Physics class is taught according to the needs, for example we can take a course in dynamics, kinematics, fluid mechanics, thermodynamics, electromagnetism, vibrations and waves or modern physics, we can even develop a whole mathematics course for example algebra, trigonometry or calculus according to the needs.

Are you an engineering or mathematics (Bachelor's/Master's) student who finds numerical analysis too abstract or difficult to apply? This course provides a rigorous yet practical introduction to scientific computing, showing how numerical methods are used to solve real engineering and scientific problems. You'll first learn how numerical computations behave by studying floating-point arithmetic, rounding and truncation errors, conditioning, stability, and error propagation. These concepts are essential for understanding why numerical algorithms succeed—or fail. Next, you'll learn how to solve nonlinear equations using the bisection method, fixed-point iteration, and Newton's method, before moving on to linear systems through both direct methods (Gaussian elimination, LU decomposition, Cholesky factorization) and iterative methods (Jacobi, Gauss-Seidel, and relaxation methods). The second half of the course covers polynomial interpolation and approximation (Lagrange, Newton, Hermite, least-squares approximation, and splines), numerical differentiation and integration, and numerical methods for ordinary differential equations, including Euler's method, Runge-Kutta methods, and finite difference techniques. Every algorithm is implemented step by step in Matlab so that you not only understand the mathematics behind it but also learn how to apply it to practical engineering problems. Throughout the course, we compare the accuracy, convergence, stability, and computational efficiency of the different numerical methods. This course is ideal for students preparing for university exams, engineering projects, scientific computing courses, or anyone wishing to build solid foundations in numerical analysis. Prerequisites: A good understanding of single-variable calculus, basic linear algebra, and elementary programming concepts is recommended.

The lesson is tailored to your wishes. I can explain the theory from the *Getal & Ruimte* method (or *Moderne Mathematics*). We can go through the exercises you want to review, or simply the exercises in the book. We can review tests or prepare for exams. Together, we will strive for mathematics that you understand.

- Python Programming is currently the most utilized tool in the data analysis world. Its ease-of-use and wide variety of packages/libraries makes it a most valuable skill to have for a student wanting to get started in the world of data analysis and Object Oriented Programming. - This course is geared towards beginners and intermediates who are interested in a hands-on approach to learning without neglecting the scientific background of the covered material, which will include the following: * Introduction to Python Programming * Understanding of OOP concepts and use-cases * Crash course in statistical analysis concepts * The usage of specified analysis libraries like Pandas and Numpy * Introduction to Data science * Neural Networks

Learn Excel in a practical way, focused on real-world work situations. This isn't a theoretical course: from the very first class, you'll work with applied exercises that will help you gain confidence and improve your professional profile. It is aimed at people who want to start from scratch or improve their level of using Excel in their work, studies or personal projects. In classes you will learn to: Master Excel from basic to intermediate/advanced level Use key formulas such as SUM, IF, VLOOKUP/XLOOKUP Create pivot tables to analyze information Clean and organize data efficiently Design clear reports and basic dashboards Applying Excel to real-world business cases The classes are personalized according to your level and goals. We can work with your own files or with practical exercises that simulate real-life situations. My approach is for you to learn quickly, understand what you're doing, and be able to apply it immediately. I'm an Electronics Engineer with experience in data analysis and marketing analytics. I've worked with tools like Excel, Power BI, and RapidMiner on real-world projects, so I can teach you not only "how" but also "why" to use Excel in the workplace.