Computational Mathematics Course
This course equips you with essential computational math tools for real data analysis, covering interpolation, numerical integration, gradient-based regression, error analysis, stability assessment, and experiment design to produce reliable, interpretable models. You'll work with time-series data to build practical skills in modeling, optimization, and visualization.

4 to 360h flexible workload
certificate valid in your country
What will I learn?
Gain hands-on skills in computational mathematics using time-series data: perform numerical interpolation and integration, optimize with gradient methods, analyze errors and stability, clean datasets, apply splines and regression for modeling, and create reproducible visualizations with clear metrics.
Elevify advantages
Develop skills
- Control numerical errors by detecting, quantifying, and minimizing round-off and truncation issues.
- Master interpolation techniques to create stable polynomial and spline models for time-series data.
- Apply efficient integration methods like trapezoid and Simpson rules to real-world data.
- Implement and tune gradient descent methods for regression analysis and optimization.
- Develop fast workflows for cleaning, aligning, and validating time-series datasets.
Suggested summary
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