PROGRAMME SPECIFICATION PROGRAMME SPECIFICATION Programme title: Mathematics and Statistical Science Final award (BSc, MA etc): MSci (where stopping off points exist they should be detailed here and defined later in the document) UCAS code: GG1K (where applicable) Cohort(s) to which this programme specification is applicable: Intakes from 2000 (e.g. from 2015 intake onwards) Awarding institution/body: University College London Teaching institution: University College London Faculty: Mathematical and Physical Sciences Parent Department: Mathematics (the department responsible for the administration of the programme) Departmental web page address: http://www.ucl.ac.uk/maths/ (if applicable) Method of study: Full/part time Full-time/Part-time/Other Criteria for admission to the programme: See: http://www.ucl.ac.uk/maths/prospective-students/undergraduates Length of the programme: 4 years full time. 1 of the 3 years spent abroad is optional. (please note any periods spent away from UCL, such as study abroad or placements in industry) Level on Framework for Higher Education Qualifications (FHEQ) (see Guidance notes) Relevant subject benchmark statement (SBS) Masters Level (Level 7) Mathematics (see Guidance notes) Brief outline of the structure of the programme and its assessment methods: See: http://www.ucl.ac.uk/maths/prospectivestudents/undergraduates/degree-programmes (see guidance notes) Board of Examiners: Professional body accreditation (if applicable): Name of Board of Examiners: Mathematics Date of next scheduled accreditation visit: EDUCATIONAL AIMS OF THE PROGRAMME: To provide a four year degree programme for undergraduate students which is intellectually challenging and rigorous as well as providing a qualification that will enable graduates to be well-placed to continue study for a postgraduate qualification or to enter employment. PROGRAMME OUTCOMES: The programme provides opportunities for students to develop and demonstrate knowledge and understanding, qualities, skills and other attributes in the following areas: A: Knowledge and understanding Knowledge and understanding of: Teaching/learning methods and strategies: 1. Core topics in various branches of advanced pure and applied mathematics, together with statistical science. Lectures, problem classes, tutorials and private study. 2. A range of optional advanced topics in courses which are informed by the scholarship and/or research interests of the staff. 3. The application of critical and analytical reasoning and the presentation of logical and concise arguments. Assessment: Written unseen examinations for all courses apart from the totally computer-based courses that have project based examinations. There is also an additional coursework and/or project component to the assessment for all core courses and most optional courses. B: Skills and other attributes Intellectual (thinking) skills: Teaching/learning methods and strategies: 1. Understanding sophisticated mathematical arguments and rigorous proofs. 2. Comprehension of high levels of abstraction in pure mathematics. Lectures, problem classes, tutorials and projects where appropriate. Assessment: See above. C: Skills and other attributes Practical skills (able to): Teaching/learning methods and strategies: 1. The assimilation and manipulation of substantial bodies of knowledge Written presentations of solutions to problems set as coursework and/or projects. 2. Apply physical insight and mathematical techniques to the solution of problems in applying the techniques of probability and statistics. The accumulation of material necessary to write a report on a project. Written work produced under examination conditions. 3. Develop investigative skills required for problem solving. Assessment: See above. D: Skills and other attributes Transferable skills (able to): Teaching/learning methods and strategies: 1. Structure and communicate ideas effectively. 2. Manage time and work to deadlines. 3. Work independently or within a group. 4. Develop self-confidence and reliance. 5. Use information technology and retrieval systems in acquiring investigative skills. 6. Assess the relevance and importance of ideas and develop the ability to identify the significant aspects in a problem that is necessary in mathematical modelling. Courses with coursework and/or project work introduce information that needs to be assessed critically. Training in the presentation of logical and precise arguments. Contribute to Peer Assisted Learning programmes. Encouragement to participate effectively in discussion groups such as tutorials. Assessment: See above. The following reference points were used in designing the programme: the Framework for Higher Education Qualifications (http://www.qaa.ac.uk/en/Publications/Documents/Framework-Higher-Education-Qualifications-08.pdf); the relevant Subject Benchmark Statements (http://www.qaa.ac.uk/assuring-standards-and-quality/the-qualitycode/subject-benchmark-statements); the programme specifications for UCL degree programmes in relevant subjects (where applicable); UCL teaching and learning policies; staff research. Please note: This specification provides a concise summary of the main features of the programme and the learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if he/she takes full advantage of the learning opportunities that are provided. More detailed information on the learning outcomes, content and teaching, learning and assessment methods of each course unit/module can be found in the departmental course handbook. The accuracy of the information contained in this document is reviewed annually by UCL and may be checked by the Quality Assurance Agency. Programme Organiser(s) Name(s): Dr M L Roberts Date of Production: August 2003 Date of Review: November 2014 Date approved by Head of Department: November 2014 Date approved by Chair of Departmental Teaching Committee: Date approved by Faculty Teaching Committee November 2014 February 2015 APPENDIX The Mathematics and Statistical Science MSci degree programme structure is summarised in the following Table. Year 1 Core Mathematics MATH1101 Analysis 1* MATH1102 Analysis 2* MATH1201 Algebra 1* MATH1401 Mathematical Methods 1* MATH1402 Mathematical Methods 2* 2 3 MATH2101 Analysis 3: Complex Analysis* MATH7102 Analysis 4: Real Analysis* MATH1202 Algebra 2* MATH3101 Real Analysis* MATH2201 Algebra 3: Further Linear Algebra* Mathematics Options Core Statistical Science Statistical Science Options At least 0.5 unit from designated maths options (possibly plus further maths options) STAT1004 Introduction to Probability and Statistics * STAT1005 Further probability and Statistics* STAT1006 Introduction to Practical Statistics* At least 1 unit of 4th level modules (possibly plus other maths options) STAT2001 STAT3001 Statistical Probability and Inference * Inference* STAT2003 Introduction to Applied Probability* STAT2002 Linear Models & Analysis of Variance* STAT7001 Computing for Practical Statistics * One from STAT7002 Social Statistics STAT7003 Optimization Algorithms in Operations Research Mathematics and/or Statistical Science Outside Options 4 At least one unit from designated statistics options (possibly plus further stats options) At least 1 unit of 4th level modules (possibly plus other stats options) Project (1 unit) * At most ½ unit At most ½ unit Normally at most a half-unit of ‘outside’ options may be taken in Years 3 and 4, subject to the approval of the appropriate departmental tutors and the constraints of the timetable. In Year 4, there is a free choice of the 4th level courses offered by the two departments, apart from the restriction of at least one unit to be studied in either department and the compulsory one unit of project supervised in Mathematics and/or Statistical Science.