CA3: Course Specification
CA3: Course SpecificationAlong with the Module Specifications, the Course Specification form the definitivedescription of any qualification awarded by the University. The Academic Quality andStandards Office (AQSO) is responsible for maintaining up to date records of alldefinitive documents (course and module specifications). Any amendments made to theCourse Specification must be submitted to the AQSO via the formal Amendmentsprocess outlined in Section 4 of the Academic Quality and Standards Handbook –Course Amendments.Refer to CA3i Course Specification Guidance for help in completing this template.PART A: About the Course (See Part B for other key information)1. Qualification (award and title):MSc BioinformaticsMSc Bioinformatics Scientist Degree Apprenticeship (ST0649)2. Date of Approval (month and year):June 20173. Delivery Partners, Sites and Recognition: who delivers this course, where? Isit accredited by any professional bodies?Campuses/PartnersRecognised/accredited byUWL (Paragon House, St Mary’s Road)4. Course Description: a short descriptive statement used for publicity (max. 250words):Our Postgraduate Bioinformatics degree is designed to prepare students to beoccupationally competent Bioinformatics scientists. The course providesopportunities for the student to develop key knowledge, skills, and behavioursrequired by industry standards to perform the job of a Bioinformatics Scientist.The MSc Bioinformatics course is a great option for candidates who wish to study fora traditional MSc qualification – it is available full time (FT) over 12 months or parttime (PT) over 24 months. The Bioinformatics Scientist degree apprenticeship optionoffers a great opportunity for candidates who wish to study for MSc qualification withVocational Competency Evaluation (VCE) certification. It takes 30 months full timeCA3 Course Specification Template – October 2019Page 1 of 31
(including 6 months for the End Point Assessment (EPA)) – and is available tostudents who wish to complete the course through an apprenticeship. The courseoffers you the opportunity to study while being employed full time in a bioinformaticssetting.Overall, our courses offer unique opportunities to successfully train in Bioinformaticsand study for an MSc award. During the course you will be exposed to the workingknowledge, skills, strategies, use, analysis, and the interpretation, ethical, legal andsocial impact of biological data. You will cover the principles and practice ofbioinformatics statistics, python, java and R programming, next generation geneticsand genomics synthesis data analyses, data storage on relational databases, nonrelational databases and distributed databases, data processing, data management,data visualisation, artificial intelligence, machine learning, the underlying health andassociations between gene variants, disease susceptibility, drug response andstratification of healthcare.For Apprenticeship onlyOur apprenticeship course is a practical industry based course and it meets therequirement for the Bioinformatics Scientist Standard ST0649. The apprenticeshiptraining is 30 months long: the first 24 months is dedicated to providing you with bothacademic and industrial training opportunities, and supporting you to achieve theMSc in Bioinformatics, and the credits you require to undertake an end pointassessment to certify you as a competent bioinformatics scientist. After successfulcompletion of the Master’s degree component, you will have a further 6 months toundertake the EPA.5. Course Structure Diagram: a visual overview of the programme of studyLEVEL 7Generally, the course content is organised into modules. Modules are normallyweighted as 20 credits or multiples of 20, e.g., the final substantial project module is60 credits. Overall students must complete 180 credits over a period up to 12months FT (or 24 months PT) to be eligible for the MSc Bioinformatics, or 30 monthsto achieve the MSc degree and Bioinformatics Scientist Apprenticeship Certificate.Typically, FT students on the standard MSc Bioinformatics full time pathwaycomplete the course in 3 semesters over 12 months – students study for 120 creditsfrom taught modules in the first 2 semesters (8 months) and complete the project inthe 3rd semester. PT students study for a minimum of 20 credits per semester (3semesters per year) over 24 months (Figure 1b).The Bioinformatics Scientist Apprenticeship (BSA) programme is 30 months longinvolving a 24 months training period, during which the apprentice will spend at least20% of his/her time in Off The Job training to achieve 180 credits at Level 7, followedby a 6 months EPA period (Figure 2) to achieve the MSc Bioinformatics degree andCA3 Course Specification Template – October 2019Page 2 of 31
Bioinformatics Scientist Apprenticeship Certificate. The order of modules will be thesame as the PT MSc Bioinformatics route. The course is structured to progressivelyup-skill the apprentice in knowledge, skills and behaviours to reach competence as abioinformatics scientist. In year 1, students will complete 100 credits. In year 2, theapprentice will complete 80 credits, consisting of a taught module (20 credits) andsupervised project (60 credits). The project is undertaken at the employer’s workplace to consolidate learning, improve on skills, occupational behaviours andcompetence in preparation for the EPA. Over the 24 months period preceding the 6month EPA period, in addition to the 180 credits at Level 7, the apprentice willcomplete their Vocational Competency Evaluation log (Figure 2).Semester 1Semester 2 Introduction to Genetics andGenomics (20 credits) Amended Bioinformatics and FunctionalGenomics PC70059W (20credits) Advanced Bioinformatics andGenome Analysis PC70060W(20 credits)MSc Bioinformatics ProjectPC70061W (60 credits)NB: this module starts in S2 andcompletes in S3Data Science for Bioinformatics PC70062W (20 credits)NB: This module starts in S1 and completes in S2Students select one option from S1optional modules list below: Machine Learning CP70066E (20credits) Information Systems inHealthcare CP70052E (20credits)Students select one option from S2optional modules list below: Data Management in HealthcareCP70053E (20 credits) Big Data Analytics CP70065E(20 credits) The Policy Context ofImprovement (NegotiatedWorkbased Learning)NS70081O (20 credits)Semester 3MSc Bioinformatics Project PC70061W (60 credits)NB: this module starts in S2 and completes in S3Note: PT students and Apprentices study 80 credits in year 1 (two modules persemester) and 100 credits in year 2CA3 Course Specification Template – October 2019Page 3 of 31
6. Course Aims and Content by Level: what is this course all about and how doesthe programme of study build and develop over time?Bioinformatics is a subject that integrates research, statistical, data mining,computational skills and knowledge of biomedical and life science to expand the useof data to solve complex problems in clinically relevant field in health care, biotechand pharmaceutical industries. Therefore, the course aims to provide learningopportunities in line with the bioinformatics scientist training competencies,postgraduate biomedical and computer science subject benchmarks for participantsto:1. Demonstrate practical knowledge and understanding of bioinformaticsprinciples, and develop productive skills and behaviours as independent andinterdisciplinary bioinformatics scientist for both the academic and industryarenas.2. Develop as a specialist who uses computational, data analytical and datamining techniques that are applied to a range of problems in the life sciences.LEVEL 7Students and apprentices will be expectedto be working towards meeting theexpectations set out in the Good ScientificPractice (GSP), QAA biomedical andcomputing subject benchmarks andBioinformatics apprenticeship standardsdocument.Reference Points: The Frameworks for HigherEducation Qualifications of UKDegree-Awarding Bodies Subject BenchmarkStatements CharacteristicsStatements All available kstatements/sbs-biomedicalsciences15.pdf?sfvrsn 3deef781 18 Bioinformatics Apprenticeshipstandards Available renticeshipstandards/bioinformaticsscientist/ Good Scientific Practiceavailable ads/2013/09/AHCS-Good-Scientific-Practice.pdfThe benchmark statements, GSP andvocational standards have been used tosupport the development of this course andwill be used to underpin the process ofjudging individual equivalence.The overarching themes: Professional Practice Scientific Practice Research development and innovation LeadershipOver the duration of the course, studentswill be introduced to modules that havebeen written to support the student toachieve these standards.CA3 Course Specification Template – October 2019Page 4 of 31
Content of the course at Level 7The course is structured with three Progression Points. Progression Point 1 applies to allpathways – FT, PT and Apprenticeship. Progression Points 2 and 3 applies toApprenticeship only.Progression Point 1To successfully meet requirements for Progression Point 1 (PP1), all students mustcomplete the taught and project component and achieve 180 Level 7 credits over 12months for FT students, and 24 months for both PT students and Apprentices (Figure 1),Typically students will complete the modules totalling 180 credits for the Final Award ofMSc Bioinformatics for FT and PT students only. Introduction to Genetics and Genomics (20)Bioinformatics and Functional Genomics (20)Data Science for Bioinformatics (20)Advanced Bioinformatics and Genome Analysis (20),Machine Learning* (20)Big Data Analytics* (20)Bioinformatics Project (60)*Options: student can select alternative modules from list below Data Management in Healthcare (20) Information Systems in Healthcare CP70052E (20 credits) The Policy Context of Improvement (Negotiated Workbased Learning) (20)CA3 Course Specification Template – October 2019Page 5 of 31
Figure 1: MSc Bioinformatics course structureThe Introduction to Genetics and Genomics module prepares students to understandthe key areas of genomics, human genetics and genetic variation, and the genomicsbasis of diseases and how genomics can be used to improve on health outcomes.Students study the genome, variations and analysis, Ethical, Legal and SocialImplications (ELSI) of genomics and personal data.The Bioinformatics and Functional Genomics (BFG) module introduce students topractical sequence and structure analyses techniques, tools and resources, and providesstrong foundation in omics and NGS analyses. It also creates opportunities to practicebioinformatics and computational analysis to problem solving in real healthcare or industryscenarios; and apply the relevant statistical and analytical methods to test hypothesis andgenerate new informationThe Advanced Bioinformatics and Genome Analysis (ABGA) module further developsand extend on the knowledge and skills acquired in the BFG module. This moduleprincipally covers the knowledge and skills underpinning clinical/industrial application ofbioinformatics: students (1) apply knowledge and understanding of DNA, RNA andproteins to develop complex skills for genome and genetic sequence data analysis, (2)apply the understanding of genetic variation and its role to disease modelling, drugsdesign; (3) build on the participants’ genomics, bioinformatics and programmingknowledge to develop an analysis strategy for new services; and (4) create opportunitiesfor effective interdisciplinary group/team working. To prepare students to undertake theirsubstantial independent supervised project in bioinformatics, participants will also beintroduced to bioinformatics research/project design and methodologies in the BFG andABGA modules.To provide students with the practical knowledge and skills to bridge the computohealthcare interface in the context of genomics and bioinformatics, participants will alsostudy Data Science for Bioinformatics. In this module, the student will be introduced tothe theoretical and practical underpinnings of data science in bioinformatics. The modulewill provide opportunities to acquire knowledge and practical skills in areas includingprograming in python, java and R, statistical techniques bioinformatics research design,big data analytics, design of appropriate database and web for computational biology andbioinformatics data storage or dissemination, data visualisation and wiki pages. Thismodule is designed to allow students to shape the content to meet their professionalneeds.In the Machine Learning module, students cover computational algorithms for learningfrom data, data-driven decision making and complex problem solving. This module looksat the underlying principles of artificial intelligence, the advantages and limitations of thevarious approaches and effective ways of applying them. Students will choose oneoptional module of preference (from a list above). Optional modules are subject toavailability and students will be informed of the modules available to choose from at thebeginning of the respective year.CA3 Course Specification Template – October 2019Page 6 of 31
Following successful completion of the taught components, students will undertake thesubstantial piece of supervised Bioinformatics Project culminating in a Dissertation (60credits). For the apprentice, the supervised project will be work-based.Figure 2: MSc Bioinformatics Award StructureStudents who do not successfully complete the requirements for the final award may beeligible, depending on the total credits for an exit award in Bioinformatics (Figure 3).Exit awardsStudents who have 120 credits or more, but less than 180 credits may be eligible for theaward of Postgraduate Diploma in Bioinformatics.Students who have 60 credits or more, but less than 120 credits from the modules listedabove may be eligible for the award of Postgraduate Certificate in Bioinformatics.CA3 Course Specification Template – October 2019Page 7 of 31
For Apprenticeship onlyProgression Point 2To successfully meet requirements to enter Progression Point 2 (PP2) stage, theapprentice must: Have achieved 180 level 7 credits eligible from the taught component (Successfullycompleted PP1). MSc Bioinformatics Scientist will be awarded after a successfulEPA. Apprentice who is unsuccessful at EPA will be awarded standard MScBioinformatics. Present certificates for English and Maths level 2 certificates (or equivalent) Have a completed and signed-off Vocational Competence Evaluation (VCE) log Be certified as competent in bioinformatics practice by employer Have written confirmation by employer that they are ready to undertake the EPA.The apprentice who successfully meets all the requirements for PP1 will advance toProgression Point 2 (PP2) stage. In PP2 stage, the apprentice must undertake the EPA.End point AssessmentThe EPA stage must be successfully completed within a maximum 6-month period. Duringthis period the apprentice will undertake 3 distinct assessments: Complete a Post Training Synoptic Report (6000 words) together with presentationmaterials for discussionUndertake a synoptic report review, followed byGive an Oral Presentation and participate in a discussion (Q and A format)Participate in a viva-style professional conversation supported by a vocationalcompetence evaluation log.The presentation and professional conversation will be organised and managed by anexternal End Point Assessment Organisation (EPAO)The apprentice who successfully meets all requirements for PP2 will be put forward forprogression to the final award stage PP3.Progression Point 3: Final Award – MSc Bioinformatics Scientist and Level 7Degree Apprenticeship CertificateTo be eligible for the MSc Bioinformatics Scientist and Level 7 Degree ApprenticeshipCertificate award the apprentice must have:1. Successfully complete PP1 and PP22. Successfully complete all elements of the EPA7. Course Contact Hours: how much time should I commit to this course?CA3 Course Specification Template – October 2019Page 8 of 31
Learning hours are determined by credits. One credit is worth 10 learning hours, so a20 credit module is 200 learning hours, a 30 credit module is 300 hours etc. This isthe amount of time you should be prepared to commit to each module.Learning hours are divided into: taught or ‘contact’ hours, ie, the amount of timestudents spend in contact with academic staff, whether through face-to-face classesor online learning; and independent study, ie, the amount of time students areexpected to spend on their own study and assessment preparation. Students alsohave one-to-one time with academic staff in personal tutorials.Off-the-job Training for Apprenticeship onlyThe apprenticeship is modelled on the formal apprenticeship training programme asset out in “English apprenticeships”. Within this set up, the apprentice will be in a fulltime employment and spend at least 20% of his/her time in Off the Job training. Partof the ‘off the job’ hours will be spent attending formal teaching at the university - theequivalent of 80 days over 24 months. The University will agree with employers onhow the additional hours to fulfil the 20% “off the job” training will be achievedthrough self-directed learning, blended learning and other activities.University teaching days will use a Day or Block release format (see attachedCourse Planner exemplar).8. Academic Staff:Staff employed on UWL Academic contracts at Lecturer level have a minimumrequirement to have a higher degree in an appropriate discipline and a teachingqualification (PG Cert or Academic Professional Apprenticeship) and/or HEAFellowship. Senior Lecturers have a similar minimum level and in addition shouldeither hold a PhD or be registered on a doctorate programme. Associate Professorand Professor levels are required to have a PhD. All staff on Academic contracts atUWL are required to undertake teaching. Hourly paid teaching staff are also usedacross UWL and these colleagues bring a wide range of professional, specialist andindustry experience to the teaching of our students. The University has made anexplicit commitment to supporting the professional development of its staff throughthe programme of continuing professional development (CPD) managed anddelivered by the ExPERT Academy. All staff on Academic contracts have 5 days ofCPD per annum as part of their terms and conditionsContinue to the next page for Section 9CA3 Course Specification Template – October 2019Page 9 of 31
9. Course Learning Outcomes: what can I expect to achieve on this course?In addition to completing this table, you may need to complete and append Form CA5 (Mapping) to show the relationship of theseacademic course learning outcomes to any PSRB standardsCourse Learning OutcomesLevel 7Relevant modulesA – KnowledgeUpon completion students will:and understandingBioinformatics and Functional GenomicsA1. Demonstrate systematic understanding of Bioinformatics knowledgethrough critical analysis of theoretical/research evidence at theforefront of the practice.Bioinformatics ProjectA2. Show comprehensive and critical understanding of genomics,computation, programming, data science and artificial intelligencetechniques through application to their own research or advancedscholarship in bioinformatics.Introduction to Genetics and GenomicsAdvanced Bioinformatics and GenomeAnalysisData Science for BioinformaticsMachine LearningA3. Through critical analysis demonstrate originality in the application ofknowledge, together with a practical understanding of howestablished techniques in genomics, programming and data analytics Optional modules:research are used to create and interpret knowledge inInformation Systems in Healthcarebioinformatics.Data Management in HealthcareA4. Demonstrate a critica
required by industry standards to perform the job of a Bioinformatics Scientist. The MSc Bioinformatics course is a great option for candidates who wish to study for a traditional MSc qualification – it is available full time (FT) over 12 months or part time (PT) over 24 months. The Bioinformatics Scientist degree apprenticeship option
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