This Programme 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.
Sources of information on the programme can be found in Section 17
Awarding Institution / Body
University of Central Lancashire
Teaching Institution and Location of Delivery
University of Central Lancashire
Preston campus
University Department/Centre
Computing, Engineering and Physical Sciences
External Accreditation
Title of Final Award
BSc (Hons) Bioinformatics
BSc Bioinformatics
Modes of Attendance offered
Full-time
UCAS Code
CG94
Relevant Subject Benchmarking Group(s)
Computing
Mathematics&Statistics
Biology
Other external influences
British Computing Society (BCS)
Date of production/revision of this form
Nov 2009
Aims of the Programme
The recent completion of the Human Genome Project and the use of novel technologies such as DNA chips has revolutionised the life sciences, enabling the biological processes to be studied far more comprehensively than was previously possible. Laboratory-based research generates vast amount of data that requires novel bioinformatics approaches for its analysis, interpretation and storage.
The course will produce graduates with both a range of general transferable skills and a knowledge and understanding of Bioinformatics as a multidisciplinary subject, and will provide students with relevant interdisciplinary theoretical and practical skills for sustained and rewarding employment. In addition, the Bioinformatics/Computing Modules are highly vocational; students may apply their software development skills in other computer-related industries.
To develop an enquiring attitude and to encourage and enable students to become independent learners.
To develop competence in the implementation and monitoring of plans for self-development
To enable students to understand the major ideas and developments in Bioinformatics and related subjects
To develop the ability to understand and define clearly a problem and develop algorithms and computer programs for solving the problem both independently and as part of a team.
To develop the ability to analyse the results of computational analyses.
To enable the student to appreciate the value and limitations of computational methods used in bioinformatics analyses.
To develop communication skills.
To prepare the student for future employment in the biological, biomedical and pharmacological industries, teaching and research.
To provide the student with a suitable multidisciplinary body of knowledge as an appropriate basis for further professional development in a variety of science-based careers (including a higher degree e.g. MSc or PhD).
Learning Outcomes, Teaching, Learning and Assessment Methods
A. Knowledge and Understanding
The successful student will be able to
A1. Describe and explain relevant principles and concepts of molecular biology.
A3. Explain, evaluate and apply a variety of bioinformatic/computational techniques.
A4. Design and implement software tools for analysis and storage of biological data.
A5. Solve biological problems using basic mathematical and statistical approaches.
Teaching and Learning Methods
Formal lectures, tutorials, workshops, discussion forums with peers.
Assessment methods
Essays, program development and evaluation, problem sheets, examinations, dissertation/project
B. Subject-specific skills
The successful student will be able to
B1. Write and modify algorithms and program to solve specific bioinformatic problems
B2. Design and query biological databases
B3. Design and implement a Web application
B4. Conduct scientific research using PC and/or Web-based software tools
B5. Critically evaluate scientific data and perform appropriate statistical tests to analyse data that will be produced by various types of study
Teaching and Learning Methods
Computational laboratory classes, workshops
Assessment methods
Program development and implementation, problem sheets, dissertation/project
C. Thinking Skills
The successful student will be able to
C1. Investigate computational aspects of complex biological problems
C2. Extract relevant information from journal articles, reviews and other sources of reference material (all Level 5 modules)
C3. Evaluate ideas, methods and systems
C4. Analyse and solve problems
Teaching and Learning Methods
Intellectual skills are developed through practical work, tutorial/seminars and coursework assignments. Discussion among students and with staff during tutorials and supervisory meetings are key methods for the development of thinking skills. Problem-solving is developed in practical classes, seminars and tutorials. Throughout the course, students practise problem-solving individually and in groups. Students research, apply and evaluate information during the Bioinformatics-related modules, Case Studies and during the problem-solving project.
Assessment methods
Staff in class and in supervisory meetings provides informal feedback. Intellectual skills are partly assessed through formal examinations but assessment of coursework, practical and theoretical project work is the main vehicle for assessment of the higher order skills. A variety of assessment methods are used, including formal reports, essays, and poster presentations.
D. Other skills relevant to employability and personal development
The successful student will be able to
D1. Write using an appropriate scientific writing style that would be acceptable to an informed reader in the relevant areas of study.
D2. Communicate ideas and conclusions effectively to a scientific audience.
D3. Learn and work independently and as part of a team
D4. Operate within an appropriate ethical and legal framework
D5. Plan, perform, manage and report on a relevant project
D6. Identify and set personal goals relevant to long-term educational and career planning
Teaching and Learning Methods
The development of essential communication and transferable skills begins in the Bioinformatics I module at level L4, alongside the introduction and discussion of relevant legal and ethical topics. Communication skills, legal and ethical understanding are further developed in context in other modules through tutorial/seminar work and coursework assignments. Teamwork is developed in the Bioinformatics modules (CO1808-L4, CO2516-L5) and Case Studies (CO2518-L5) module. An individual project (CO3808-L6), supported by supervisory meetings, reinforces and extends the student’s abilities to research topics relevant to their project, to write a paper summarising and evaluating their findings, to plan and monitor their progress, solve problems and write an extended report.
Assessment methods
These skills are assessed through written coursework in many modules, but particularly the Case Studies project and the final year individual project, where students write a project report, are interviewed, and give a poster presentation.
13. Programme Structures*
14. Awards and Credits*
Level
Module Code
Module Title
Credit rating
Level 6
CO3808
CO3811
CO3812
CO3813
CO3402
Honours Degree Project
Machine Learning and Data Mining
Bioinformatics for ‘omics (transcriptomics, proteomics, metabolomics)
Requires 360 credits including a minimum of 240 at Level 5 or above and 100 at Level 6.
Bachelor Degree in Bioinformatics
Requires 320 credits including a minimum of 200 at Level 5 or above and 40 at Level 6.
Diploma of Higher Education in Bioinformatics
Requires 240 credits including a minimum of 100 at Level 5 or above. It must include at least two of the following modules: CO1808, CO2516, CO3812 and CO3813; otherwise the title will be Diploma of Higher Education.
Certificate of Higher Education in Bioinformatics
Requires 120 credits at Level 4 or above. It must include at least one of the following modules: CO1808, CO1809 or CO2516; otherwise the title will be Certificate of Higher Education.
Students are able to engage in PDP during many of the taught modules. PDP activities are also conducted through meetings with their Course Leader/Personal Tutors. In these sessions students identify past skills, evaluate new skills and consider long-term goal setting and link PDP with employability and enterprise. Case Studies module (CO2518) and the project module CO3808 give opportunities to explore project management skills and evaluate performance.
Meetings with project supervisors allow the students to identify areas of strength and weakness and to develop appropriately. It provides an opportunity to investigate an area relevant to career goals.
In addition, students can undertake a semester-based University Employability Certificate. This enhances the students’ self-awareness and ability to seek employment particularly within computing. Students gain a separate University Certificate in addition to their Degree.
16. Admissions criteria
Programme Specifications include minimum entry requirements, including academic qualifications, together with appropriate experience and skills required for entry to study. These criteria may be expressed as a range rather than a specific grade. Amendments to entry requirements may have been made after these documents were published and you should consult the University’s website for the most up to date information.
Students will be informed of their personal minimum entry criteria in their offer letter.
Two A2 Levels at Grade ‘B’ in a science, maths or computing related subject is expected, plus 5 GCSE’s (including both mathematics and English at Grade ‘C’ or above) or equivalent.
17. Key sources of information about the programme
School Web Site (www.uclan.ac.uk/computing)
Student Handbook
18. Curriculum Skills Map
Please tick in the relevant boxes where individual Programme Learning Outcomes are being assessed
