Senior Lecturer in Mathematics and Data Science

Mathematics - UH-000053

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 <a href="https://www.hud.ac.uk/policies/registry/awards-taught/section-1/">https://www.hud.ac.uk/policies/registry/awards-taught/section-1/</a> (section 1.5) and the specific requirements of the course which can be found on the <a href="https://courses.hud.ac.uk/">University’s coursefinder website</a>.
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.

Course Specific Progression Requirements
<ul>
<li>Year 2 to Year 3/4  
<ul>
<li>normally, a minimum average mark for the year of 60% or above. </li>
</ul>
</li>
<li>Year 4 to Year 5 
<ul>
<li>normally, a minimum average mark for the year of 60% or above, </li>
<li>the Individual Project module to be passed at the first attempt. </li>
</ul>
</li>
<li>Year 4 students who are unable to progress to Year 5 or elect not to continue will be eligible for a Bachelor’s degree with honours award, as detailed in the University of Huddersfield Regulations for Awards. </li>
</ul>
Applicable for student entry from academic year 2020-2021
<ul>
<li>The Integrated Master’s Degrees (second cycle award) receive classification awards, using the same system for the Bachelor’s degree (first cycle), namely First Class, Upper Second Class, Lower Second Class, or Third Class. </li>
</ul>
Master’s Award Classification Calculation
<ul>
<li>On completion of a Master’s course the classification will be determined by the weighted numerical year credit average, as follows:</li>
</ul>
On Master’s courses where the Placement year is included and graded.
M-level:       weighting of 3         - 120 credit average.
H-Level:      weighting of 2          - ‘best’ 100 credit average.
S-level:       weighting of 1          - 120 credit average.
I-Level:       weighting of 1          - ‘best’ 100 credit average.
On completion of a Master’s course where the Placement year is not undertaken, the classification will be determined by the weighted numerical year credit average, as follows: 
M-level:       weighting of 3          - 120 credit average.
H-Level:      weighting of 2          - ‘best’ 100 credit average.
I-Level:        weighting of 1          - ‘best’ 100 credit average.

UH-000053 - Mathematics

Intermediate Compulsory

Intermediate Core

Intermediate Optional

Intermediate Level

Honours Compulsory

Honours Optional

Honours Core

Honours Level

Foundation Core

Foundation Optional

Foundation Compulsory

Foundation Level

Masters Optional

Masters Compulsory

Masters Core

Masters Level

Sandwich Year Out Optional

Sandwich Year Out

Structure

Bachelor of Science with Honours

Bachelor of Science

Diploma of Higher Education

Certificate of Higher Education

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

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

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

Show good judgement in the application of mathematical tools to solve unfamiliar and complex problems within the studied areas.

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.

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

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.

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

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

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.

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.

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

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

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

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

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

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

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

The ability to work independently, critically evaluate their own strength and weaknesses.

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

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.

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.

A systematic understanding of core knowledge (including advanced topics) in a) statistics b) data science c) artificial intelligence d) logical reasoning and e) mathematical modelling.

The ability to plan and develop advanced projects in a) statistics, b) data science and c) operational research.

Effectively develop and apply mathematical and logical concepts to design and perform advanced modelling in applications such as a) data science, b) artificial intelligence and c) engineering.

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

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

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

Quality and Standards
<ul>
<li>The University’s Teaching and Learning Committee has ultimate responsibility for quality and standards of teaching and learning in the University.</li>
<li>The School Board, via the School Teaching and Learning Committee has responsibility for implementing University policy through School-defined procedures.</li>
<li>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&amp;IT strategies, the articulation and assurances of standards, external examiner reports and evaluation and links with professional bodies, employers and other external organisations.</li>
</ul>
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:
<ul>
<li>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.</li>
<li>From external examiners through annual reports, course assessment board minutes, assessment moderation reports and informal communication during the year.</li>
<li>From Professional Standards and Review Bodies through quinquennial reaccreditation visits.</li>
</ul>
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 <a href="https://www.hud.ac.uk/policies/registry/qa-procedures/">Quality Assurance Procedures for Taught Programmes</a>. 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.

UH-000053

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.

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