Physics Data Analysis Course
This course equips you with essential skills for handling real-world physics data. Learn to preprocess time-series data, fit models to damped oscillators, estimate uncertainties accurately, and evaluate models using strong statistical methods. Utilise contemporary Python tools in science to produce analysis ready for academic publishing, complete with thorough checks and diagnostics.
from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
Gain expertise in analysing physics data through practical exercises. You will model damped oscillations, use signal processing techniques and filters, and manage noisy time-series data effectively. Develop skills in nonlinear fitting, uncertainty assessment, model evaluation, and thorough residual checks, while creating repeatable processes, professional charts, and detailed reports suitable for publication or peer review.
Elevify advantages
Develop skills
- Time-series data preparation: quickly clean, resample, and remove trends from noisy physics datasets.
- Signal analysis techniques: implement FFT, filtering, and wavelets to derive key physical properties.
- Advanced model fitting: perform nonlinear least squares and resilient fits for oscillation models.
- Error quantification: apply bootstrapping, covariance matrices, and Bayesian methods for reliable uncertainty measures.
- Fit evaluation: analyse residuals, benchmark models, and deliver top-quality fitting reports.
Suggested summary
Before starting, you can change the chapters and workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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