Physics Data Analysis Course
Dive into authentic physics data handling: preprocess time-series, fit damped oscillators accurately, evaluate uncertainties properly, and assess models using solid statistical methods. Construct publication-ready analyses with cutting-edge Python tools for science and stringent checks, ensuring reliable outcomes for research or professional use.
from 4 to 360h flexible workload
certificate valid in your country
What will I learn?
Gain expertise in precise data analysis through this practical course. Master modelling of damped oscillations, signal processing techniques, filtering methods, and managing 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 succinct reports suitable for publication or scrutiny.
Elevify advantages
Develop skills
- Time-series preparation: swiftly clean, resample, and remove trends from noisy physics datasets.
- Signal analysis: utilise FFT, filters, and wavelets to derive key physical properties.
- Nonlinear model fitting: execute least squares and resilient fits for oscillating systems.
- Error quantification: apply bootstrap, covariance matrices, and Bayesian methods for uncertainty.
- Fit validation: examine residuals, benchmark models, and deliver top-tier analysis 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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