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Computational Mathematics Course

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

What will I learn?

Gain practical skills in numerical methods for interpolation, integration, optimisation, and error analysis with time-series data. Clean datasets, model performance using splines and regression, optimise gradient techniques, evaluate stability, and deliver reproducible results through clear metrics and visuals.

Elevify advantages

Develop skills

  • Control numerical errors by detecting, measuring, and minimising round-off and truncation issues.
  • Master interpolation techniques to develop stable polynomial and spline models for time-series data.
  • Apply efficient integration methods like trapezoid and Simpson rules to practical effort data.
  • Implement and tune gradient descent algorithms for effective regression and optimisation.
  • Streamline data preparation by quickly cleaning, aligning, and validating time-series datasets.

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.
Workload: between 4 and 360 hours

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