Introduction to analytical modelling  

Obligatory base module 2 The Learning outcomes * By the end of this course it is expected that the learners will learn about applied way of combing their knowledge on programming, math and statistics with their biology, material science and bioengineering knowledge. * The learners will develop their programming skills in MATLAB environment and will get familiar with different toolboxes in there. * The learners will develop their critical thinking skills Brief description of content The content of the course consists of three categories that are 1. Using MATLAB in Biology In this category, we use System Biology toolbox of MATLAB for computational biology in order to do these tasks: * Import, analyse, and model data, and share results. * Automate workflow elements. * Customize algorithms and tools critical to developing innovative methods for working with unexplored research areas. * Leverage proven, commercially supported algorithms and tool. 2. Using MATLAB in Bio Engineering In this category, we talk about topics in this field which MATLAB can do them such as: * Types and sources of numerical error * Systems of linear equations * Hypothesis testing * Root finding techniques for nonlinear equations * Numerical quadrature * Numerical integration of ordinary differential equations * Nonlinear data regression and optimization * Basic algorithms of bioinformatics 3. Using MATLAB in Physic and chemistry In this category we talk about some physics and chemistry algorithms that are implemented in MATLAB such as: * Solar systems * Potential and Field. * Waves * Random systems * Determination of the stoichiometric coefficients in a chemical equation.
Presential
English
Introduction to analytical modelling
English

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