These tutorials focus on the use of mathematical tools and techniques to model and solve practical problems across science, engineering, finance, and technology:
- Numerical Methods: Practical tutorials on approximation techniques (e.g., Newton's method, numerical integration) used when exact analytical solutions are impossible. Learn to implement these methods using tools like Python.Mathematical Modeling: Step-by-step guidance on translating real-world phenomena (e.g., population growth, fluid dynamics, financial markets) into mathematical equations and interpreting the result
- Probability and Statistics: Essential for data science, covering probability distributions, hypothesis testing, regression analysis, and modeling uncertainty.
- Optimization Techniques: Learning methods like Linear Programming (LP) and calculus-based optimization to find the most efficient solution under constraints.