University home > Unit and programme catalogues in 2022/23 > Programme catalogue > Faculty of Science > School of Mathematics > Data Science (BSc) > Specification
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Programme code | 2MATH025U |
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Programme type | Single Honours |
Programme director(s) |
Christophe Andrieu
|
Faculty | Faculty of Science |
School/department | School of Mathematics |
Teaching institution | University of Bristol |
Awarding institution | University of Bristol |
Accrediting types: |
This programme will meet the educational requirements of the Chartered Mathematician designation, awarded by the Institute of Mathematics and its Applications, when it is followed by subsequent training and experience in employment to obtain equivalent competences to those specified by the Quality Assurance Agency (QAA) for taught masters degrees. (http://www.ima.org.uk/) |
Mode of study | Full Time |
Programme length | 3 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:
This programme aims to foster the student’s interest in and develop knowledge and rigorous understanding of the areas of Mathematics and Computer Science underpinning Data Science, covering machine learning, computing and data management. The degree allows them to gain the skills that enable them to develop Data Science pipelines in order to extract knowledge from real-world data, supporting Science and informing decision making in Industry, Business or Government, while being aware of the ethical implications of the use of these techniques on real world data. The students will be highly employable data scientists by the end of their degree. For degree with Year in Industry: the aim is to provide working experience in a professional environment, applying and developing the Data Science skills acquired during the first two years of the degree. The student will also gain experience in working with Data Science specialists and non-specialists and understand the constraints involved with working in a business or organisation.
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 •Coursework •Computer based practical and lab quizzes •Peer group work•Self-directed learning/library study, use of internet resources. •Projects: discussion with unit organiser and project supervisor/industry mentor where applicable. |
Methods of assessment (formative and summative) | |
•Formative: feedback on problem sheets and practical work. •Formative:feedback from project supervisor. •Summative: coursework/computer practical assignments. •Summative: closed-book examinations. •Summative: project/year in industry written reports where applicable. •Summative: oral presentations. |
Programme Intended Learning Outcomes | Learning/teaching methods and strategies |
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|
• Lectures. • Coursework. • Computer based practicals and lab quizzes. • Peer group work. • Self-directed learning/library study, use of internet resources. • Projects: discussion with unit organiser and project supervisor/industrymentor. |
Methods of assessment (formative and summative) | |
• Formative:feedback on problem sheets and practical work. • Formative: feedback from project supervisor. • Summative: coursework/computer practical assignments. • Summative: closed-book examinations. • Summative: project/year in industry written reports. • Summative: oral presentations. |
Programme Intended Learning Outcomes | Learning/teaching methods and strategies |
---|---|
|
•Lectures. •Coursework. •Computer based practicals and quizzes. •Peer group work. •Self-directed learning/library study, use of internet resources. •Projects: discussion with unit organiser and project supervisor/industry mentor. |
Methods of assessment (formative and summative) | |
•Formative: feedback on problem sheets and practical work. •Formative:feedback from project supervisor. •Summative: coursework/computer practical assignments. •Summative: closed-book examinations. •Summative: project/year in industry written reports. •Summative: oral presentations. |
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 C/4 - Certificate |
The students gain knowledge and understanding of the foundational rigorous mathematical and computer science concepts and tools underpinning data science and start developing the skills required to carry out the data science lifecycle in simple,but realistic,scenarios.They acquire general intellectual skills and attributes necessary for that knowledge and understanding and develop several practical skills. |
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Level I/5 - Intermediate |
The students acquire more in depth understanding and knowledge of core concepts supporting the Data Science pipeline and there is a step-up change in the range and complexity of practical problems the students are expected to be able to tackle on their own.The students also develop an understanding of the human and ethical implications of data analysis and awareness of the world of the enterprise. |
Level H/6 - Honours |
The students have a thorough understanding of the Data Science lifecycle and make informed choices about areas they wish to specialise in, in the light of their professional projector personal tastes. The units they choose explore more advanced topics which nevertheless build on the material learned in the first two years.The project is a culmination of the students’progress through the degree programme and an opportunity to apply the technical, methodological and intellectual skills they have been developing to a Data Science problem suggested by a mentor or that they have identified themselves.By the end of the year they are sophisticated data scientists,expected to be highly employable or ready to start postgraduate studies in Data Science, possibly at PhD level. |
For information on the admissions requirements for this programme please see details in the undergraduate prospectus at http://www.bristol.ac.uk/prospectus/undergraduate/ or contact the relevant academic department.
UG Workload Statement
Success as an undergraduate student depends on you being able to make the transition to self-motivated, independent learning. Programmes are designed to assist you in this development, in many cases by starting with units in which timetabled teaching, such as lectures and practical classes, provides the foundations of knowledge and skills in a subject, moving on to individual research-based work. Over time you will be expected to take increasing responsibility for your own learning, guided by the feedback on your work that you will receive. At the heart of your studies at every level there must be regular and disciplined individual reading, reflection and writing and it is this skill of independent studies, above all others, that will serve you best when you leave the University.
Most programmes use credits and a 20 credit unit broadly equates to about 200 hours of student input. This includes all activities related to the teaching, learning and assessment of taught units.
A component of this is the time that you spend in class, in contact with the teaching staff, which includes activities such as lectures, laboratories, tutorials and fieldwork. Some of this activity may be online and could consist of activity that is synchronous (using real-time environments such as Blackboard Collaborate) or asynchronous (using tools such as tutor moderated discussion forums, blogs or wikis).
In some programmes there are field courses and/or placements that will take place in concentrated periods of time.
Outside scheduled activities you are expected to pursue your own independent learning to build your knowledge and understanding of the subjects you are studying. Such independent activities include, reviewing lecture material, reading textbooks, working on examples sheets, completing coursework, writing up laboratory notes, preparing for in-class progress tests and revising for examinations.
We recognise that many students undertake paid employment. To achieve a sensible balance between work and study, you are advised to undertake paid work for no more than 15 hours per week in term-time.
Professional Programmes
Many undergraduates in the Faculty of Health Sciences will be following the professional programmes of:
For these professional programmes, full time attendance is compulsory unless absence is formally approved. Academic activities are timetabled throughout the 5-day week and student workload is around 40 hours per week on average. Where possible, students in the early years are permitted Wednesday afternoons for sport and extra-curriculum activities. This may not be available in later years of professional programmes as when a student progresses through the curricula there is an increasing exposure to clinical and professional activities. Students in clinic or on placements may need to stay later than core times of 08.00 – 18.00 or even overnight to observe out-of-hours activities. This increasing exposure to clinical activities means that students on these professional programmes often have longer term dates than the University standard. Individual years within programmes are likely to vary in length (for example because of the timings of placements) and further information on this will be found in individual programme regulations. Another important point to note is that many of the assessments sit outside of the standard University examination timetable and are likely to be more frequent meaning that students will more oftentimes be engaged in revision activities and self-directed learning.
Faculty of Health Sciences
Faculty Assessment and Feedback Statement for Undergraduate Students. University of Bristol access only.
www.maths.bristol.ac.uk
Undergraduate admissions
Tel: +44 (0)117 394 1649
choosebristol-ug@bristol.ac.uk
Unit Name | Unit Code | Credit Points | Status | |
---|---|---|---|---|
Analysis | MATH10011 | 20 | Mandatory | TB-4 |
Probability and Statistics | MATH10013 | 20 | Mandatory | TB-4 |
Introductory Scientific Computing | SCIF10001 | 20 | Mandatory | TB-4 |
Algorithms and Programming in C(++) and R | MATH10017 | 20 | Mandatory | TB-4 |
Matrix Algebra and Linear Models | MATH10016 | 20 | Mandatory | TB-4 |
Mathematical Tools for Data Science | MATH10018 | 20 | Mandatory | TB-4 |
Certificate of Higher Education | 120 |
Unit Name | Unit Code | Credit Points | Status | |
---|---|---|---|---|
Statistics 2 | MATH20800 | 20 | Mandatory | TB-1 |
Probability 2 | MATH20008 | 20 | Mandatory | TB-2 |
Perspectives in Data Science | MATH20018 | 20 | Mandatory | TB-4 |
Programming and Data Analysis for Scientists | SCIF20002 | 20 | Mandatory | TB-4 |
Advanced Linear Modelling and Classification | MATH20016 | 20 | Mandatory | TB-2 |
Algorithms and Machine Learning | MATH20017 | 20 | Mandatory | TB-1 |
Diploma of Higher Education | 120 |
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.
University of Bristol,
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Tel: +44 (0)117 928 9000