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Computational Mathematics Course

Computational Mathematics Course
from 4 to 360h flexible workload
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 with real time-series data. Clean and validate datasets, model performance using splines and regression, optimise gradient techniques, evaluate stability, and deliver reproducible results with clear metrics and visuals.

Elevify advantages

Develop skills

  • Master numerical error control by detecting, measuring, and minimising round-off and truncation errors.
  • Develop expertise in interpolation using stable polynomial and spline models for time-series data.
  • Apply efficient integration techniques like trapezoid and Simpson rules to practical datasets.
  • Implement and tune gradient descent methods for effective regression analysis.
  • Streamline data preparation workflows to clean, align, and validate time-series data quickly.

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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