Mathematical modelling with Python

Course summary

Welcome to this introductory-level course on modelling. This course will guide you through developing a mathematical model for a lake, understanding Python's structure and functionality, and setting up and solving problems using numerical software.

You'll adopt a task-based approach to develop composite models, apply generic modelling procedures to create complex models, and use dependency graphs to analyse intricate systems.

Combining these skills, you’ll tackle a practical problem in medicines manufacturing.

• Course outline

  The main objectives of this course are to:

  • Develop a mathematical model for a simple system, in this case, a lake

  • Understand the structure and functionality of Python

  • Setup the problem on a numerical software package and solve it

  • Understand the task-based approach to developing composite models

  • Apply generic modelling procedures to write your own complex models

  • Use dependency graphs to analyse more complex systems

  • Combine the above learning outcomes to use the knowledge in a practical problem based on medicines manufacturing

  Note: Intro to Python and Intro to Machine Learning should be done as prerequisites to this course.

• Learning outcomes

  Description text goes here

• Target audience

  Engineers and scientists in pharmaceuticals and bioprocessing - Professionals working in medicine manufacturing who need to apply mathematical modelling techniques to optimise processes and improve decision-making.

  Data scientists and computational modellers - Those with a background in Python and machine learning who want to expand their skills in modelling and simulation.

  Professionals transitioning into computation modelling - Those with experience in related fields (e.g. chemical engineering, physics, or biology) who want to gain practical skills in modelling and simulation.

Available now

Format: E-learning

Duration: 1 hour (approx)

Cost: Free

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