Physics Data Analysis Course
Gain expertise in analysing real-world physics data by cleaning time-series, fitting damped oscillators, quantifying uncertainties, and comparing models using robust statistical methods. Develop publishable analyses with modern scientific Python tools and thorough diagnostics, ensuring reproducible workflows and clear reporting for publication or review.

4 to 360 hours flexible workload
valid certificate in your country
What will I learn?
This hands-on course teaches rigorous data analysis for physics, focusing on modelling damped oscillations, signal processing, filtering noisy time-series data, nonlinear fitting, uncertainty quantification, model diagnostics, and robust residual analysis. Build reproducible workflows, create clear figures, and produce concise technical reports suitable for publication or review.
Elevify advantages
Develop skills
- Time-series preprocessing: clean, resample, and detrend noisy physics data quickly.
- Signal processing: apply FFT, filters, and wavelets to extract physical parameters.
- Model fitting: perform nonlinear least squares and robust fits for damped oscillators.
- Uncertainty analysis: use bootstrap, covariance, and Bayesian methods to quantify errors.
- Model diagnostics: test residuals, compare models, and produce high-quality fit reports.
Suggested summary
Before starting, you can change the chapters and the 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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