KL3014 - Modelling

During the first half of the module, you will learn through a series of formal sessions, including both lectures and seminars.

During lectures, main mathematical and statistical techniques with suitable applications/examples are introduced. Seminars

allow you to gain experience in applying the techniques introduced in lectures through working on case studies. The second

half of the module consists of two modelling exercises, either mathematical or statistical, based on loosely-defined scenarios.

These modelling activities are carried out as tutor guided independent learning, allowing you to gain experience of individual

and team work. Students are divided in groups and given modelling tasks to carry out, under the supervision of the lecturer,

who monitors their progress. The first modelling exercise is formative, giving you experience in working in groups and using

the necessary transferable skills to present your findings, and will include substantive feedback to support the second

exercise which is summatively assessed.

Direct contact with the teaching team during the formal sessions will involve attendance of lectures and seminars, and

participation in both general class discussions as well as one-to-one discussions during the hands-on part of the more formal

sessions. This gives you a chance to get immediate feedback pertinent to your particular needs in this session. Further

feedback and discussion with the teaching team are also available at any time through our open door policy. In addition,

teaching materials and supplementary material (such as relevant journal articles and news) are available through the e-

learning portal.

All modules at Northumbria include a range of reading materials that students are expected to engage with. Online reading lists (provided after enrolment) give you access to your reading material for your modules. The Library works in partnership with your module tutors to ensure you have access to the material that you need.

Knowledge & Understanding: · MLO1: Solve a variety of modelled problems by various standard mathematical and statistical approaches. Intellectual / Professional skills & abilities: · MLO2: Develop the necessary skills and confidence to model and solve problems ‘from scratch’ using various mathematical and statistical techniques; · MLO3: Formulate simple mathematical or statistical models, solve using standard techniques, and present results in an attractive and explanatory fashion. Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA): · MLO4: Work effectively as a member of a group.

SUMMATIVE 1. Individual assignment (50%) – MLO1, MLO2, MLO3. Max length: 2000 words or equivalent. 2. Group assignment (group report (2000 words max) and a short multimedia report (10 minutes max in duration)) on a case study, with peer assessment (50%) – MLO1, MLO2, MLO3, MLO4 FORMATIVE Formative assessment will be available on a weekly basis in the formal sessions through normal lecturer-student interactions, allowing them to extend, consolidate and evaluate their knowledge. Formative feedback will be provided on student work and errors in understanding will be addressed reactively using individual discussion. Solutions for laboratory tasks will be provided after the students have attempted the questions, allowing students to receive feedback on the correctness of their solutions and to seek help if matters are still not clear.

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The ability of modelling real-life phenomena occurring in time and space, so to provide a quantitative explanation of causes and to allow predictions within a certain degree of confidence (and possibly control by introducing a convenient set of parameters), is a core skill for anyone working in mathematical sciences, and one of the reasons for the high employability of mathematicians and statisticians. This module aims at providing the basic principles of mathematical and statistical modelling, giving the opportunity to practice modelling techniques on real-life problems. Through a combination of traditional lectures, seminar classes and, most notably, group activities, modern mathematical and statistical concepts and methodologies will be applied to describe phenomena and events in a quantitative manner, allowing predictions and/or explaining causes. Problems will be solved on paper and utilising programming languages, such as R, MATLAB, Mathematica and Python. You will be assessed via individual and group assignments, covering mathematical and statistical knowledge, as well as your work on case studies. The module is delivered by research-active staff, with relevant professional experience in mathematical and statistical modelling of natural and human phenomena.