Linear Programming and Optimization for Data Science

Data science is more than just predictive modeling; it is also about making the best possible decisions under strict real-world constraints. Linear programming provides the mathematical framework to find optimal solutions for resource allocation, logistics, and portfolio optimization. This text-based course guides you from foundational mathematical concepts to implementing practical optimization models. Through clear explanations and practical code snippets, you will learn how to translate complex business constraints into linear equations and solve them efficiently. You will gain the skills to bridge the gap between data analysis and strategic decision-making. What you'll learn: - Understand the fundamental components of linear programming, including decision variables, objective functions, and constraints. - Apply the graphical method to solve and understand simple two-variable optimization problems. - Analyze model sensitivity to determine how changes in constraints impact your optimal solutions. - Formulate real-world data science and business problems into clean mathematical models. - Solve complex linear programs programmatically using modern Python optimization libraries like SciPy and PuLP. - Interpret solver outputs and shadow prices to make informed, data-driven recommendations. The course begins with core definitions and the mathematical foundations of linear systems before moving into graphical solutions. You will then progress to writing clean code, translating business scenarios into Python-based optimization models, and analyzing the results. This course is designed for aspiring data scientists, business analysts, and programmers who want to expand their analytical toolkit. No prior optimization experience is required, and all concepts are introduced from the ground up. Start optimizing your data-driven decision-making today.