PROGRAMME SPECIFICATION Final...Page 1 of 20 s PROGRAMME SPECIFICATION Final PART 1: COURSE SUMMARY...

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UNIVERSITY OF BRIGHTON

COVID-19 Course Delivery Statement 2020/21

School CEM

Name of Course(s) BSc(Hons) Mathematics (with IFY)

MMath Mathematics

MMath Mathematics for Data Science

Are there minimum equipment requirements for students?

Yes. Ideally, off-line access to on-campus software. Depending on the year of their course and their choice of options our students will need a subset of the following software (some of which is free):

Adobe acrobat Anaconda with Python 3.x. Excel Lindo Maple MATLAB Minitab

Microsoft SQL Management Studio application MS Office Octave Python R R Studio SAS - ideally SAS on Demand + SAS Enterprise Guide SPSS

Are there minimum hardware requirements for students?

Intel i5 or equivalent or better

Windows 10

8Gb RAM, 256Gb SSD

Full HD screen

Course Specific Delivery Statement:

The Maths Department has adopted the University’s blended learning model for the coming academic year. Module teams developed considerable expertise in this model during the last few months of 2019-20, aided by student feedback.

None of the course content has been altered and there are no changes in assessment, although in some cases this will need to be conducted remotely. We aim to help our new students quickly develop a sense of belonging by personal interaction with peers and staff, so 50% of the 16-hour/week semester-1 contact time in year 1 is planned to take place on campus. It will nevertheless be possible for students who prefer to do so to fully engage with the course remotely.

In year 2, some modules will be completely online using pre-recorded materials backed up by extensive learning resources on MyStudies and including regular live interactive sessions. Other modules will be delivered on campus. The final year of the course, where all modules are optional, will mix online and face-to-face delivery. It will be possible to engage only remotely in many of these options.

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All learning is planned to ensure that it is a) centred on our students’ experience, b) well-organised with a weekly breakdown of sessions and topics from the onset, and c) supported by high-quality resources, including appropriate software.

PROGRAMME SPECIFICATION

Final

PART 1: COURSE SUMMARY INFORMATION

Course summary

Final award MMath Mathematics for Data Science

Intermediate award BSc(Hons) Mathematics

BSc Mathematics

Dip HE Mathematics

Cert HE Mathematics

Course status Proposed

Awarding body University of Brighton

School Computing, Engineering and Mathematics

Location of study/ campus Moulsecoomb

Partner institution(s)

Name of institution Host department Course status

1. SELECT

2.

3.

Admissions

Admissions agency UCAS

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Entry requirements Include any progression opportunities into the course.

Check the University’s website for current entry requirements

A-levels or BTEC

Entry requirements are in the range of A-level ABB–BBC (128–112

UCAS Tariff points), or BTEC Extended Diploma DMM–MMM. Our

conditional offers typically fall within this range.

A-levels must usually include maths at grade B.

We will generally make you an offer if your predicted grades are at

the top of this range. If your predicted grades are towards the lower

end of this range we may still make you an offer if you have a good

GCSE (or equivalent) profile or relevant non–academic

achievements.

International Baccalaureate

28 points, with three subjects at Higher level which must include

maths at grade 6.

Access to HE Diploma

Pass with 60 credits overall. Level 3 maths units required. At least 45

credits at level 3, with 24 credits at merit or above.

GCSE

At least five GCSEs, subjects must include English language and

maths at grade 4.

Foundation degree/HND

may enable you to start the course in year 2 or 3. HNC may also

count towards direct entry.

Studied before or have relevant experience?

A qualification, HE credits or relevant experience may count towards

your course at Brighton, and could mean that you do not have to take

some elements of the course or can start in year 2 or 3.

For non-native speakers of English

IELTS 6.0 overall, with 6.0 in writing and a minimum of 5.5 in the

other elements.

International students may also gain entry via completing pathway

courses at The University of Brighton International College. For more

information see: http://www.kic.org.uk/brighton/

Start date (mmm-yy) Normally September

Sep-20

Mode of study

Mode of study Duration of study (standard) Maximum registration period

Full-time 4 years 10 years

Part-time 8 years 10 years

Sandwich 5 years 10 years

Distance Select Select

http://www.kic.org.uk/brighton/

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Course codes/categories

UCAS code GG31

Contacts

Course Leader (or Course Development Leader)

Dr W P Wilkinson (Course Leader)

Admissions Tutor Dr P J Harris

Examination and Assessment

External Examiner(s)

Name Place of work Date tenure expires

Dr James Hind

Prof Andrew Osbaldestin

Nottingham Trent University.

University of Portsmouth

30/09/2022

30/09/2020

Examination Board(s) (AEB/CEB)

AEB – Mathematical Sciences

CEB – Mathematical Sciences

Approval and review

Approval date Review date

Validation April 20181 2

Programme Specification January 20203 January 20214

Professional, Statutory and Regulatory Body 1 (if applicable): Institute of Mathematics and its Applications

April 2018 April 2018

Professional, Statutory and Regulatory Body 2 (if applicable):

Professional, Statutory and Regulatory Body 3 (if applicable):

1 Date of original validation. 2 Date of most recent periodic review (normally academic year of validation + 5 years). 3 Month and year this version of the programme specification was approved (normally September). 4 Date programme specification will be reviewed (normally approval date + 1 year). If programme specification is applicable to a particular cohort, please state here.

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PART 2: COURSE DETAILS

AIMS AND LEARNING OUTCOMES

Aims

The aims of the course are:

• to develop graduates who have a broad range of academic skills in the mathematical sciences and transferable skills which equip them for employment or further study and life-long learning. Specifically to provide a broad foundation in the mathematical sciences with the opportunity for further specialisation as well as opportunities for applying mathematical knowledge and skills in a variety of contexts.

• to produce mathematically literate graduates who have the confidence to apply their knowledge, working individually or in teams, and the ability to communicate their ideas and conclusions effectively taking account of the needs and knowledge base of the intended audience.

• to produce graduates capable of studying in depth specific topics in the area of applied statistical science and of developing into professional mathematicians or researchers in industry or the Higher Education sector.

• to develop a range of transferable skills including general IT skills, expertise in the use and evaluation of specialist software, communication skills, the ability to research and evaluate information, to evaluate problems and think logically, team-working abilities and personal organisation and development.

• to develop careers-related and life-long learning skills in partnership with the University’s Careers Service.

At level 4, the specific aims are

• to provide a balanced introduction to the core concepts, principles and techniques of the mathematical sciences and their application to a range of situations which will be relatively straightforward or self-contained in nature.

• to develop students’ IT, communication and team working skills and to provide the basis for consideration of the options in Level 5 including the optional placement.


• to develop a sound knowledge and critical understanding of the concepts, principles and techniques of the mathematical sciences and their application to a range of problems that will be more complex and open ended and may require the use of specialist software.

• to develop the ability to construct reasoned arguments and to present conclusions and findings in a structured way that meets the needs of the intended audience.

A particular feature of the programme is the optional ‘sandwich’ placement year which aims to

• provide opportunities for students to develop and apply their knowledge in a professional environment thus reinforcing the aims articulated above.


• to build on level 5 to provide opportunities for students to develop a deeper and systematic understanding of some areas of the mathematical sciences and to produce more sophisticated reasoned arguments. The application of knowledge and techniques at level 6 will pertain to problems in a variety of areas that are less well formed, more complex and may require techniques to be adapted.

• to further develop the skills of independent learning, researching and evaluating information, critical reflection, communication and time management.

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• to develop graduates with a deep understanding of some of the concepts, theories and applications of mathematical sciences and with an appreciation of the nature and beauty of mathematics.

• to further develop the ability to study independently specific advanced topics in the mathematical sciences and report the results of an applied statistical science investigation according to professional academic standards.

Learning outcomes

The outcomes of the main award provide information about how the primary aims are demonstrated by students following the course. These are mapped to external reference points where appropriate5.

Knowledge and theory Students will be able to demonstrate knowledge and understanding of:

LO1 a range of mathematical theories and concepts

LO2 a variety of mathematical methods and techniques and their application

LO3 structured mathematical or analytical approaches to problem solving

LO4 the modelling process and a range of modelling techniques

LO5 the importance of assumptions, awareness of their use and consequences of their violation

LO6 the use and application of software within mathematical sciences.

Award specific learning outcomes

(MMath Mathematics for Data Science)

Students will be able to demonstrate:

MDat1 a sound understanding of a range of mathematical structures, arguments and proofs.

MDat2 a deep understanding of some areas of mathematics, statistics or their applications.

Skills Includes intellectual skills (i.e. generic skills relating to academic study, problem solving, evaluation, research etc.) and professional/ practical skills.

Students will have the ability to:

LO7 construct and develop logical mathematical arguments

LO8 engage in the processes of mathematical modelling, identifying assumptions and drawing appropriate conclusions

LO9 choose and use appropriate software to aid the mathematical and modelling processes and acquire and interpret further information

LO10 transfer expertise, knowledge and understanding from one context to another

LO11 demonstrate and apply general transferable skills including time management, organisational skills and working effectively within a team

LO12 research and learn independently and to obtain, assess and interpret information from a range of sources

LO13 effectively communicate mathematical arguments and disseminate information and conclusions appropriate for the intended audience.

Award specific learning outcomes

(MMath Mathematics for Data Science)

Students will have:

5 Please refer to Course Development and Review Handbook or QAA website for details.

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MDat3 developed the ability to apply their mathematical knowledge to the solution of a variety of problems.

MDat4 developed the ability to present the results of a substantial applied statistical science investigation in a professional manner.

QAA subject benchmark statement (where applicable)6

The aims and learning outcomes of the programme have been informed by, and are consistent with, the QAA Framework for Higher Education Qualifications and the revised (2015) Subject Benchmark for Mathematics, Statistics and Operational Research:

http://www.qaa.ac.uk/publications/information-and-guidance/publication?PubID=2952#.Wo3pFmdIk34

PROFESSIONAL, STATUTORY AND REGULATORY BODIES (where applicable)

Where a course is accredited by a PSRB, full details of how the course meets external requirements, and what students are required to undertake, are included.

This programme meets the educational requirements of the Chartered Mathematician designation, awarded by the Institute of Mathematics and its Applications.

LEARNING AND TEACHING

Learning and teaching methods

This section sets out the primary learning and teaching methods, including total learning hours and any specific requirements in terms of practical/ clinical-based learning. The indicative list of learning and teaching methods includes information on the proportion of the course delivered by each method and details where a particular method relates to a particular element of the course.

The information included in this section complements that found in the Key Information Set (KIS), with the programme specification providing further information about the learning and teaching methods used on the course.

Overview A wide range of teaching and assessment methods ensures that the aims and learning outcomes of the course are met whilst taking into consideration the diverse learning styles and needs of students. Introduction of key concepts, an increasingly deeper understanding of the subject matter and its applications, and the development of key skills are achieved through traditional lectures and problem-solving tutorials, group and individual case study investigations, computer laboratory classes, flipped learning featuring guided exploration of new concepts, computer modelling and simulation, student-led seminars, formative peer-assessments, and individual or group project work. The learning progressively becomes more student-centred with some optionality at level 5 and no compulsory modules in the last two levels of the course other than the mandatory 40-credit MM740 Advanced Data Sciences Project module at level 7. This structure enables students to tailor the curriculum according to their specific interests and strengths.

Level 4

The programme for full-time students includes six semester long modules: three 20-credit modules in semester 1 (MM422, MM423, MM404) and three 20-credit modules in semester 2 (MM405, MM410, MM421). This immersive learning approach in level 4 has two aims. Firstly, it promotes deeper learning of the topics and skills that provide the foundation for more advanced work in future years. Secondly, it helps students to get to know their peers as quickly as possible and helps integration within the cohort. The 20-credit modules are designed to require a total learning effort of approximately 13 hours per week over a 15 week semester (200 hours in total). These include approximately 70 hours of timetabled contact time per module, the rest being guided independent study. All semester-1 modules include guided exploration of new content. Modules MM404 and MM423 involve group work to help students get to know each other quickly. The composition of the groups is rotated often in order to promote inclusivity. Inclusivity is also supported by the range of assessment methods used (posters, presentations, group and individual reports, and traditional examinations) and by a choice of assessment in module MM423. The real-world applications of maths, stats and operational research are emphasized in the semester 2

6Please refer to the QAA website for details.



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modules MM421, MM410 and MM405, respectively. Group work continues to feature significantly in this second semester and students are introduced to specialist software through computer-based work. Timetabled hours will typically be shared between sessions used for introducing and discussing a topic or supervised sessions in the form of problem-solving, computer practicals or structured group discussions. A flipped classroom approach and case study investigations are used in several of the modules.

Level 5

Full-time students will be studying six year-long modules at any one time. All modules require a total learning effort of around 200 hours of which around 60 hours are timetabled and the rest is guided independent study. There are three compulsory year-long modules broadly exploring further topics in pure maths (MM503), applied maths (MM506) and statistics (MM510), respectively. Students can then choose one of three year-long modules devoted to the applications of the subject in business/decision-making (MM505), statistics (MM511) or the modelling of the physical world (MM504). Group investigations, online discussion boards, and formative peer-assessment feature alongside the more traditional lecture and problem-solving sessions. There is an emphasis on the development of modelling skills in several modules and extensive use is made of computer tutorials to promote mastery of a number of specialist mathematical and statistical software.

Optional Placement

An optional element of professional education and practice is the 48-week placement that full-time students may opt to undertake between Levels 5 and 6.

Level 6

By the time they reach Level 6, students will have identified where their own interests lie within the mathematical sciences and what assessment methods better help them demonstrate their knowledge, understanding and skills. Students build-up their own 120-credit curriculum from a portfolio of up to 300-credits worth of optional modules, of which at least 40 credits are in the general area of applied statistics. Students may also opt for a semester-long short project on a topic of interest to them. All modules are 20-credits and are designed to require a total learning effort of approximately 200 hours. These include a total of 48-50 hours of contact time with the rest of the time allocation devoted to guided independent study. A wide range of assessment methods are used and inclusivity supported through a choice of assessment types in as many as 5 of the modules. Most modules include an element of individual or group research into some aspect of the syllabus, exposure to research articles, and/or the solution of open-ended problems. Advanced modelling skills are developed in several of the modules with the help of specialist software.

Level 7

Through the mandatory project module MM740 students at level 7 plan, research and execute a substantial piece of independent investigative work in the area of applied statistics. This is worth one third of the credits for this level. The project may include a critical review of a specific topic, a major piece of modelling and analysis, or an exploration of some aspects of the research work of the academic staff in the Department. Students are assessed through a viva examination and either a poster or a report produced to academic standards. The total learning effort is approximately 400 hours, of which 15 are scheduled contact time with the project supervisor. The remaining 80 credits are chosen from a portfolio of optional modules exploring in greater depth more advanced material. Of these, at least 40 credits are chosen from modules focusing in the applications of statistics and data science (e.g Stochastic Methods, Actuarial Science, Data Mining and Management, Medical Statistics, Forecasting, Artificial Intelligence). There is an emphasis on the development of research skills through inquiry-led learning, exposure to research articles, student-led activities such as the design of probing examination questions and investigation of the most appropriate methods for statistical analysis of particular real-world problems. All optional modules are 20-credits worth and require a learning effort of approximately 200 hours, of which 48 hours are dedicated to timetabled activities in lecture/seminar rooms or computer laboratories.

Research-informed learning

The mathematical sciences are frequently regarded as being hierarchical in nature with the frontiers of current research lying a long way from the undergraduate curriculum, so that ‘undergraduate programmes … are not generally expected to reach the frontiers of knowledge’ (QAA Subject

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Benchmark: MSOR, section 2.4). Nevertheless, our MMath Mathematics for Data Science incorporates research-informed learning in the ways outlined below.

Several modules at levels 4 (MM422, MM423, MM404, MM405), 5 (MM503, MM506, MM504, MM505, MM511) and 6 (e.g. MM600, MM606, MM607, MM610, MM611, MM612, MM640, MM654) involve guided exploration of new content, group work on open-ended problems or investigations into some areas of the syllabus or specific techniques. These research-based activities allow students to develop and demonstrate their own practice as researchers. Students choosing either of the project modules (MM600, MM640) are often exposed to the research interests of the academic staff. For example, students have in recent years completed projects examining aspects of diagrammatic reasoning or of algorithmic information theory, looking at numerical methods for the solution of multiple integrals arising in the study of solar wind velocity distribution functions, researching the modelling of expanding underwater bubbles or deformations of the spinal cord. Students have, on occasion, presented their project work at external academic conferences.

In addition, research-led activities are part of a number of the level 6 and 7 module options that are related directly to the research interests of staff. For example, research in the diagrams domain filters into the more basic discrete maths curriculum, where reasoning with diagrams is discussed. The more correct but less common terminology, arisen from research activity, of ‘Venn-Euler diagrams’ is explained (rather than the informal, high-school notion of ‘Venn diagrams’). Discussion of current research on Online and Batch Learning paradigms takes place in MM610 Methods of Machine Learning, and MM710 Machine Learning and Artificial Intelligence. Students on MM612 Graph Theory and Applications are also exposed to some relatively recent published results and to some conjectures such as Grunbaum’s conjecture. Staff research interests in formal languages are discussed in MM612. In the level 6 option MM619, the material on low-rank formats in stochastic simulation has been inspired by staff research. In module MM607, students are given the opportunity to discuss critically some of the controversies and dilemmas of modern Mathematics while MM605 allows students to explore some of the most recent developments in Mathematics Pedagogy. Research-oriented elements highlight the processes by which knowledge is produced in a number of modules (e.g. MM611, MM612, MM713 and MM714) through, for example, developing students’ skills of constructing question variants, answering them, and researching via technological means to verify outcomes.

Education for Sustainable Development

Some elements of the curriculum explore and model issues related directly to sustainability. For example, one of the group case study topics in level 4 examines the issue of renewable energy resources with students investigating how to meet the energy needs of a group of astronauts at a base in an imaginary Earth-like planet. Various population and epidemiology models are explored (e.g. in modules on Stochastic Methods and on Medical Statistics) which include the effects of environmental factors on the evolution of populations. Other discrete models consider how improving operational efficiency (for example, reducing transportation costs or improving the occupancy of passenger aircraft) can have genuine environmental impacts. In level 7, a couple of options explore the implications of data integrity and security. The options on Time Series and Forecasting at levels 6 and 7 develop crucial skills for the analysis of the time-evolution of key environmental data such as global atmospheric and surface temperatures. Finally, the assessment in the level 6 option MM607 provides an opportunity for students to explore more deeply some aspect of the interaction of mathematics on the wider environment.

E-learning

Students are exposed to a variety of specialist software packages (e.g. Matlab, Maple, SPSS, SAS, R, Lindo) throughout their degree course. These re-enforce theoretical concepts and/or enable sophisticated modelling and data analysis. Students are also introduced to LaTeX in level 4 and are expected to produce professional looking typeset mathematics in written work and presentations.

All teaching is supported by materials made available via StudentCentral, an on-line mediated learning environment. The Mathematical Sciences Division strives to comply with the School of Computing, Engineering and Mathematics’ minimum information requirement that must be made available to students via StudentCentral.

Formative assessment and feedback

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Every module includes formative assessment to guide students’ learning, provide timely, constructive feedback on their progress and prepare them for the summative assessment task. Formative assessment is designed to be complementary to the type of summative assessment – for example, preparation for an examination could be through the design of sample questions, test or mock examination.

Formative feedback is often given directly in class, by the tutor and peer learners. Alternative methods are to provide written feedback online, through studentcentral (the University’s Virtual Learning Environment or VLE) and ‘My Grades’, which is how students can view all their marks online.

ASSESSMENT

Assessment methods

This section sets out the summative assessment methods on the course and includes details on where to find further information on the criteria used in assessing coursework. It also provides an assessment matrix which reflects the variety of modes of assessment, and the volume of assessment in the course.

The information included in this section complements that found in the Key Information Set (KIS), with the programme specification providing further information about how the course is assessed.

The following methods of assessment are used:

Written Examinations (E): demonstration of knowledge and analytical skills

Computer based exams (CE) demonstration of knowledge, analytical skills and ICT skills

Tests (T): demonstration of knowledge and analytical skills Coursework (C): demonstration of knowledge and analytical skills

Reports (R): demonstration of analytical, research and communication skills

Presentations (P): demonstration of knowledge, analytical and communication skills

Project (Proj): independent and research skills, problem analysis, problem solving, solution building and evaluation

Group projects (GP): demonstration of independent research, problem-solving and ability to work as a member of a team

Poster (PS): demonstration of knowledge, analytical and communication skills

Portfolio (PF): demonstration of analytical, research and communication skills

Seminar (S) demonstration of knowledge, analytical and communication skills

Viva Examination (V) demonstration of knowledge, analytical and research skills

The table below shows the minimum set of methods used to assess each course learning outcome. For each individual student some outcomes will also be met by other methods, depending on the choice of elective modules. Please note that in the mappings of course learning outcomes onto modules, since there is a large number of possible combinations of optional modules which can be chosen at level 6 this is reflected in the potential number of additional credits for this level of the course.

Learning Outcome (Knowledge and Theory)

Assessment method Module Number of credits

1 Ability to demonstrate knowledge and understanding of a range of mathematical theories and concepts

E, CE, T, C, R, P, Proj, GP, PS, PF, S, V

All MM modules At least 460

2 Ability to demonstrate knowledge and understanding of a variety of mathematical methods and techniques and their application


All modules 480

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3 Ability to demonstrate knowledge and understanding of structured mathematical or analytical approaches to problem solving



4 Ability to demonstrate knowledge and understanding of the modelling process and a range of modelling techniques

E, CE, T, P, GP, R Level 4: MM404, MM405, MM410

Level 5: MM506, MM510 Optional Level 5: MM504, MM505, MM511

Level 6: MM602, MM606, MM610, MM612, MM614, MM617, MM618, MM619

Level 7: MM710, MM716, MM717, MM718, MM719, MM754

60

40 20

Up to an additional 120


5 Ability to demonstrate knowledge and understanding of the importance of assumptions, awareness of their use and consequences of their violation


Level 4: All modules

Level 5: All modules

Level 6 MM600, MM602, MM606, MM607, MM610, MM611, MM612, MM614, MM617, MM618, MM619, MM640, MM654

Level 7: All MM modules

120 120 Up to an additional 120 At least 100

6 Ability to demonstrate knowledge and understanding of the use and application of software within mathematical sciences

E, CE, T, R, P, GP Level 4: MM421, MM405, MM410

Level 5: MM520, MM506, MM510 Level 5 optional MM505, MM511


60

60 Up to an additional 20


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Level 7: MM710, MM712, MM716, MM717, MM718, MM719, ISM122, MM702


MDat1 Ability to demonstrate a sound understanding of a range of mathematical structures, arguments and proofs.

E, CE, C, R, P, GP, PS, Proj, S, V

Level 4:

MM422, MM423, MM421

Level 5: MM520, MM521, MM503

Level 6: MM600, MM607, MM610, MM611, MM612, MM640, MM654


60

60



MDat2 Ability to demonstrate a deep understanding of some areas of mathematics, statistics or their applications.

E, CE, R, C, P, GP, PS, S, V, Proj

All Level 7 MM modules

At least 100

Learning Outcome

(Skills)

Assessment method Module Number of credits

7 Ability to construct and develop logical mathematical arguments

E, CE, T, C, R, P, GP, S, Proj


8 Ability to engage in the processes of mathematical modelling, identifying assumptions and drawing appropriate conclusions

E, CE, T, P, GP, R Level 4: MM404, MM405, MM410

Level 5: MM506, MM510 Optional Level 5: MM504, MM505, MM511



60

40 20



9 Ability to choose and use appropriate software to aid the mathematical and modelling processes and acquire and interpret further information

E, CE, T, R, P, GP Level 4: MM421, MM405, MM410

Level 5: MM520, MM506, MM510 Level 5 optional

60

60

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MM505, MM511


Level 7: MM712, MM714, MM716, MM717, MM718 MM719, ISM122, MM702




10 Ability to transfer expertise, knowledge and understanding from one context to another.

E, CE, T, C, R, P, PS, PF, Proj, GP,S

All modules 480

11 Ability to demonstrate and apply general transferable skills including time management, organisational skills and working effectively within a team

E, CE, R, P, GP, PS, PF, Proj

Level 4:

MM422, MM423, MM404, MM405, MM410

Level 5: MM520, MM506, Level 5 optional: MM505, MM504, MM511


Level 7: MM740, MM710, MM712, MM713, MM714, MM717, MM754, ISM122, MM702

100

40 20 Up to an additional 120 Up to an additional 120

12 Ability to research and learn independently and to obtain, assess and interpret information from a range of sources

E, CE, R, P, Proj, GP, PS, PF, S, V

Level 4:

MM422, MM423, MM410, MM404, MM405

Level 5: MM520, MM506 Level 5 optional: MM511, MM505, MM504

Level 6: MM600, MM605, MM606, MM607, MM610, MM611, MM612, MM617, MM618, MM640, MM654, MM699 Level 7: All modules

100

40 20

Up to an additional 120 120

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13 Ability to effectively communicate mathematical arguments and disseminate information and conclusions appropriate for the intended audience.

E, CE, T, C, R, P, Proj, GP, PF, PS, S,V

All modules 480

MDat3 Ability to apply mathematical knowledge to the solution of a variety of problems

E, CE, T, C, R, P, Proj, GP, PF, PS, V

All modules 480

MDat4 Ability to present the results of a substantial applied statistical science investigation in a professional manner.

Proj, V, R/PS MM740 40

SUPPORT AND INFORMATION

Institutional/ University All students benefit from:

University induction week

Student Contract

Course Handbook

Extensive library facilities

Computer pool rooms (see below under “Course-specific”)

E-mail address

Welfare service

Personal tutor for advice and guidance

Level Tutors

Student Support and Guidance Tutor

Studentcentral (Managed Learning Environment)

StudentProfile

Course-specific Additional support, specifically where courses have non-traditional patterns of delivery (e.g. distance learning and work-based learning) include:

In addition, students on this course benefit from:

Computing, Engineering and Mathematics (CEM) computer laboratories with approximately 300 networked workstations, technical support and printing facilities.

Personal webspace.

Placements Unit and tutor support during the placement year.

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PART 3: COURSE SPECIFIC REGULATIONS

COURSE STRUCTURE

This section includes an outline of the structure of the programme, including stages of study and progression points. Course Leaders may choose to include a structure diagram here.

At Level 4, all modules are semester-long, immersive delivery, to promote faster learning of core material and quickly develop integration within the student cohort. Modules MM422, MM423, MM421, MM410 are common to all degrees in the mathematical sciences programme. These modules provide a foundation in the theory of mathematical sciences and develop key skills.

At level 5, MM520, MM521 and MM510 are common to all mathematical sciences degrees. These modules cover fundamental topics in maths and stats. MM503 Algebra and Analysis and MM506 Numerical Methods develop further pure mathematical theory and core numerical methods, respectively. Students also have the option of further developing applications in either mathematics (MM504), statistics (MM511) or operational research (MM505).

At Level 6, students choose 120 credits of optional modules, all of which are 20 credits worth. The range of modules available at Level 6 allows for students to graduate from the same degree course with differing subject expertise. Students on this programme must choose at least two modules (40 credits in total) out of the following five options: MM602, MM610, MM617, MM618 or MM619. Some optional modules are semester-long and some are year-long. Students may therefore opt to register for 70 nominal credits in one semester and 50 nominal credits in the other semester rather than go for a 60-60 split, if this helps them meet their specific interests.

At level 7 students must register for and pass the mandatory 40-credit Project module MM740. Students on the MMath Mathematics for Data Science must investigate a topic in applied statistical science as the subject of their Project. In addition, students choose 80 credits from a range of 20-credit optional modules, of which at least 40 credits must be chosen from the following group of modules: MM702, MM710, MM712, MM717, MM718, MM719 or ISM122. As is the case at level 6, some optional modules are semester-long and some are year-long. Students may therefore opt for 30 nominal credits worth of options in one semester and 50 nominal credits in the other semester rather than go for a 40-40 split.

Level 4

MM422 Discrete Mathematics Semester 1 20 credits

MM423 Continuous Mathematics Semester 1 20 credits

MM404 Modelling and Mechanics Semester 1 20 credits

MM421 Linear Algebra and Calculus Semester 2 20 credits

MM410 Introduction to Statistics Semester 2 20 credits

MM405 Introduction to Operational Research Semester 2 20 credits

Semester-1

Semester-2

MM404

(20)

Modelling and

mechanics

MM421

(20)

Linear algebra and

calculus

MM410

(20)

Intro to statistics

MM405

(20)

Intro to OR

MM422

(20)

Discrete Mathematics

MM423

(20)

Continuous Mathematics

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Level 5

Each module at Level 5 carries 20 credits.

Compulsory modules

MM520 Further Calculus Year-long 20 credits

MM521 Linear Mathematics Year-long 20 credits

MM503 Analysis and Algebra Year-long 20 credits

MM506 Numerical Methods Year-long 20 credits

MM510 Applied Linear Statistical Models Year-long 20 credits

Optional module: one module from the following

MM505 Operational Research Year-long 20 credits

MM511 Mathematical Methods for Statistical Practice Year-long 20 credits

MM504 Advanced Mechanics Year-long 20 credits

Year-long

Placement

After completing Level 5 students have the option of taking the placement year module, MM699. This module carries zero credits.

Level 6

120 credits chosen from Level 6 optional modules which may be a mixture of 20-credits semester long modules and/or 20-credits year-long modules. Students can choose between 50 and 70 nominal credits in one semester and the remainder in the other semester. Students on this programme must choose at least two modules out of the following five options: MM602, MM610, MM617, MM618 or MM619. Students choosing MM610 at level 6 may not choose MM710 in level 7. Students choosing MM606 in level 6 may not choose MM716 in level 7. Likewise, students choosing MM618 in level 6 may not choose MM718 in level 7. Finally, students choosing MM619 at level 6 may not choose MM719 at level 7. Students opting for module MM600 at level 6 must choose a different topic and supervisor for their mandatory project module MM740.

MM600 Mathematical Sciences Project Semester 2 20 credits

MM602 Mathematics of Finance Year-long 20 credits

MM605 Mathematics Education: Research and Experience Year-long 20 credits

MM606 Numerical Analysis of Partial Differential Equations Year-long 20 credits

MM607 Historical, Social and Philosophical Perspectives on Semester 1 20 credits

Mathematics

MM610 Methods of Machine Learning Year-long 20 credits

Choose one

MM

506

Num

eric

al

me

thods

MM

504

Advanced

Me

chanic

s

MM

505

Opera

tional R

esearc

h

MM

511

Ma

them

atic

al M

eth

ods fo

r Sta

tistic

al P

ractic

e

MM

503

Analy

sis

and a

lgebra

MM

510 A

pplie

d L

inear S

tatis

tical M

odels

MM

520

Fu

rther C

alc

ulu

s

MM

521

Lin

ear M

ath

em

atic

s

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MM611 Topics in Abstract Algebra Semester 2 20 credits

MM612 Graph Theory and Applications Semester 1 20 credits

MM614 Optimisation Methods Year-long 20 credits

MM617 Statistical Modelling Year-long 20 credits

MM618 Time Series and Forecasting Year-long 20 credits

MM619 Stochastic Methods Semester 1 20 credits

MM654 Complex Analysis Semester 2 20 credits

Semester-1

Semester-2

Year-long:

Level 7

The 40-credit project module MM740 is mandatory and the project must investigate a topic in the area of applied statistical science. Students select an additional 80 credits from the options below of which at least 40 credits must be chosen from the following group of modules: MM702, MM710, MM712, MM717, MM718, MM719 or ISM122. Students may opt to choose between 30 and 50 nominal credits in one semester and the remainder in the other semester.

MM740 Advanced Data Sciences Project (Mandatory) Year-long 40 credits

MM710 Machine Learning and Artificial Intelligence Year-long 20 credits

MM712 Topics in Actuarial Science Year-long 20 credits

MM713 Logic and Number Systems Semester 2 20 credits

MM714 Introduction to Topology Semester 1 20 credits

MM716 Partial Differential Equations: Theory and Solutions Year-long 20 credits

MM717 Medical Statistics Year-long 20 credits

MM718 The Analysis of Time Series Year-long 20 credits

MM719 Stochastic Methods and Algorithms Semester 1 20 credits

MM754 Topics in Mathematical Physics Semester 2 20 credits

ISM122 Data Management Semester 1 20 credits

MM702 Data Mining and Knowledge Discovery in Data Semester 2 20 credits

MM619

Stochastic methods

MM654

Complex analysis

MM628

Time series and forecasting

MM612

Graph theory & applications

MM611

Topics in abstract algebra

MM607

Historical, social and

philosophical perspectives on

mathematics

MM600

Mathematical sciences project

MM

605

Ma

ths E

ducatio

n

MM

606

Num

eric

al A

naly

sis

of

PD

Es

MM

610

Me

thods o

f machin

e

learn

ing

MM

614

Optim

isatio

n m

eth

ods

MM

617

Sta

tistic

al m

odellin

g

MM

618

Tim

e s

erie

s a

nd

fore

castin

g

MM

602

Ma

ths o

f finance

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Semester-1

Semester-2

Year-long:

Modules

Status:

M = Mandatory (modules which must be taken and passed to be eligible for the award)

C = Compulsory (modules which must be taken to be eligible for the award)

O = Optional (optional modules)*

A = Additional (modules which must be taken to be eligible for an award accredited by a professional, statutory or regulatory body, including any non-credit bearing modules)

*Optional modules listed are indicative only and may be subject to change, depending on timetabling and staff availability

Level7

Module code

Status Module title Credit

4 MM422 C Discrete Mathematics 20

4 MM423 C Continuous Mathematics 20

4 MM421 C Linear Algebra and Calculus 20

4 MM410 C Introduction to Statistics 20

4 MM404 C Modelling and Mechanics 20

4 MM405 C Introduction to Operational Research 20

5 MM520 C Further Calculus 20

5 MM521 C Linear Mathematics 20

5 MM510 C Applied Linear Statistical Models 20

7All modules have learning outcomes commensurate with the FHEQ levels 0, 4, 5, 6, 7 and 8. List the level which corresponds with the learning outcomes of each module.

MM754

Topics in Mathematical

Physics

MM702

Data Mining and KDD

MM713

Logic and Number Systems

MM

740

(40, M

)

Advanced D

ata

Scie

nces

Pro

ject

MM

710

Ma

chin

e L

earn

ing a

nd A

I

MM

717

Me

dic

al S

tatis

tics

MM

718

Th

e A

naly

sis

of T

ime

Serie

s

MM

712

To

pic

s in

Actu

aria

l S

cie

nce

MM719

Stochastic methods and Algorithms

ISM122

Data Management

MM714

Introduction to Topology

MM

716

Partia

l Diffe

rentia

l

Equatio

ns: T

heory

and

Solu

tions

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5 MM506 C Numerical Methods 20

5 MM503 C Analysis and Algebra 20

5 MM505 O Operational Research 20

5 MM511 O Mathematical Methods for Statistical Practice 20

5 MM504 O Advanced Mechanics 20

6 MM699 O Industrial Placement 0

6 MM600 O Mathematical Sciences Project 20

6 MM602 O Mathematics of Finance 20

6 MM605 O Mathematics Education: Research and Experience 20

6 MM606 O Numerical Analysis of Partial Differential Equations 20

6 MM607 O Historical, Social and Philosophical Perspectives on Maths 20

6 MM610 O Methods of Machine Learning 20

6 MM611 O Topics in Abstract Algebra 20

6 MM612 O Graph Theory and Applications 20

6 MM614 O Optimisation Methods 20

6 MM617 O Statistical Modelling 20

6 MM618 O Time Series and Forecasting 20

6 MM619 O Stochastic Methods 20

6 MM654 O Complex Analysis 20

7 MM740 M Advanced Data Sciences Project 40

7 MM710 O Machine Learning for Artificial Intelligence 20

7 MM712 O Topics in Actuarial Mathematics 20

7 MM713 O Logic and Number Systems 20

7 MM714 O Introduction to Topology 20

7 MM716 O Partial Differential Equations: Theory and Solutions 20

7 MM717 O Medical Statistics 20

7 MM718 O The Analysis of Time Series 20

7 MM719 O Stochastic Methods and Algorithms 20

7 MM754 O Topics in Mathematical Physics 20

7 ISM122 O Data Management 20

7 MM702 O Data Mining and Knowledge Discovery in Data 20

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AWARD AND CLASSIFICATION

Award type Award* Title Level Eligibility for award Classification of award

Total credits8 Minimum credits9 Ratio of marks10: Class of award

Final MMath Mathematics for Data Science 7 Total credit 480 Minimum credit at level of award 120

Levels 6 and 7 (50:50) Honours degree

Intermediate BSc (Hons)

Mathematics 6 Total credit 360 Minimum credit at level of award 90

Levels 5 and 6 (25:75) Honours degree

Intermediate BSc Mathematics 6 Total credit 300 Minimum credit at level of award 60

Select Not applicable

Intermediate DipHE Mathematics 5 Total credit 240 Minimum credit at level of award 90


Intermediate CertHE Mathematics 4 Total credit 120 Minimum credit at level of award 90


*Foundation degrees only

Progression routes from award:

Award classifications Mark/ band % Foundation degree Honours degree Postgraduate11degree (excludes PGCE and BM BS)

70% - 100% Distinction First (1) Distinction

60% - 69.99% Merit Upper second (2:1) Merit

50% - 59.99% Pass

Lower second (2:2) Pass

40% - 49.99% Third (3)

8 Total number of credits required to be eligible for the award. 9 Minimum number of credits required, at level of award, to be eligible for the award. 10 Algorithm used to determine the classification of the final award (all marks are credit-weighted). For a Masters degree, the mark for the final element (e.g, dissertation) must be in the corresponding class of award. 11Refers to taught provision: PG Cert, PG Dip, Masters.

Document template revised: 2010 of 21

EXAMINATION AND ASSESSMENT REGULATIONS

Please refer to the Course Approval and Review Handbook when completing this section.

The examination and assessment regulations for the course should be in accordance with the University’s General Examination and Assessment Regulations for Taught Courses (available from staffcentral or studentcentral).

Specific regulations which materially affect assessment, progression and award on the course e.g. Where referrals or repeat of modules are not permitted in line with the University’s General Examination and Assessment Regulations for Taught Courses.

To progress onto level 6 of the MMath Mathematics for Data Science programme, a student must normally achieve an average mark of at least 60% at level 5. A student who fails to achieve this threshold will normally be transferred to the BSc (Hons) Mathematics degree programme.

Exceptions required by PSRB These require the approval of the Chair of the Academic Board

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