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Computational Mathematics Course

Computational Mathematics Course
flexible workload of 4 to 360h
valid certificate in your country

What will I learn?

Gain practical skills in computational mathematics through numerical methods for interpolation, integration, optimisation, and error analysis on time-series data. Clean and validate datasets, apply splines and regression for modelling, optimise gradient techniques, evaluate stability, and deliver reproducible results with visuals and metrics.

Elevify advantages

Develop skills

  • Control numerical errors by detecting, measuring, and minimising round-off and truncation issues.
  • Master interpolation using stable polynomials and splines for accurate time-series modelling.
  • Perform efficient numerical integration with trapezoidal and Simpson's rules on real data.
  • Optimise gradient descent methods for regression, including tuning and diagnostics.
  • Streamline data preparation by quickly cleaning, aligning, and validating time-series datasets.

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.
Workload: between 4 and 360 hours

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