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

BSc(Hons) Bachelor of Science with Honours

Teaching Institution

University of Huddersfield

School

School of Computing and Engineering

Department

Department of Computer Science

Subject Benchmark Statement

UG Mathematics, Statistics and Operational Research (2019)

Date of Programme Specification Approval

2023-09-26

Version Number

2023.02

Educational Aims of the Course

This course is designed to blend together core topics in mathematics, statistics and operational research to provide students with a rich and diverse learning experience equipping them with the advanced skills and understanding needed to solve a wide range of complex real-world mathematical problems. Within a supportive teaching environment, the… For more content click the Read More button below.

This course is designed to blend together core topics in mathematics, statistics and operational research to provide students with a rich and diverse learning experience equipping them with the advanced skills and understanding needed to solve a wide range of complex real-world mathematical problems. Within a supportive teaching environment, the program will develop problem solving skills, individual and team-working skills and stimulate students’ interests in a wide range of modern applications of mathematics, statistics and operational research.

The course will develop highly numerate, professional mathematicians capable of applying both analytical and numerical skills to determine solutions to a diverse range of problems. By applying intellectual rigour and logical reasoning, alongside more generic skills such as team-working, organisation and communication skills, students will develop skills highly sought after by employers in a range of fields. Typical employment routes include the natural and physical sciences, economics, actuarial science, accountancy, informatics, computer science and engineering. Graduates will also find themselves well equipped to progress on to teacher training or further study at Masters or PhD level.

The main aims of the programme, which are aligned to the QAA Subject Benchmark, are to:

Instil into students an appreciation of the importance of mathematics, statistics and operational research in solving real-world problems and to prepare them for a career in industry.
Develop, through an education in mathematics, statistics and operational research, a range of transferable skills, including scientific computing and IT skills, project management and design skills, problem solving skills, logical thinking and communication skills of value in employment.
Develop, in students, an ability to apply their mathematical knowledge and skills to the solution of a wide range of theoretical and practical problems drawn from industry and from research.
Provide specialised knowledge in specific areas of mathematics, statistics and operational research that align with current, internationally-leading research within the Department.
Through alignment with current, internationally-leading research within the Department, produce students that have a critical awareness of current problems and new insights at the forefront of research. They should be able to evaluate current research and methodologies, develop critiques, show originality in the application of knowledge and, where appropriate, propose new hypotheses.
Provide graduates with the knowledge and skills required for further study and research in mathematics, computing and related areas.
Provide graduates with a high level of skills to identify and use the mathematical tools (including some advanced techniques) required to solve a problem in applications such as Data Science, Artificial Intelligence, Engineering, etc.
Develop, through the application of the mathematical and computational tools to the real-life problems the ability to plan, develop and evaluate the advanced and complex industrial and research projects.

Course Accredited by

Institute of Mathematics and its Applications

Course Offering(s)

Sandwich Year Out

Sandwich Year Out - September

Full Time

Full Time - September

Learning Outcomes

On successful completion you will be able to:

Mathematical methods and techniques of key areas in a) mathematics, b) statistics, and c) operational research, including common ground topics, such as calculus and linear algebra.

Results from a range of major areas of a) mathematics, b) statistics and c) operational research.

How and when mathematical methods from major areas of a) mathematics, b) statistics and c) operational research can be applied.

The role and use of assumptions; the power of generalisation and abstraction and how it can be applied to problem-solving.

A range of modelling techniques, including model validation, model validation and revision, optimisation, and conditions and limitations.

The role of numerical approximation and numerical computing, its application to a wide range of problems, including stability analysis, convergence and error rates and the effects of finite precision arithmetic on the computing process.

Demonstrate knowledge of key topics in areas of a) mathematics, b) statistics and c) operational research, including by application to solve real-life problems.

Analyse a given problem, including real-world problems, in terms of its abstracted components, including any assumptions and constraints, and represent that problem mathematically using appropriate symbolic notation. Grasp how mathematical process may be applied to it.

Select appropriate solution methods to a range of analytically and numerically formulated problems, analyse the quality of the solutions found and present conclusions.

10.

Create, design and execute practical investigations from the problem recognition stage through to the evaluation and appraisal of the results.

11.

Interpret essential facts, concepts, principles and theories, develop arguments and make distinctions and design choices based upon this.

12.

Describe solutions to qualitative and quantitative problems of a familiar and unfamiliar nature and discriminate between different methodologies and approaches.

13.

Interpret experimental results in terms of their statistical significance and underlying theory; present mathematical arguments, using appropriate notation.

14.

Illustrate mathematical and statistical results and conclusions clearly and correctly, in writing and orally, to a variety of audiences and show an ability to both question and exemplify the results and present new hypothesis.

15.

Use computers and software for data processing, problem design, retrieving and interpreting scientific information.

16.

Critically evaluate novel problems and plan and deploy strategies for their solution using techniques which are at the forefront of the discipline. Apply comprehensive and deep understanding of solution processes and methodologies to unfamiliar situations.

17.

Interpersonal skills, including the ability to co-operate with others and work as part of a team and develop an awareness of group dynamics.

18.

Information technology and scientific computational skills, including the use of general and specialist software.

19.

Verbal and written communication skills which show the ability to summarise scientific information and statistical data, interpret results, and compose, present and justify arguments, making the presentation clear for either specialists or non-specialists.

20.

Time management and organisational skills – the ability to initiate, co-ordinate and direct programmes of work and study, including a major research project.

21.

Information retrieval skills, including on-line searches and primary literature research skills.

22.

Study skills for continuing personal development including the ability to apply the above skills in a wide range of mathematical situations and adaptability.

23.

The ability to transfer knowledge from one context to another; the ability to assess problems logically.

Course Structure

Foundation Level

Foundation Core

Foundation Compulsory

Foundation Optional

Intermediate Level

Intermediate Core

Intermediate Compulsory

Intermediate Optional

Sandwich Year Out

Honours Level

Interim Award

Bachelor of Science

Certificate of Higher Education

Diploma of Higher Education

Teaching, Learning and Assessment

A variety of teaching and learning strategies are used appropriate to the nature of the material being delivered. The nominal mean workload on students is 10 hours per credit. Typically, lectures are used as a mechanism to deliver key facts, concepts, theories and methodologies. These may be backed up by… For more content click the Read More button below.

A variety of teaching and learning strategies are used appropriate to the nature of the material being delivered. The nominal mean workload on students is 10 hours per credit.

Typically, lectures are used as a mechanism to deliver key facts, concepts, theories and methodologies. These may be backed up by tutorial and/or practical sessions. These sessions allow students to develop their skills, to receive feedback on their progress and to take ownership of their own learning.

In some subject areas, particularly the group-based module, teaching may be studio-based and/or delivered using group seminars.

Use is made of IT resources in teaching across the full range of subjects. This may be in the form of materials made available via the VLE, electronic forums, simulations and examples. Students are introduced to the University and Departmental systems for C&IT through induction and in the Introduction to Modelling and Problem Solving module.

Formative assessment will be provided in a variety of ways. Whenever practical, students will be given individual feedback on their progress prior to formal assessment. This may be in the form of oral feedback on work reviewed in a tutorial, seminar or studio session or written feedback on a piece of work submitted prior to assessment. Formative assessment is a student driven process.

Assessment is used to determine if students have achieved the learning outcomes of individual modules and hence, the learning outcomes of the programme. A number of forms of assessment are used. These may include portfolios of work, essays, reports on group work, audio-visual presentations (both individually and as a member of a group), computer-based tests, short tests and formal examinations. In all cases, assessment is governed the University’s Regulations for Awards as reproduced in the Students’ Handbook of Regulations.

The assessment to be used in individual modules is indicated in supporting documentation.

The University complies fully with the Special Educational Needs and Disabilities Act. The wide variety of delivery and assessment methods used makes the course accessible to students with a range of special educational needs and/or disabilities, for example a Personal Support Learning Plan (PSLP) or software designed to help the students with disabilities. The course materials go in line with accessibility statement.

The course has been designed to meet the QAA Benchmark Statement for Mathematics, Statistics and Operational Research. Consolidation and development of the student’s fundamental mathematical skills is initiated in the first year where knowledge of the under-pinning mathematics and statistics is delivered. Subject-specific skills are developed in the context of a broad range of problem solving activities allowing students to develop knowledge of key mathematical concepts and topics both explicitly and by application to the solution of problems. Use of this knowledge is further amplified in the Applied Mathematics module with a focus on application to real world problems. Students are thus able to comprehend problems, abstract the essential elements and formulate solutions both mathematically and symbolically, so facilitating problem analysis and solution.

Throughout the course, students will gain the necessary knowledge and experience to develop logical mathematical arguments with clear appreciation of underlying assumptions and conclusions. Mathematical study will be reinforced throughout by the use of computers and relevant software packages appropriate to the level of study. Through individualised and collaborative analysis students will present their mathematical arguments and conclusions using appropriate mathematical notation.

The creative and innovative skills necessary for successful assessment of problems in a given field and the ability to transfer knowledge from one context to another, with logical appraisal and analytical approach are reinforced. Specific application of these skills is introduced in year one in the Introduction to Modelling and Problem Solving module and is further enhanced in Mathematical Programming and in Applied Mathematics. Year 2 modules build further on these problem solving skills in both Advanced Statistical Methods and in Mathematical Methods and Modelling’. The specific application of these skills is developed in the second year Mathematics Group Project. In addition, students will normally further apply their problem solving and analytical skills in the fourth year Individual Project.

Throughout the course, students will gain an appreciation of the social, environmental, ethical, economic and commercial considerations impacting on real world problem solving activities and will be able to place their activities within a broader picture. These aspects are embedded in a variety of modules but are explicitly addressed in the Year 1 Introduction to Modelling and Problem Solving module, the Year 2 Group Project and the Year 4 Individual Project.

A wide range of practical skills are delivered including advanced computer analysis practice and programming skills which form an integral part of the study and application process at all levels. Computer aided solutions are derived in Introduction to Modelling and Problem Solving, Mathematical Programming and Applied Mathematics in Year 1 as well as in the Group Project, Operational Research, and Advanced Statistical Analysis in Year 2. Analytical and transferable problem solving skills are further developed along with computer-based analysis in the final year of the course. Project-based assignments are incorporated in all years of the programme.

During Year 1 students will build an online PDP during the ‘Introduction to Modelling and Problem Solving’ module. This will be enhanced in the second year in the ‘Year 2 Group Project (Mathematics)’ and monitored through the interaction with placement unit in the Year 3 and Personal Academic Tutors in Year 2. During the final year, support will be given through final year project supervision sessions to maintain and further enhance the PDPs.

The modules on years 1, 2 and 4 of the course are delivered over a 24-week teaching period followed by a 3-week assessment period. Year 3 of the programme comprises a monitored Professional Placement with a minimum duration of 48 weeks. The professional placement does not contribute to the final degree classification but is recognised in the ‘Sandwich’ award.

Study Plans

Support for Students and their Learning

Year Tutors are available to provide guidance on academic progress.
Module Tutors are available to help with academic problems specific to the modules they deliver.
Supporting documentation is provided in the form of student handbooks, module handbooks, programme specifications and module specifications.
The virtual-learning environment, Brightspace, is used to support all modules and year groups.
Lecture Capture is available for a large number of taught classes to aid student learning.

Criteria for Admission

The recruitment and admissions process endeavors to ensure a good match between the abilities and aptitudes of the applicants and the demands of the programme. The aim is to facilitate widening participation whilst ensuring that students can reasonably expect to succeed on their chosen course. Candidates must be able to satisfy the general admissions requirements of the University of Huddersfield https://www.hud.ac.uk/policies/registry/awards-taught/section-1/ (section 1.5) and the specific requirements of the course which can be found on the University’s coursefinder website.

Those students who wish to progress to the integrated master’s course, for a fifth year, will be considered on a case by case basis.

Candidates with non-standard qualifications and/or experience will be considered on a case by case basis. For candidates with supplementary qualifications and/or experience it may be possible to take this into account and offer these candidates exemption from specific modules, or entry onto a later year of the course.

The overriding consideration in admitting a student to any of these courses is evidence that the student is likely to be able to complete the course satisfactorily.

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

Quality and Standards

The University’s Teaching and Learning Committee has ultimate responsibility for quality and standards of teaching and learning in the University.
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.

Monitoring, Development and Evaluation

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, termly Student Panel meetings, input from student members of the Course Committee and the National Student Survey.
From external examiners through annual reports, course assessment board minutes, assessment moderation reports and informal communication during the year.
From Professional Standards and Review Bodies through quinquennial reaccreditation visits.

Annual evaluation of the 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.

Validation of Courses, Modules and Changes

Course validation takes place under the University's Quality Assurance Procedures for Taught Programmes. Amendments to programme and module documents are validated by the School Accreditation and Validation Panel.

Teaching and Learning

The School 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.

Staff Development Priorities Include

Staff Annual Appraisal and institutional staff development courses.

Fellowship of the Higher Education Academy.

Updating professional developments.

Regular course meetings and annual review and planning for subsequent academic year.

Engagement in subject specific research conferences, including pedagogical research.

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

Students on BSc (Hons) Mathematics course, who successfully complete Year 1/Year 2/the BSc (Hons) award with a minimum award of a 2:1 honours degree (i.e. a classification profile of at least 60%) will then be offered progression to the integrated master’s enhanced year of study.

Indicators of Quality and Standards

Reports of validation panels.

Annual course reviews.

Annual evaluation report.

External examiners’ reports.

Qualifications and experience of staff.

Report on University Review.

The program will be offered for accreditation by the Institution of Mathematics and its Applications (IMA). Accreditation will normally constitute quintennial external audit/review.

In the periods between professional institution accreditation visits, quality will be monitored and maintained via the external examining system. The teaching quality of staff responsible for delivering course modules will be assured by a mandatory FHEA requirement. An additional quality indicator will be the significant involvement in the delivery of the programme of staff contributing to Research Excellence Framework submissions.