Computational Mathematics Course
This course equips you with essential computational mathematics tools for handling real-world data. Learn interpolation, numerical integration, gradient-based optimisation, error analysis, and stability checks to create reliable mathematical models that are interpretable and reproducible for practical applications.

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.What our students say
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