University home > Unit and programme catalogues in 2023/24 > Programme catalogue > Faculty of Science > School of Mathematics > Computational Statistics and Data Science (PhD) > Specification
Programme code | 2MATH007R |
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Programme type | Postgraduate Research Degree |
Programme director(s) |
Mathieu Gerber
|
Faculty | Faculty of Science |
School/department | School of Mathematics |
Teaching institution | University of Bristol |
Awarding institution | University of Bristol |
Mode of study | Full Time |
Programme length | 1 years (full time) |
This section sets out why studying this programme is important, both in terms of inspiring you as an individual and in considering the challenges we face. It describes how this degree programme contributes to:
The aim of the programme is to provide PhDs with advanced skills in statistics and data science and address the shortfall between the skills that students acquire via typical undergraduate (UG) and postgraduate degrees and the realities of statistical and data science research, COMPASS is distinguished by:
Through our strong links with partners and by facilitating student movement onto their next career stage, COMPASS will fulfill the established need for highly trained people in modern statistical & data science, people who will be readily employed by commerce, government & charities.
The learning outcome statements shown below for your programme have been developed with reference to relevant national subject benchmarks (where they exist), national qualification descriptors (see the Framework for Higher Education Qualifications) and professional body requirements.
Teaching, learning and assessment strategies are listed to show how you will be able to achieve and demonstrate the learning outcomes.
This programme provides opportunities for you to develop and demonstrate knowledge and understanding, qualities, skills and other attributes in the following areas:
Programme Intended Learning Outcomes | Learning/teaching methods and strategies |
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|
Lectures, seminars, workshops, cohort learning modules, practical modules, group research projects, individual research projects. |
Methods of assessment (formative and summative) | |
As appropriate to the subject matter, assessment will be selected from the menu of: oral exam, assessed presentation, group project, computer lab under exam conditions, or written exam. |
Programme Intended Learning Outcomes | Learning/teaching methods and strategies |
---|---|
|
Lectures, seminars, workshops, cohort learning modules, practical modules, group research projects, individual research projects. |
Methods of assessment (formative and summative) | |
As appropriate to the subject matter, assessment will be selected from the menu of: oral exam, assessed presentation, group project, computer lab under exam conditions, or written exam. |
Programme Intended Learning Outcomes | Learning/teaching methods and strategies |
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|
Computer labs, lectures, seminars, workshops, cohort learning modules, practical modules, group research projects, individual research projects. Bristol Doctoral College resources. |
Methods of assessment (formative and summative) | |
As appropriate to the subject matter, assessment will be selected from the menu of: oral exam, assessed presentation, group project, computer lab under exam conditions, or written exam. |
This section describes what is expected from you at each level of your programme. This illustrates increasing intellectual standards as you progress through the programme. These levels are mapped against the national level descriptors published by the Quality Assurance Agency.
Level M/7 - Postgraduate Certificate |
Students are expected to understand the basic concepts of computational statistics, at a level above that of a typical undergraduate course. They are expected to be able to write and speak intelligently about computational statistics. They should have an appreciation for the field at large, as well as their own place within it. |
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Level M/7 - Postgraduate Diploma |
In addition to satisfying the level M Postgraduate Certificate criteria, students are expected to have an advanced understanding of computational statistics, well above the undergraduate level. Have developed programming skills in R and one other language They are expected to have engaged with primary sources of research and demonstrated their own research potential. They should be able to formulate their own opinions that they can defend in a sound scientific manner. |
Level M/7 - Postgraduate Masters |
In addition to satisfying the level M Postgraduate Diploma criteria, students are expected to have accomplished work at the level of current research in computational statistics, working independently under the supervision of an expert. Students at this level will appreciate the demands of modern research and have demonstrated the ability to engage. They are expected to be mature graduate students capable of planning and managing a research project, including conveying results to the research and wider community. |
For information on the admissions requirements for this programme please see details in the postgraduate prospectus at http://www.bristol.ac.uk/prospectus/postgraduate/ or contact the relevant academic department.
The maximum period of study for full-time students is 4 years. This catalogue only shows the taught units on the programme and may not show all years of study.
The MRes is an exit point from the PhD in Computational Statistics and may be awarded to students who leave the PhD following successful completion of the first year of study, comprising the units outlined below.
Unit Name | Unit Code | Credit Points | Status | |
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BootCamp | MATHM0043 | 20 | Mandatory | TB-1 |
Statistical Methods 1 | MATHM0041 | 20 | Mandatory | TB-1 |
Statistical Methods 2 | MATHM0038 | 20 | Mandatory | TB-2 |
Statistical Computing 1 | MATHM0039 | 20 | Mandatory | TB-1 |
Statistical Computing 2 | MATHM0040 | 20 | Mandatory | TB-2 |
Mini-projects | MATHM0042 | 20 | Mandatory | TB-2 |
Research Project | MATHM6301 | 60 | Mandatory | AYEAR |
Computational Statistics (MRes) | 180 |
The assessment of the taught component of a doctoral degree is governed by the Regulations and Code of Practice for Taught Programmes and is assessed separately from the research project. Progression to the research project may be dependent on the successful completion of the taught component - please refer to the relevant handbook for the structure of the particular programme.
The pass mark set by the University for any level 7(M) unit is 50 out of 100.
It may be possible to exit the programme with a taught award. For detailed rules on progression please see the Regulations and Code of Practice for Research Programmes and the relevant faculty handbook.
Students must pass 120 credit points of the taught component to progress to the PhD.
Additional Exit Awards
A Postgraduate Certificate may be awarded on successful completion of 60 credit points and a Postgraduate Diploma on successful completion of 120 credit points.
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.
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