Scientific Computing stands as a foundational pillar in modern science and engineering, enabling researchers and practitioners to translate complex physical phenomena into solvable mathematical models. At its core, this discipline integrates numerical methods, algorithmic thinking, and computational techniques to address problems that are analytically intractable or practically impossible to solve through traditional means. In an era where data-driven insights and precision modeling drive competitive innovation, mastering Scientific Computing is no longer optional, it is a strategic imperative. This lab is designed to operationalize theoretical knowledge, allowing students to move beyond passive learning and engage with simulations that mirror real-world scientific and engineering challenges. Built around a carefully curated suite of experiments, including root finding for algebraic and transcendental equations, interpolation, numerical differentiation and integration, differential equation solving, eigenvalue computations, regression, curve fitting, and Fourier analysis, the lab offers a holistic and industry-relevant roadmap. Each experiment is intentionally aligned with core numerical principles that have stood the test of time while supporting modern engineering workflows. By engaging with these classical yet continuously evolving computational tools, students develop the capability and confidence to solve scientific problems with accuracy, insight, and computational efficiency.