Physics Data Analysis Course
This course equips you with essential skills for physics data analysis. Learn to preprocess time-series data, fit models to damped oscillators, evaluate uncertainties using statistical methods, and perform model comparisons. Utilise contemporary Python tools to produce high-quality, publication-ready analyses with comprehensive diagnostics and robust practices.
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 workflows, professional visuals, and detailed reports suitable for publication or peer review.
Elevify advantages
Develop skills
- Time-series preparation: efficiently clean, resample, and remove trends from noisy data.
- Signal analysis: employ FFT, filtering, and wavelets to derive key physical insights.
- Nonlinear model fitting: implement least squares and resilient methods for oscillators.
- Error quantification: apply bootstrapping, covariance matrices, and Bayesian approaches.
- Fit evaluation: analyse residuals, benchmark models, and deliver superior results.
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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