Industrial Mathematics

Please note

This document only provides information for the academic year selected and does not form part of the student contract

Awarding Institution

University of Huddersfield

Final Award

MSc Master of Science

Teaching Institution

University of Huddersfield

School

School of Computing and Engineering

Department

Department of Computer Science

Subject Benchmark Statement

PG Mathemematics, Statistics and Operational Research (2019)

Date of Programme Specification Approval

2023-11-23

Version Number

2023.02

Educational Aims of the Course

The MSc in Industrial Mathematics is designed to meet the requirements of the modern knowledge economy for employees with both the mathematical skills needed for the modelling of industrial problems and processes and the communication and teamwork skills needed to be able to apply these skills in as part of a cross-functional or interdisciplinary team. Thus, in addition to deep knowledge of the mathematical tools and techniques needed to model industrial systems, graduates of this course will also develop the communication skills needed to understand the requirements of the problem owner and work with them to formulate a description true to the actual need and amenable to mathematical analysis. After the analysis has been carried out graduates of this course will also have the skills needed to communicate results in the context of the original problem, e.g. as actionable recommendations.

Programme graduates will be able to work as mathematicians and mathematical modellers in industry, either working independently or embedded in cross functional teams and this is envisaged as the main destination of graduates of the course. However, the skillset taught by this degree will also qualify graduates to go into academic research via a PhD qualification.

The course is aimed primarily at recent graduates of quantitative degrees (Mathematics, Physical Sciences, Computer Sciences and Engineering) who want to upskill in the area of mathematics and mathematical modelling. It would suit candidates interested in the broad area of data science but interested in the type of problem where a purely statistical approach will not work and modelling of underlying physical, chemical or biological mechanisms is needed.

The main aims of the course are:

To provide excellent technical knowledge and skills in the mathematical tools and techniques needed for mathematical modelling, both analytical and numerical, of industrial problems
To impart the skills needed to develop and evaluate logical arguments, including but not limited to those invoking mathematical concepts and principles.
To develop expertise in all stages of mathematical modelling: abstracting the essentials of problems into a form amenable to mathematical analysis, carrying out that analysis and interpreting the results in terms of the original problem, e.g., as actionable recommendations.
To provide training in the excellent communication and teamwork skills needed to work in a cross functional or multidisciplinary team.
To develop the skills needed to work independently, including the self-awareness needed to recognise strengths and when to ask for help. This should be demonstrated in particular by the planning and execution of an advanced project in the area of industrial mathematics.

Course Offering(s)

Full Time

Full Time - January

Full Time - September

Learning Outcomes

On successful completion you will be able to:

Have a systematic understanding of general principles and techniques appropriate to industrial applications of mathematics.

A comprehensive knowledge of calculus, differential equations (ordinary and partial), linear algebra and continuum mechanics.

A systematic understanding of the mathematical modelling of real-world problems and systems.

An insightful understanding of the implementation and use of numerical methods in developing approximate solutions.

A good understanding of computing principles, software processes and the uses and capabilities of computer languages and packages including MATLAB, R and Python.

An understanding of mathematical reasoning including the process by which mathematical proofs are constructed.

A professional mathematician’s understanding of codes of practice and the ethical conventions that underpin practice.

Develop general and critical abilities of an intellectual, logical, analytical, creative and problem-solving nature.

Show good judgement in the selection and application of mathematical tools and techniques to solve unstructured ”real world” problems.

10.

Ability to develop mathematical models by abstracting the essentials of problems, formulating them mathematically, obtaining solutions by appropriate methods and interpreting these solutions.

11.

Evaluate the correctness and reliability of numerical and analytical solutions.

12.

Exercise critical judgement in searching, appraising and evaluating literature from a variety of sources and synthesizing the results of a literature review.

13.

Competently plan and execute an advanced project themed in mathematics and report on it.

14.

Reflectively formulate mathematical models to address specific complex problems or requirements and solve or analyse those models using appropriate analytic or numerical techniques.

15.

Give critical advice on the use of a given mathematical model, for a given situation.

16.

Use computer packages to solve problems in mathematics, critically evaluating their results.

17.

Use professional judgment in the selection and use of industry standard software for specific purposes.

18.

Confident and accurate communication of arguments and conclusions in appropriate forms.

19.

Use and write software to process data for simulation and analysis.

20.

Quantitative understanding and interpreting data before and after processing.

21.

Use problem solving techniques to formulate appropriate problem solving strategies.

22.

Improve learning and performance by building on previous experience in order to generalize ideas and skills.

23.

The ability to work competently and independently, to be aware of own strengths and weaknesses, and knowing when to ask for help.

Course Structure

Masters Level

Masters Core

Masters Compulsory

Masters Optional

Interim Award

Postgraduate Certificate

Postgraduate Diploma

Teaching, Learning and Assessment

A variety of teaching and learning strategies are used to enable students to acquire the knowledge and skills required by the Course. Which ones are deployed depends on the nature of the material being considered. Staff will also take into account the individual learning styles of the students (identified formally during the Induction period) and then challenge them to experience learning in this, and different, ways. Whatever the learning strategy(-ies) adopted in a particular module and the timetabled contact hours, it is expected that a 15-credit module will occupy a FT student for up to 150 hours during the period of the module.

Typically, lectures are used as a mechanism to provide key facts, concepts, theories and methodologies. These are usually backed up by tutorial and/or practical sessions. These sessions allow students to develop their skills in the areas being considered, to receive immediate feedback on their progress and to take charge of their own learning.

Use of IT resources in teaching is made across the full range of subjects. This may be in the form of online learning materials, workbooks and interactive demonstrations and examples. This type of learning environment allows students to take greater control of their own learning and allows study to take place according to each student’s schedule. However, this type of learning is provided in addition to timetabled sessions where attendance is compulsory. Students are introduced to the University and Departmental systems for Computing & IT during the Induction period.

Formative assessment is seen as an important part of the learning process and may be provided in a variety of ways. Whenever possible, students will be given individual feedback on their progress prior to formal assessment. This feedback may be in the form of verbal comments on work reviewed in a tutorial, seminar or workshop session, or written feedback posted on the VLE, or written feedback on a piece of work submitted prior to assessment. It is important to understand that formative assessment is a student driven process. Students are not required to submit work for formative assessment but must do so if they desire this level of feedback.

Assessment is used to determine if students have achieved the learning outcomes of individual modules and hence, the learning outcomes of the Course. Several forms of assessment are used. In all cases, assessment takes place under the regulations set down in the University’s Regulations for Awards and repeated in the Students’ Handbook of Regulations.

Study Plans

Support for Students and their Learning

At course level support is provided as follows:

Supporting documentation is provided, online, in the form of Student Handbooks, Module Handbooks, Programme Specification Documents (PSD) and Module Specification Documents (MSD)
The Course Leader is available to provide guidance on academic progress.
Module tutors are available to help with academic problems during term time, either on campus or through electronic means such as Microsoft Teams, to facilitate support for distance learning students.
All modules and year groups are supported on the Virtual Learning Environment

Criteria for Admission

The University of Huddersfield seeks and encourages applicants in order to widen participation, improve access and apply the principles of equal opportunities. We provide support for applicants who require additional assistance in order to select the right course of study and make a successful transition to studying at University. We encourage local, national and international applications. Further information for International Students can be found on their website.

If you were educated outside the UK, you are required to have International English Language Testing System (IELTS) at a score of 6.0 with a minimum score of 6.0 in writing and a minimum of 5.5 in any single component. For the MSc Industrial Mathematics with Placement course, the minimum for IELTS is 7.0 overall with 6.5 in any single component. If you have alternative qualifications or do not meet the IELTS requirement we also offer a range of Pre-Sessional English Programmes.

The University provides opportunities for the accreditation of prior learning (APL) as stated in section 3 of the Regulations for Awards.

The University’s general minimum entry requirements are specified in Section 1 of the Regulations for Awards.

Every person who applies for this course and meets the minimum entry requirement – regardless of any disability – will be given the same opportunity in the selection process. General advice and information regarding disability and the support the University can give can be found by contacting student services as follows:

Telephone: 01484 472675

Email: disability@hud.ac.uk

Further information is available on the disability services website.

Further advice on the specific skills and abilities needed to successfully undertake this course can be found by contacting the admissions tutor and by visiting our course finder website page.

MSc Industrial Mathematics:

An Honours degree (2:2 or above) in mathematics, physical science, computing, engineering or a related discipline or an equivalent professional qualification.
Applicants are expected to be familiar with and have some aptitude for mathematics and basic statistical concepts and methods
Other qualifications and/or experience that demonstrate appropriate knowledge and skills equivalent to Honours degree level.
Substantial (5+ years) relevant industry experience.

Methods for evaluating and improving the quality and standards of teaching and learning

The School Teaching and Learning Committee, a sub-committee of the University Teaching and Learning Committee, is tasked with implementing the University’s teaching and learning strategy and with fostering innovation in teaching and learning and the dissemination of good practice
The School Board, via the School Teaching and Learning Committee has responsibility for implementing University policy through school-defined procedures.
Periodic school and subject reviews take place on a rolling quinquennial programme and focus inter alia on the arrangements for quality management and enhancement, teaching, learning and assessment, C&IT strategies, the articulation and assurances of standards, external examiner reports and evaluation and links with professional bodies, employers and other external organisations.
The Course Committee is responsible for the monitoring and development of the course or programme, taking account of feedback from staff, students and external examiners. Feedback is sought as follows:
- From students through annual course and module evaluation questionnaires.
- From external examiners through annual reports, course assessment board minutes, assessment moderation reports and informal verbal communication during the year.
The annual evaluation of the course/programme is the responsibility of the School Board. The Course Committee prepares an annual evaluation report comprising reporting and evaluation, informed by feedback from staff, students and external examiners and by statistical data.
Amendments to course/programme and module documents are validated by the School Accreditation and Validation Panel.
A process for peer observation of teaching is in place with the object of enhancing teaching practice and sharing ideas between staff.

Please note

University awards are regulated by the Regulations for Awards (Taught Courses) on the University website.

Quick links to the Regulations for Taught Students, procedures and forms can be accessed on the University website.

Regulation of Assessment

PgCert Industrial Mathematics

The PgCert Industrial Mathematics consists of a programme in which students must acquire the following credits: 60 credits from any of the taught modules available on the programme.

PgDip Industrial Mathematics

The PgDip Industrial Mathematics consists of a programme in which students must acquire the following credits: 120 credits from all the taught modules available on the programme.

Indicators of Quality and Standards

The latest subject review for the subject area that includes this course took place in January 2021. The panel commended the subject area for, among others, the strong links with industry which offers clear benefits for students and the ambitious plans for curriculum development.

Course and Module Catalogue