13 hours, optionally

Syllabus (12 weeks, 2 hrs/week)

• Week 1: Introduction and Python scientific ecosystem – NumPy, SciPy, Matplotlib, Pandas; Jupyter Notebook, script management
• Week 2: Data basics – Loading, filtering, editing and visualizing data (CSV, TXT); graphs and tables
• Week 3: Numerical differentiation and integration – numpy.gradient, trapezoidal and Simpson’s rule; applications: work and energy calculations
• Week 4: Solving differential equations – Euler’s method, Runge–Kutta, solve_ivp; models: damped oscillator, free fall
• Week 5: Linear algebra in practice – Matrices, vectors, inversion, solving systems of equations (numpy.linalg.solve)
• Week 6: Interpolation and approximation – interp1d, polynomial and spline interpolation, applications to experimental data
• Week 7: Fourier transform – FFT basics, spectral analysis, frequency filtering of signals
• Week 8: Numerical simulations of physical processes – 1D particle motion, oscillation, heat transfer – creation of simple models
• Week 9: Numerical simulation of 2D particle motion – ballistic curve
• Week 10: Visualization and animation – 3D graphs, animation with FuncAnimation, visualization of trajectories and fields
• Week 11: Project programming and OOP – Structure of a larger program, modules, functions, working with data sets
• Week 12: Mini-projects and summaries – Presentation of simulations or models, discussion, final summary of methods

Learning outcomes
Student:
- effectively uses NumPy, SciPy, Matplotlib libraries
- can solve differential equations and integrals numerically,
- simulates simple physical processes,
- prepares data visualizations and animations,
- manages to structure code and organize a project in Python.