This chapter offers a hands-on experience, demonstrating how to implement multiple optimization techniques using Python, a popular programming language for data scientists and engineers. The goal is to demonstrate how to use existing implementations of techniques seen in previous chapters, such as Linear Programming for production planning, Mixed-Integer Linear Programming for the knapsack problem, and Non-Linear Programming for engineering design and data-driven tasks. Solutions for Multi-Objective Optimization problems, such as truss and heat exchanger design, classifier tuning, and portfolio optimization, are also implemented. Additionally, this chapter covers Hyperparameter Optimization for machine learning models and multi-criteria decision-making using data visualization tools and mathematical techniques like TOPSIS. The goal is to show how to apply the technical concepts from previous chapters to real-world implementations using tools common in academia and industry.