PGCert Genomic Medicine with Data Science (online)

Programme overview

Entry requirements

• Applicants should normally have a bachelor’s degree with at least a 2:1 or equivalent in a relevant scientific discipline, typically one of the biological sciences or natural sciences. Applicants with equivalent knowledge gained through the workplace are encouraged to apply.
• Graduates who hold an honours degree equivalent to a UK lower second class may also be eligible, providing they can demonstrate sufficient experience in a professional environment. Professionally qualified candidates who do not meet the formal qualifications will also be considered. All qualifications must be from a NARIC recognised institution.
• Maths requirement: The course does not assume any prior knowledge of statistics, but students are required to demonstrate their aptitude for and interest in statistics. This could be evidenced through: 1) the successful completion of a statistical/mathematical component as part of their university degree, 2) the successful completion of a recognised professional qualification with a clear numerical component (e.g. ACCA, CFA), 3) A-level Maths (or recognised international equivalent). Other forms of evidence such as professional experience may be considered subject to consultation with the Programme Director.
• Students for whom English is not their first language must meet the University of Leeds entry criteria for English language, IELTS 6.5 overall, with no less than 6.0 in any component. 
  
  TOEFL iBT (Test of English as a Foreign Language Internet-Based Test) or TOEFL iBT Home Edition at 88 overall with no less than 19 in listening, 20 in reading, 22 in speaking and 21 in writing. Please note that we do not accept TOEFL MyBest scores and expect candidates to have met the relevant requirements from a single TOEFL test. 
  
  Other accepted minimum qualifications for English Language skills are outlined in the current Taught Postgraduate Admissions policy. 

Programme specification

The rapid transformation of healthcare through personalised genomic medicine is matched only by the consistently growing demand for talented graduates with the right skill-set.

From early diagnosis, to drugs based on our unique genetic codes, to disease prevention, there is a huge demand for more biomedical scientists with analytical skills. Responding to this gap, this unique course has been designed to directly meet the need for those with both biological knowledge and the computational and analytical interest to drive genomic precision medicine.

Whether you are experienced with data analysis or not, this course will develop your skills and provide extra support for those students who are less confident in their mathematical ability.

You will gain the skills to use large volumes of complex data, encompassing genomics, proteomics, metabolomics, phenotypic data, epidemiology and clinical trial investigations, to improve the understanding of disease mechanisms.

Teaching is delivered across four different schools (Molecular and Cellular Biology, Medicine, Mathematics and Computing) emphasising the considerable importance of interdisciplinarity in this area. Students will learn from expert researchers, with a programme combining biological insight, statistical analysis, computing prowess and clinical relevance. Thus students will experience the full range of Precision Medicine, from the analysis of genomic ‘big data’ to the application of research in clinical practice. They will develop advanced analytical and computational skills specifically relevant to genomics but also, more broadly, to general data analytics; crucially students will learn the inherent complexity of real world applications of these skills. As well as these ‘harder skills, students will learn to communicate complex ideas and arguments to a range of non-specialists, which is vital in interdisciplinary research and to think critically and creatively about research design.

Upon completion, you will find you have an excellent chance of moving into the analytical genomics field in industry, the NHS and academia.

Year 1

(online)

[Learning Outcomes, Transferable (Key) Skills, Assessment]
View Timetable

For the award of Postgraduate Certificate you will study three Foundation modules, as well as one Development module.

Compulsory Modules

You will study the following three Foundation modules.

Code	Title	Credits	Semester	Pass for Progression
OCOM5100M	Programming for Data Science	15	1 Mar to 30 Apr, 1 Sep to 31 Oct
OGDS5100M	High-Throughput Technologies	15	1 May to 30 Jun (2mth)(adv yr), 1 May to 30 June, 1 Nov to 31 Dec, 1 Nov to 31 Dec (2mth)(adv yr)
OGDS5101M	Statistical Methods	15	1 Jan to 28 Feb, 1 Jan to 28 Feb (adv year), 1 Jul to 31 Aug

Optional Modules

You will study one of the following six Development modules.

Code	Title	Credits	Semester	Pass for Progression
OCOM5101M	Data Science	15	1 May to 30 June, 1 Nov to 31 Dec
OGDS5200M	Analytical Skills in Precision Medicine	15	1 Sep to 31 Oct, 1 Sep to 31 Oct (adv yr)
OGDS5201M	Genetic Epidemiology	15	1 Nov to 31 Dec, 1 Nov to 31 Dec (2mth)(adv yr)
OGDS5202M	Clinical Trials	15	1 Jan to 28 Feb, 1 Jan to 28 Feb (adv year)
OGDS5203M	Big Data: Rare and Common Disorders	15	1 Mar to 30 Apr, 1 Mar to 30 Apr (2mth)(adv yr)
OGDS5204M	Statistical Learning	15	1 Jul to 31 Aug
OGDS5300M	Cancer Drug Development	15	1 Mar to 30 Apr, 1 Mar to 30 Apr (2mth)(adv yr), 1 Sep to 31 Oct, 1 Sep to 31 Oct (adv yr)

Last updated: 08/11/2024 12:04:31

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