MODULE 5: Statistics and Analysis

In this module you will study statistical data on linear relationships. For this you will use an advanced software package, SPSS. In the Mathematical statistics part of the module, you will learn much of the underlying theory. Alongside that, in the second part of the module, you will continue to develop your knowledge of basic mathematics: Analysis. This is something you already started learning in module 2 in your first year. The reflection course is part of the reflection line.

MODULE 6: Dynamical Systems

Mathematical models often involve differential equations. These models are called dynamic, because they show the relationship between the variables and their changes. In so-called static models, on the other hand, only the variables are represented. In the sixth module you will study dynamic models from the perspective of differential equation, mathematical system theory and numerical aspect, making use of MATLAB mathematical software.

MODULE 7: Discrete Structures and Efficient Algorithms

You will follow this module together with students doing our Bachelor’s programme in Technical Computer Science. You will be studying so-called discrete structures. As opposed to continuous structures, such as a collection of real numbers, these structures are about finite or countable infinite sets and variations thereof. The focus is on calculations within such sets, such as finding the shortest path between a certain number of points and the links between those points. The computability and complexity of these calculations are important factors. You will use elements from abstract algebra, such as groups, rings and fields, but also finite automata and Turing machines.

MODULE 8: Modelling and Analysis of Stochastic Processes for Math

You will follow parts of this module together with students doing our Bachelor’s programmes in Industrial Engineering and Management and Civil Engineering. As far as mathematics is concerned, this module is about Markov chains. These are stochastic models, or models in which outcomes depend on chance and where you go from state to state according to a given probability distribution, regardless of where you stood in the past. These models are used for all sorts of applications, like modelling queues for example. They also play a vital role in modelling and stimulating complex stochastic systems. In your project you will learn how to use such models to make good decisions in practical situations in which change plays an important role, for example, logistics, a hospital environment and traffic.

Take a look at the study programme for the third year.

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