Applied Bioinformatics and Genetic Epidemiology (MSc)

How will I be taught?

The programme is delivered face-to-face in Cardiff. You will have access to course materials, links to related materials and assessments via our virtual learning environment.

Each module will be delivered using a small-group classroom-model (< 20 participants per session).You will be expected to commit to approximately 200 hours of effort per module; from this you will receive approximately 60 hours of face-to-face contact. This will be delivered through approximately 20 teaching sessions. Each teaching session will be 3-hours with appropriate breaks.

• Teaching sessions will include a combination of elements such as theory (blackboard learning), computer practical, group-work and tutorial. We believe that application and interaction are crucial for a bioinformatics-based course and there will be an emphasis on these. Where appropriate, some theory will be delivered as lectures.
• Each session will take a variety of forms, but the overall structure will be to achieve 2-3 key learning outcomes per session via introduction to key concepts, conveying relevant up-to-date information and exposure to research methodology and application.
• In the teaching sessions, we will provide feedback on formative assessments and course content. In these elements, you will have the opportunity to discuss themes or topics, to consolidate and get feedback on your individual learning and to develop skills in oral presentation. Communication skills will be developed in tutorials, where you will make individual contributions to group study; for example, by summarising a formative assessment or outcomes from group sessions.
• You will practise and develop theory, intellectual skills, team-work and presentational skills by participating in diverse learning activities, such as solving bioinformatics problems, small-group discussions, oral presentations, independent research tasks and written assignments. 

How will I be assessed?

Our preferred assessment methods include coursework and presentation. You will generally receive two coursework elements; a theory-driven review paper (e.g. a “compare and contrast” essay), and an applied research project.

Coursework and assessments will vary depending on the module and will be designed to test your knowledge and understanding of topics, as well as key intellectual skills, practical skills, and transferable skills.

Coursework may include data analysis, programming exercises, written reports, problem-based exercises, case studies, practical assignments, slide and poster presentations.

Formative assessments and feedback

These are assessments that do not contribute to progression or degree classification decisions.  The goal of formative feedback is to improve your understanding and learning before you complete your summative assessment. More specifically, it helps you to identify your strengths and weaknesses, and helps staff to support you in improving these areas.

The development of skills in data sciences requires practice. You learn through your successes and failures. You will receive short questions and tasks as part of the teaching sessions. These formative tasks will help reinforce your learning and provide useful feedback to the course co-ordinators about where we can improve and focus resources.

Although formative marks will not contribute to progression or degree classification decisions, appropriate investment in these tasks will assist the summative elements of the module.

Summative assessments and feedback

These assessments contribute to progression or degree classification decisions.  The goal of summative assessment is to indicate how well you have succeeded in meeting the intended learning outcomes of a module and will enable you to identify any action required to improve.

How will I be supported?

During induction you will be given an introductory session to help you make the most of tutoring, library services, and available resources.

You will be supported by a personal tutor with the aim of encouraging both academic success and personal well-being and development.

From an academic viewpoint, your tutor is there to provide guidance on any general academic issues/questions arising from the course and to encourage you to pursue self-directed learning. The role of the tutor is not to read or mark assignment drafts, but they can communicate with you about any topic that you may find challenging. You will receive detailed feedback from the module team on completed assignments and you will be able to discuss this feedback with your tutor

During the MSc dissertation stage, you will also be allocated a Dissertation Supervisor who will guide your planning and advise you as you complete your project.

What skills will I practise and develop?

Upon completing the Programme, you will be able to demonstrate:

• a systematic understanding of the principles of statistics, biosciences and computer science that are the foundations of genetic epidemiology and bioinformatics
• a knowledge of current bioinformatics software and databases and their applicability to solving bioinformatics problems in the field and in their own research
• a keen awareness, understanding and critical appreciation of the wide variety of statistical methods available to analyse genetic data together with appreciation of the issues involved in study design
• a knowledge of statistical software used to analyse omics data and the situations in which the packages should/should not be applied.
• integrate acquired knowledge and understanding with practical skills resulting in a sound approach to problem solving;
• synthesise information from a variety of sources at the forefront of Genomics and Bioinformatics;
• critically evaluate different approaches to solving problems and to demonstrate their applicability, strengths and weaknesses;
• formulate hypotheses and use analytical skills to test these hypotheses. Interpret the results and make decisions in the light of all available information in possibly complex and unpredictable situations;
• undertake a semi-independent piece of original research or critical review demonstrating self-initiative, responsibility for planning and carrying out tasks, tackling and solving research problems
• the ability to assess the correct type of analysis for a dataset and to perform it using an appropriate computer package
• competency in accessing and using bioinformatics tools from a variety of sources
• computer programming skills and competency in good program design
• the ability to design, implement and analyse algorithms for bioinformatics applications
• the ability to produce case study reports on major topics within the fields of bioinformatics and biostatistics
• competent research skills such as literature searches
• work independently, to manage his/her own time and to take responsibility for learning required for continued professional development
• effectively manage individual tasks and larger scale projects identifying and resolving possible obstacles
• work as part of interdisciplinary teams, identifying individual’s strengths and weaknesses and allocating work accordingly to meet the team’s objectives
• competently use information technology such as e-mail, word-processors, software tools and the world-wide web
• transfer computing skills and concepts to subsequently unfamiliar packages/languages/databases
• present information, understanding and arguments using effective communication (written and oral) with awareness of intended audience