Textbook in PDF format This practical guide to optimization combines mathematical theory with hands-on coding examples to explore how Python can be used to model problems and obtain the best possible solutions. Presenting a balance of theory and practical applications, it is the ideal resource for upper-undergraduate and graduate students in applied mathematics, data science, business, industrial engineering and operations research, as well as practitioners in related fields. Beginning with an introduction to the concept of optimization, this text presents the key ingredients of an optimization problem and the choices one needs to make when modeling a real-life problem mathematically. Topics covered range from linear and network optimization to convex optimization and optimizations under uncertainty. The book's Python code snippets, alongside more than 50 Jupyter notebooks on the author's GitHub, allow students to put the theory into practice and solve problems inspired by real-life challenges, while numerous exercises sharpen students' understanding of the methods discussed. Covers all the mathematical fundamentals needed to understand how to implement and solve optimization problems, with a good balance between applications and theory Focuses on active learning, with numerous examples, exercises and code samples to build a deeper understanding Employs more than 50 Jupyter notebooks with optimization applications, allowing students to see how the theoretical constructs drive solutions to real-life problems Highlights the impact that uncertainty might have on solutions of optimization problems and teaches various approaches to handle it Explores the choices one needs to make when modeling a real-life problem mathematically