Computational Mathematics Course
Gain expertise in essential computational mathematics techniques for handling real data, covering interpolation, numerical integration, gradient-based optimisation, error analysis, stability checks, and structured experimental design to create dependable and understandable mathematical models that support reliable decision-making.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
This practical course in computational mathematics equips you with skills in numerical methods like interpolation, integration, optimisation, and error analysis using real-world time-series data. You'll prepare datasets, apply spline and regression models to weekly data, refine gradient techniques, evaluate stability, and deliver reproducible results through clear visuals and metrics.
Elevify advantages
Develop skills
- Master numerical error control by identifying, measuring, and minimising round-off and truncation errors.
- Develop strong interpolation skills using stable polynomials and splines for time-series modelling.
- Apply efficient numerical integration methods such as trapezoidal and Simpson's rules to practical data.
- Implement and optimise gradient descent methods for regression analysis and performance tuning.
- Establish a streamlined data preparation process to clean, align, and validate time-series datasets quickly.
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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