projects
  • unQLlite/ - NoSQL database implementations

• Django/ - Django web applications
  • SSN security project
  • Web application templates and utilities

• infosec/ - Information security projects
  • Security tools and implementations
• kali/ - Kali Linux security tools
  • Penetration testing and security utilities
• open-vpn/ - VPN implementations
  • Terraform configurations for VPN setup

• python-grpc/ - gRPC implementations
  • Microservices communication patterns
• gws/ - Google Workspace APIs
  • Directory API, Reports API, Data Transfer API
• sre-interview-prep-guide/ - Site Reliability Engineering
  • Interview preparation materials and guides

• google-interview-university/ - Google Interview University
  • Comprehensive interview preparation guide
• machine-learning/ - Machine learning resources
  • Supervised and unsupervised learning materials
• pythonicStuff/ - Python best practices
  • Pythonic patterns and utilities
• ctci/ - Cracking the Coding Interview
  • Chapter-wise solutions and implementations

• quality-of-life/ - Quality of Life application
  • Modern health and wellness platform with AI mentorship
  • React, TypeScript, Tailwind CSS web application
  • Medical appointment booking and educational content

# Recommended: clone with submodules
git clone --recurse-submodules git@github.com-personal:mkmad/computer-science.git
cd computer-science

# If already cloned without submodules
git submodule update --init --recursive

# Optional: use a specific Python version if defined
pyenv install -s $(cat .python-version 2>/dev/null || echo 3.11.0)
pyenv local $(cat .python-version 2>/dev/null || echo 3.11.0)

# Install dependencies for specific projects
cd data-visualization && pip install -r requirements.txt
cd ../data-mining && pip install -r requirements.txt

• Purpose: Course assignments and final project for data visualization
• Structure: Organized into assignments, documentation, and final project
• Quick Start:

  cd data-visualization
  pip install -r requirements.txt
  python run_examples.py

• Purpose: Machine learning projects for diabetes management
• Structure: Datasets, projects (meal detection, time series, clustering), documentation
• Quick Start:

  cd data-mining
  pip install -r requirements.txt
  python run_examples.py

• Purpose: Google Cloud Platform implementations and utilities
• Key Areas: App Engine, Cloud Run, Kubernetes, IAM, Networking, Security, AI/ML
• Usage: Each subdirectory contains specific GCP service implementations
• Submodules:
  • ai-ml/vertex-ai-mlops/ - Comprehensive Vertex AI MLOps workflows and examples

• Purpose: Infrastructure as Code templates
• Usage: Deploy infrastructure using Terraform configurations

• Purpose: Canary deployment strategies for Cloud Run
• Usage: Traffic management and gradual rollouts

• Purpose: Comprehensive Vertex AI MLOps workflows and examples
• Content: AutoML, BigQuery ML, scikit-learn, TensorFlow, PyTorch, XGBoost, R implementations
• Usage: End-to-end machine learning operations on Google Cloud Platform
• Quick Start:

  cd gcp/ai-ml/vertex-ai-mlops
  # Follow the setup instructions in the README

• Purpose: Modern health and wellness platform
• Content: AI health mentorship, medical appointment booking, educational content
• Tech Stack: React, TypeScript, Tailwind CSS, Framer Motion
• Quick Start:

  cd quality-of-life
  npm install
  npm start

• Purpose: Comprehensive algorithm implementations and practice
• Coverage: LeetCode, Codility, CTCI, Dynamic Programming, Backtracking
• Usage: Reference implementations and practice problems

• Purpose: Cracking the Coding Interview solutions
• Structure: Chapter-wise organized solutions
• Usage: Interview preparation and algorithm practice

This repository uses Git submodules extensively. Here are the key submodules:

• google-interview-university/ - Interview preparation guide
• machine-learning/ - ML resources and materials
• algorithms/ - Algorithm implementations
• ctci/ - CTCI solutions
• pythonicStuff/ - Python best practices

• data-visualization/ - Data visualization projects
• data-mining/ - Machine learning projects

• gcp/ - Google Cloud Platform implementations
  • ai-ml/vertex-ai-mlops/ - Vertex AI MLOps workflows
• terraform/ - Infrastructure as Code
• canary-bg/ - Canary deployments
• grafana_prometheus/ - Monitoring and observability

• databases/ - Database coursework
• Django/ - Django web applications

• infosec/ - Information security
• kali/ - Security tools
• open-vpn/ - VPN implementations

• python-grpc/ - gRPC implementations
• gws/ - Google Workspace APIs
• sre-interview-prep-guide/ - SRE materials

• quality-of-life/ - Quality of life application
• distributed-systems/ - Distributed systems patterns

• Add a submodule

git submodule add <remote_url> <destination_folder>
git commit -m "Add submodule"

• Update/initialize submodules

git submodule update --init --recursive

• Update submodules to latest remote commits

git submodule foreach git fetch --all
git submodule foreach git checkout main || true
git submodule foreach git pull --ff-only || true

• Remove a submodule

# 1) Edit .gitmodules and remove the entry
# 2) Stage the change
git add .gitmodules
# 3) Remove from git config
# 4) Untrack the submodule
git rm --cached path_to_submodule
# 5) Remove the submodule's git dir
rm -rf .git/modules/path_to_submodule
# 6) Commit and delete working tree files
git commit -m "Remove submodule"
rm -rf path_to_submodule

• Manage nested submodules (e.g., submodules within submodules)

# Navigate to the parent submodule
cd gcp
# Update the nested submodule
git submodule update --remote ai-ml/vertex-ai-mlops
# Commit and push changes
git add ai-ml/vertex-ai-mlops
git commit -m "Update nested submodule"
git push origin main

• Total Submodules: 20+ organized repositories
• Categories: 8 major areas of computer science
• Languages: Python, JavaScript, TypeScript, Terraform, SQL, R
• Cloud Platforms: Google Cloud Platform, AWS (via Terraform)
• Data Science: Visualization, Mining, Machine Learning
• Architecture: Microservices, Distributed Systems, MLOps

• Keep edits scoped and documented in the relevant folder README.md
• Match existing code style and avoid noisy reformatting
• Prefer small, focused commits with clear messages
• Update submodule references when making changes to submodules

Educational and personal reference material. Respect licenses of third-party materials and datasets included within subfolders.