Physics Data Analysis Course
This course equips you with essential skills for real-world physics data analysis. Learn to clean and preprocess time-series data, fit models to damped oscillators, quantify uncertainties using robust statistical methods, and compare models effectively. Utilize modern Python tools to build reproducible analyses with high-quality diagnostics, preparing you to produce publishable scientific work that stands up to rigorous review.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
Gain expertise in handling physics data through practical exercises. You will model damped oscillations, use signal processing techniques and filters, and manage noisy time-series data effectively. Practice nonlinear fitting, assess uncertainties, perform model diagnostics, and conduct thorough residual analysis. Develop reproducible workflows, create clear visualizations, and produce concise technical reports suitable for publication or review.
Elevify advantages
Develop skills
- Time-series preprocessing: efficiently clean, resample, and detrend noisy physics data.
- Signal processing: implement FFT, filters, and wavelets to derive key physical parameters.
- Model fitting: perform nonlinear least squares and robust fitting for damped oscillators.
- Uncertainty analysis: apply bootstrap, covariance matrices, and Bayesian methods for error quantification.
- Model diagnostics: evaluate residuals, compare competing models, and deliver superior 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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