2020-21 BSc Maths and Stats Programme Design Document

	A	C	F	G	I	K	L	M	N	O	P	Q	R	S	T	U	V	W	X	Y	Z	AA	AB	AC	AD	AE	AF	AG	AH	AI	AJ	AK	AL	AM	AN
1	Programme Information & PLOs
2	Title of the new programme – including any year abroad/ in industry variants
3	BSc in Mathematics & Statistics
4	Level of qualification
5	Please select:				Level 6
6	Please indicate if the programme is offered with any year abroad / in industry variants																				Year in Industry Please select Y/N								No
7																					Year Abroad Please select Y/N								No
8	Department(s): Where more than one department is involved, indicate the lead department
9	Lead Department		Mathematics
10	Other contributing Departments:
11	Programme Leader
12	Dr Christopher Hughes
13	Purpose and learning outcomes of the programme
14	Statement of purpose for applicants to the programme
15	With a BSc degree in Mathematics and Statistics from York, you will have developed your mathematical and statistical skills to be able to confidently analyse complex or unfamiliar problems. Throughout the degree you will learn to use statistical techniques covering a wide range of applications and requiring a high level of sophistication. You will develop skills which will be valuable throughout your career, such computer programming (using a general purpose language and the statistical software package R for data analysis) and the ability to write on technical subjects with clarity and precision. We pride ourselves on being a friendly and inclusive department with high-quality teaching provided in a relaxed atmosphere. You will experience a variety of ways of learning and working, through lectures, small group seminars, group and individual projects, under the careful guidance of our dedicated staff, all of whom are engaged in current research and many of whom are world leaders in their field. In the final year you will write a dissertation on a topic which puts your statistical skills into practice, under the supervision of an expert statistician. By the end you will have mastered the main tools used by statisticians working in the modern world, and have a qualification highly valued by key employers. The excellence of our programme together with York’s reputation as a top university make a BSc degree in Mathematics and Statistics at York an outstanding choice.
16	Programme Learning Outcomes Please provide six to eight statements of what a graduate of the programme can be expected to do. Taken together, these outcomes should capture the distinctive features of the programme. They should also be outcomes for which progressive achievement through the course of the programme can be articulated, and which will therefore be reflected in the design of the whole programme.
17	PLO	On successful completion of the programme, graduates will be able to:
18	1	use the language of mathematics and confidently identify those problems that can be analysed or resolved by standard mathematical techniques. This includes the ability to apply those techniques successfully in the appropriate context.
19	2	investigate unfamiliar problems by adapting and/or synthesising a range of mathematical approaches, with an emphasis on statistical approaches
20	3	use a wide range of statistical tools, including statistical software, to investigate data sets and understand the confidence with which predictions can be made from data. They will also be able to explain the reasoning behind these tools, which tools are appropriate, and the value or limitations of each,
21	4	conduct a study into a specialised area of statistics, by researching material from a variety of sources, and synthesize this material into a well-organized and coherent account.
22	5	communicate complex mathematical ideas clearly in writing, at a level appropriate for the intended audience, and also be able to provide an effective summary of these ideas for non-specialists,
23	6	create mathematical documents, presentations and computer programmes by accurately and efficiently using a range of digital technologies.
24	Programme Learning Outcome for year in industry (where applicable) For programmes which lead to the title ‘with a Year in Industry’ – typically involving an additional year – please provide either a) amended versions of some (at least one, but not necessarily all) of the standard PLOs listed above, showing how these are changed and enhanced by the additional year in industry b) an additional PLO, if and only if it is not possible to capture a key ability developed by the year in industry by alteration of the standard PLOs.
25	n/a
26	Programme Learning Outcome for year abroad programmes (where applicable) For programmes which lead to the title ‘with a Year Abroad’ – typically involving an additional year – please provide either a) amended versions of some (at least one, but not necessarily all) of the standard PLOs listed above, showing how these are changed and enhanced by the additional year abroad or b) an additional PLO, if and only if it is not possible to capture a key ability developed by the year abroad by alteration of the standard PLOs.
27	n/a
28	Explanation of the choice of Programme Learning Outcomes Please explain your rationale for choosing these PLOs in a statement that can be used for students (such as in a student handbook). Please include brief reference to:
29	i) Why the PLOs are considered ambitious or stretching?
30	Each PLO represents a challenge to the student to develop existing skills to a higher level. Through each stage the level of challenge is raised, as more depth or complexity is encountered. In studying mathematics each stage builds naturally on the attainments of the previous one, as foundational ideas are developed into fully fledged theories or methodologies.
31	ii) The ways in which these outcomes are distinctive or particularly advantageous to the student:
32	The outcomes identify six basic areas, which can be summarised as: technique, adaptability, analytical thinking, scholarship, communication and digital literacy. When possessed together they give each student the abilities and understanding to function in any environment where the precision and clarity of mathematical thinking are valuable.
33	iii) How the programme learning outcomes develop students’ digital literacy and will make appropriate use of technology-enhanced learning (such as lecture recordings, online resources, simulations, online assessment, ‘flipped classrooms’ etc)?
34	All students will learn to programme in Java and to write code in the statistical package R. They will also use mathematical typesetting for written projects and for presentations. The project work in all three years develops their skills with using the internet for literature search and review.
35	iv) How the PLOs support and enhance the students’ employability (for example, opportunities for students to apply their learning in a real world setting)? The programme's employability objectives should be informed by the University's Employability Strategy:
36	The PLOs cover a list of skills which are desired by employers: analytical reasoning, confidence with high level mathematics, clarity of communication, flexible thinking, the ability to learn complex ideas quickly and precisely, and digital literacy. Employability skills are also embedded in the curriculum in Mathematical Skills 1 and Mathematical Skills 2
37	vi) How will students who need additional support for academic and transferable skills be identified and supported by the Department?
38	For first year students regular "drop-in" academic support sessions are scheduled into the timetable, as optional support for all first year students. These are run by our Transition Officer. The Mathematics Society runs weekly "Cake and Calculus" sessions in the Department's undergraduate social space (Maths Student Study Centre) during Autumn and Spring term. These sessions are an opportunity for later year students to help first year students, but also a place where all years can come together to work in groups on weekly homework. Mathematical Skills 1 has optional timetabled drop-in sessions (fortnightly) during Spring term to help with the written assignments (particularly the use of LaTeX). Specific student needs related to disability are identified through statements of needs, with the oversight of the department's Disability Coordinator and each student's academic supervisor.
39	vii) How is teaching informed and led by research in the department/ centre/ University?
40	The vast majority of teaching staff are active in research, and through lectures, tutorials and seminars communicate the influence foundational ideas have on making progress in research. Students also explicitly connect with the principles of research through projects (in Math Skills 1 & 2, and the final year dissertation) as well as having the option to choose modules which connect to relatively recent research in their final year.
41	Stage-level progression Please complete the table below, to summarise students’ progressive development towards the achievement of PLOs, in terms of the characteristics that you expect students to demonstrate at the end of each year. This summary may be particularly helpful to students and the programme team where there is a high proportion of option modules. Note: it is not expected that a position statement is written for each PLO, but this can be done if preferred (please add information in the 'individual statement' boxes). For a statement that applies across all PLOs in the stage fill in the 'Global statement' box.
42	Stage 0 (if your programme has a Foundation year, use the toggles to the left to show the hidden rows)
43	Stage 1
44	On progression from the first year (Stage 1), students will be able to:											Global statement
45	PLO 1		PLO 2			PLO 3					PLO 4					PLO 5					PLO 6					PLO 7					PLO 8
46	competently use foundational mathematical techniques		adapt foundational techniques to unfamiliar situations			create and critique elementary mathematical reasoning and understand the importance of sound reasoning					produce, in collaboration with others, a well-researched survey of some elementary idea or foundational tool in mathematics					communicate elementary mathematical ideas clearly and concisely					use computers for (a) elementary mathematical typesetting to produce a written report and slides for presentation (b) elementary statistical analysis.
47	Stage 2
48	On progression from the second year (Stage 2), students will be able to:											Global statement
49	PLO 1		PLO 2			PLO 3					PLO 4					PLO 5					PLO 6					PLO 7					PLO 8
50	confidently perform calculations, or use methods, which require the combination of several foundational techniques, and identify which of those techniques is appropriate.		recognize when some foundational techniques can be applied outside the standard context, and put together two or more techniques to analyse a problem.			reproduce, with understanding and some insight, important examples of analysis of data using a range of statistical tools and be able to justify the choice of tool used					independently perform a literature survey of a renowned or noteworthy mathematical or statistical idea, method or process.					write clearly and concisely, with an appropriate balance between mathematics and English, about well-understood mathematical ideas					write basic programmes in Java, typeset using LaTeX, use R to implement standard tools for the statistical analysis of data, and understand how to search for technical information digitally
51	Stage 3
52	(For Integrated Masters) On progression from the third year (Stage 3), students will be able to:											Global statement
53	PLO 1		PLO 2			PLO 3					PLO 4					PLO 5					PLO 6					PLO 7					PLO 8
54	Individual statements
55	Programme Structure
56	Module Structure and Summative Assessment Map Please complete the summary table below which shows the module structure and the pattern of summative assessment through the programme. ‘Option module’ can be used in place of a specific named option. If the programme requires students to select option modules from specific lists these lists should be provided in the next section. From the drop-down select 'S' to indicate the start of the module, 'A' to indicate the timing of each distinct summative assessment point (eg. essay submission/ exam), and 'E' to indicate the end of the module (if the end of the module coincides with the summative assessment select 'EA') . It is not expected that each summative task will be listed where an overall module might be assessed cumulatively (for example weekly problem sheets). If summative assessment by exams will be scheduled in the summer Common Assessment period (weeks 5-7) a single ‘A’ can be used within the shaded cells as it is understood that you will not know in which week of the CAP the examination will take place.
57	Stage 0 (if you have modules for Stage 0, use the toggles to the left to show the hidden rows)
71	Stage 1
72	Credits	Module				Autumn Term										Spring Term										Summer Term
73		Code		Title		1	2	3	4	5	6	7	8	9	10	1	2	3	4	5	6	7	8	9	10	1	2	3	4	5	6	7	8	9	10
74	30	MAT00001C		Calculus		S										A													E	A
75	20	MAT00010C		Algebra		S										A													E	A
76	10	MAT00011C		Mathematical Skills 1: Reasoning and Communication		S										A									EA	S	A
77	20	MAT00004C		Introduction to Probability and Statistics		S									EA	A
78	20	MAT00005C		Real Analysis													S												E	A
79	20	MAT00003C		Introduction to Applied Mathematics													S												E	A
80	Stage 2
81	Credits	Module				Autumn Term										Spring Term										Summer Term
82		Code		Title		1	2	3	4	5	6	7	8	9	10	1	2	3	4	5	6	7	8	9	10	1	2	3	4	5	6	7	8	9	10
83	40	MAT00035I		Probability & Statistics		S										A													E	A
84	40	MAT00034I or MAT00032I		One of Applied Mathematics or Pure Mathematics		S										A													E	A
85	10	MAT00027I		Mathematical Skills 2		S									A										E	A
86	10	MAT00026I		Linear Algebra		S									E	A
87	10	MAT00030I		Vector Calculus		S									E	A
88	10	MAT00024I		Functions of a Complex Variable													S								E					A
89	Stage 3
90	Students take the 40cr BSc Final Year Project (which must be on a statistics-related topic) as well as the four 10 cr modules at the top of the table. Students then choose 40cr of options (Lists A-C or Practical Data Science with R) so as to ensure an Autumn/Spring credit balance of 40/40, 30/50 or 50/30. Students may replace 20cr of options with electives from other departments subject to the above constraints concerning the total number of credits in each term, and subject to approval by the (Deputy) Chair of the Board of Studies. The elective must be at H-level, with the exception of Languages For All (LFA) modules which may be at any level.
91	Credits	Module				Autumn Term										Spring Term										Summer Term
92		Code		Title		1	2	3	4	5	6	7	8	9	10	1	2	3	4	5	6	7	8	9	10	1	2	3	4	5	6	7	8	9	10
93	10	MAT00003H		Bayesian Statistics		S									E	A
94	10	MAT00017H		Generalised Linear Models		S									E	A
95	10	MAT00021H		Multivariate Analysis													S								E					A
96	10	MAT00045H		Time Series													S								E					A
97	10			Autumn - List A		S									E	A
98	10			Spring - List B													S								E					A
99	20			Autumn/Spring - List C		S							A										A		EA					A
100	40	MAT00004H		BSc Final Year Project		S									A														EA				A
101	10	MAT00058H		Option - Practical Data Science with R													S								EA
102	Optional module lists If the programme requires students to select option modules from specific lists these lists should be provided below. If you need more space, use the toggles on the left to reveal ten further hidden rows.
103	Option List A		Option List B			Option List C					Option List D					Option List E					Option List F					Option List G					Option List H
104	Survival Analysis MAT00018H		Mathematical Finance II MAT00016H			Numerical Analysis MAT00041H
105	Mathematical Finance I MAT00015H		Cryptography MAT00034H
106	Stochastic Processes MAT00030H		Linear Optimization and Game Theory MAT00050H
107	Statistical Pattern Recognition MAT00031H
108	Dynamical Systems MAT00011H
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