Computational Mathematics Course
This course teaches key computational mathematics techniques for real-world data analysis. You will master interpolation, numerical integration, gradient-based optimisation, error control, stability checks, and designing reliable experiments to develop trustworthy mathematical models that are easy to understand and reproduce.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
Gain practical skills in computational maths using time-series data. Learn numerical interpolation, integration, optimisation, and error analysis. Clean datasets, model performance with splines and regression, tune gradient methods, check stability, and create clear visual results with solid metrics.
Elevify advantages
Develop skills
- Control numerical errors by spotting, measuring, and cutting down round-off and truncation issues.
- Master interpolation to create steady polynomial and spline models for time-series data.
- Use efficient integration methods like trapezoid and Simpson rules on actual performance data.
- Apply and fine-tune gradient descent methods for regression and troubleshooting.
- 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.What our students say
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