UNIVERSITY OF YORK · Computational Biology are: 1. to provide knowledge of the concepts and methods underpinning research in bioinformatics and computational biology; 2. to provide

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UNIVERSITY OF YORK

POSTGRADUATE PROGRAMME SPECIFICATION

This document applies to students who commence

the programme(s) in:

2014

Awarding institution Teaching institution

University of York University of York

Department(s)

Biology

Award(s) and programme title(s) Level of qualification

MSc in Computational Biology and Bioinformatics Level 7 (Masters)

Award(s) available only as interim awards

Diploma in Computational Biology and Bioinformatics

Certificate in Computational Biology and Bioinformatics

Admissions criteria

For entry to the MSc degree programme, applicants will normally be expected to have (i) a first or

upper second class honours degree in either any biological science (including biochemistry and

applied sciences such as medicine, pharmacology, etc.) or in chemistry with a strong interest in

biomolecules, and (ii) satisfactory references. Additionally, students with a first degree in other

relevant subjects such as Mathematics, Statistics or Computer Science can be accepted on the

course where they can demonstrate a commitment to the Biosciences. Applicants holding a 2ii or

lower due to extenuating circumstances or with additional relevant experience will be considered.

Length and status of the programme(s) and mode(s) of study

Programme Length (years)

and status

(full-time/part-

time)

Start

dates/months

(if applicable – for

programmes that have

multiple intakes or start

dates that differ from the

usual academic year)

Mode

Face-to-face,

campus-based

Distance

learning

Other

MSc 1 year, full time September X

Language of study English

Programme accreditation by Professional, Statutory or Regulatory Bodies (if applicable)

None

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Educational aims of the programme(s)

For the Masters, Diploma and Certificate:

The educational aims common to the Masters programme and the ‘step-off’ Diploma and Certificate in

Computational Biology are:

1. to provide knowledge of the concepts and methods underpinning research in bioinformatics

and computational biology;

2. to provide training in bioinformatics research skills, principally in the areas of sequence to

structure to function, and evolutionary relationships in biomolecules, analysis of large

biological data sets, modelling and simulation of biological systems;

3. to provide training in skills that are widely transferable, such as mathematical, statistical and

programming skills, personal effectiveness, team working, communication and technology

transfer

The educational aims for the ‘step-off’ Certificate of Scientific Investigation

are:

1. To provide training and experience in the planning, execution and reporting of a small

research project

Additionally for the Diploma and Masters (if applicable):

The additional educational aims for the Diploma in Computational Biology are:

1. to apply and develop these skills through an internal research project.

Additionally for the Masters:

The additional educational aims for the Masters in Computational Biology are:

1. to apply and develop these skills through a research placement in academia, research

institutes or industry worldwide.

Intended learning outcomes for the programme – and how the programme enables students to

achieve and demonstrate the intended learning outcomes

This programme provides opportunities for students

to develop and demonstrate knowledge and

understanding qualities, skills and other attributes in

the following areas:

The following teaching, learning and assessment

methods enable students to achieve and to

demonstrate the programme learning outcomes:

A: Knowledge and understanding

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Knowledge and understanding of:

For the Masters, Diploma and Certificate in

Computational Biology:

1. knowledge of the concepts and methods

underpinning research in bioinformatics

and computational biology

2. understanding of the concepts and

methods underpinning research in

bioinformatics and computational biology

3. Knowledge and understanding of the

management and commercialisation of

science

Additionally for the Masters, Diploma and

Certificate of Scientific Investigation:

4. Knowledge and understanding of the

research process

Learning/teaching methods and strategies (relating

to numbered outcomes):

For outcome 1: Through lectures and

seminars

For outcome 2: Through lectures and

seminars and through practicals coupled

(for the Masters and Diploma) with use of

these methods to achieve research

objectives in project work

For outcome 3: Through a set of lectures

and workshops to develop a business

presentation

For outcome 4: Through supervision during

the planning, execution and reporting of

research projects

Types/methods of assessment (relating to

numbered outcomes)

For outcomes 1 and 2: Through open

assessments, reports, data analyses,

presentations and closed exams

For outcome 3: Through a business

presentation

For outcome 4: Through a report, seminar

and poster

B: (i) Skills – discipline related Able to:

For the Masters, Diploma and Certificate

in Computational Biology:

1. use methods underpinning

bioinformatics and computational

biology

Additionally for the Masters Diploma and

Certificate of Scientific Investigation::

Learning/teaching methods and strategies (relating to

numbered outcomes):

For outcome 1: Through lectures and seminars

For outcomes 2 and 3: Through transferable

skills workshops and supervision while working

on a research project

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2. Work independently and

effectively on a research project

3. Plan, execute and report a small

research project

Types/methods of assessment (relating to numbered

outcomes)

For outcome 1: Through open assessments,

reports, data analyses, presentations and closed

exams

For outcomes 2 and 3: Through a presentation,

report, and an Approach and Aptitude

assessment by the supervisor

B: (ii) Skills - transferable Able to:



1. Communicate effectively in a

range of different situations and to

different audiences both verbally

and in writing

2. Identify their own personal

strengths as an individual and in

the context of a team

Additionally for the Masters & Diploma:

3. Interact and communicate

effectively within a research team

4. Report their work in a written

dissertation


numbered outcomes):

For outcomes 1 and 2: Through specific

interactive workshops and opportunities for

practice in assessments

For outcomes 3 and 4: Through specific

workshops and supervision during the planning,

execution and reporting of research project


outcomes)

For outcome 1: Through a number of open

assessments

For outcome 2: Not directly assessed

For outcome 3: Through an Approach and

Aptitude assessment by the supervisor

For outcome 4: Through a dissertation

C: Experience and other attributes

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Able to:



1. Direct experience of a wide range

of computational methods

Additionally for the Masters & Diploma:

2. Experience of working

independently in a research

environment

3. Experience of planning, executing

and reporting research projects


numbered outcomes):

For outcome 1: Through lectures, workshops,

practicals and supervision

For outcome 2: Through supervision while

working on research projects

For outcome 3: Through supervision during the

planning, execution and reporting of research

projects


outcomes)

For outcomes 1: Through open assessments,

reports, data analyses, presentations and closed

exams

For outcomes 2 and 3: Through a presentation,

report and an Approach and Aptitude assessment

by the supervisor

Relevant Quality Assurance Agency benchmark statement(s) and other relevant external

reference points (e.g. National Occupational Standards, or the requirements of Professional, Statutory or

Regulatory Bodies)

University award regulations

To be eligible for an award of the University of York a student must undertake an approved

programme of study, obtain a specified number of credits (at a specified level(s)), and meet any other

requirements of the award as specified in the award requirements and programme regulations, and

other University regulations (e.g. payment of fees). Credit will be awarded upon passing a module’s

assessment(s) but some credit may be awarded where failure has been compensated by

achievement in other modules. The University’s award and assessment regulations specify the

University’s marking scheme, and rules governing progression (including rules for compensation),

reassessment and award requirements. The award and assessment regulations apply to all

programmes: any exceptions that relate to this programme are approved by University Teaching

Committee and are recorded at the end of this document.

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Departmental policies on assessment and feedback

Detailed information on assessment (including grade descriptors, marking procedures, word counts

etc.) is available in the written statement of assessment which applies to this programme and the

relevant module descriptions. These are available in the student handbook and on the Department’s

website:

http://www.york.ac.uk/biology/intranet/current-masters/

Progression, reassessment and exit awards

A Progress Review will be conducted in week 1 or 2 of the Spring term. At this time a student’s

performance in 50 credits will be available.

If a student is required to re-sit 30 or more credits (i.e., marks below 40 in 30 or more credits or

marginal fails in 40 or more credits) or has an average mark below 49.5 then they will normally be

expected to take either the Certificate or Diploma exit route.

A second progress review will be conducted at the end of the Spring term. At this time a student’s

performance in an additional 30 credits will be available as well as an indication of their progress on

the Individual project. If a student is required to re-sit 30 or more credits (i.e., marks below 40 in 30 or

more credits or marginal fails in 40 or more credits) or has an average mark below 49.5 then they will

normally be expected to take Diploma exit route.

Information on formative and summative feedback to students on their work is available in the written

statement on feedback to students which applies to this programmes and the relevant module

descriptions. These are available in the student handbook and on the Department’s website:


Diagrammatic representation of the programme structure, showing the distribution and credit

value of core and option modules

Masters Autumn term Spring term (incl Easter) Summer term Summer

vacation

Professional Skills (10) Placement (60)

Sequence and Structure with

case study (20)

Computational Systems Biology

(10)

Data Analysis (10) Biocomputing and Web

Applications (10)

Introduction to Programming

(10)

Introduction to Pattern

Recognition & Machine Learning

(10)

Statistical Modelling (10)



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Project: Individual (30)

Assessed Total = 50 Assessed Total = 70 Assessed Total = 60

Postgraduate Diploma (if applicable)

Autumn term Spring term (incl Easter)

Professional Skills (10)


case study (20)

Computational Systems Biology

(10)

Data Analysis (10) Biocomputing and Web

Applications (10)


(10)

Introduction to Pattern

Recognition & Machine Learning

(10)

Statistical Modelling (10)

Project: Individual (30)

Assessed Total = 50 Assessed Total = 70

Postgraduate Certificate in Computational Biology

Autumn term Spring term

Professional Skills (10) Literature Review (10)


case study (20)

Data Analysis (10)


(10)

Assessed Total = 50 Assessed Total = 10

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Diagrammatic representation of the timing of module assessments and reassessments, and

the timing of departmental examination/progression boards

Autumn term Spring term Summer term Summer vacation Date of final

award board

Professional skills

(Pass/Fail)

Data Analysis

closed exam.

SpT Wk1

Progression Board

Meeting Wk 8

Research

Placement Ends

Wk 19

Report hand-in

SuT Wk19

SuT Wk 22

External

examiner

Award

Meeting.

Sequence &

Structure with case

study open

assessment (Wk 8)

posters (Wk9)

Computational

Systems Biology

open assessment

SpT Wk8

Statistical

Modelling

Closed exam SuT

Wk1

Wk 25

Reassessment

of ISM module

and SpT

modules.

Introduction to

Programming open

assessment

AuT Wk 10

Biocomputing and

Web Applications

open assessment

SpT Wk 11

Introduction to

Pattern

Recognition &

Machine Learning

open assessment

SpT Wk12

Individual Project

Project work ends

SuT Wk 0, Report

hand-in & Seminar

SuT Wk 0 (i.e. the

week before the

start of SuT, or

earlier to avoid

Easter)

Start Research

Placement SuT Wk

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Award decisions about all students except those who are required to resubmit their Placement report

will be made at the September BOE with the External Examiner in attendance. The External Examiner

will examine the work of all students during his visit. Award decisions about students who had to

resubmit their placement report will be made at the November BOE following consultation with the

External Examiner through email, telephone conversation or conference call where appropriate.

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Overview of modules

Core module table Module title Module code Credit level

i Credit

valueii

Prerequisites Assessment

rulesiii

Timing (term and week) and format

of main assessmentiv deadlines

ISM?v

Professional Skills BIO00048M Level 7 Masters 10 Entry requirements Pass/Fail

Standard

compensatable

AuT Weeks 2

N

Sequence and

Structure with case

study

BIO00032M Level 7 Masters 20 Entry requirements Standard

compensatable

Group poster + individual report

AuT Wk 8

N

Data Analysis BIO00047M Level 7 Masters 10 Entry requirements Standard

compensatable

Closed – SpT Wk1 N

Introduction to

Programming

(Python)

CHE00026M Level 7 Masters 10 Entry requirements Standard

compensatable

Open programming exercise

AuT Wk 10

N

Biocomputing and

Web Applications

CHE00025M Level 7 Masters 10 Introduction to

Programming

Standard

compensatable

Open programming exercise

SpT Wk 11

N

Computational

Systems Biology


compensatable

Open data analysis exercise

SpT Wk 8

N

Statistical Modelling BIO00050M Level 7 Masters 10 Data Analysis Standard

compensatable

Closed – SuT Wk 1 N

Introduction to

Pattern recognition

and Machine

Learning

CHE00025M Level 7 Masters 10 Entry requirements Standard

compensatable

Open data analysis exercise

SpT Wk 12

N

10

Individual Project BIO00023M Level 7 Masters 30 Entry requirements Standard

compensatable

Report + oral presentation +

Supervisor’s mark. SuT Wk 1

N

Literature Review

(Certificate only)


compensatable

Week 3 or 4 SpT N

Research Placement BIO00010M Level 7 Masters 60 Strong academic

standing” at Progress

Review Meeting

NC Report + Supervisor’s mark

SuT Wk 19 / 48 of programme

Y

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Transfers out of or into the programme

None allowed

Exceptions to University Award Regulations approved by University Teaching Committee

Exception Date approved

1. Exemption to the requirement that where

possible ‘students undertake reassessments of

‘taught’ modules before they embark on

significant work on their ISM’

Agreed by the CDT, December 2009

Quality and Standards

The University has a framework in place to ensure that the standards of its programmes are maintained, and the quality of the

learning experience is enhanced.

Quality assurance and enhancement processes include:

The academic oversight of programmes within departments by a Board of Studies, which includes student

representation

The oversight of programmes by external examiners, who ensure that standards at the University of York are

comparable with those elsewhere in the sector

Annual monitoring and periodic review of programmes

The acquisition of feedback from students by departments.

More information can be obtained from the Academic Support Office: http://www.york.ac.uk/admin/aso/

Date on which this programme

information was updated:

19th December 2013

Departmental web page: http://www.york.ac.uk/biology/intranet/current-masters/

Please note

The information above provides a concise summary of the main features of the programme and learning outcomes that a

typical students might reasonably be expected to achieve and demonstrate if he/she takes full advantage of the leaning

opportunities that are provided.

Detailed information on learning outcomes, content, delivery and assessment of modules can be found in module descriptions.

The University reserves the right to modify this overview in unforeseen circumstances, or where processes of academic

development, based on feedback from staff, students, external examiners or professional bodies, requires a change to be

made. Students will be notified of any substantive changes at the first available opportunity.

http://www.york.ac.uk/admin/aso/


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i The credit level is an indication of the module’s relative intellectual demand, complexity and depth of learning

and of learner autonomy. Most modules in postgraduate programmes will be at Level 7/Masters. Some modules

are permitted to be at Level 6/Honours but must be marked on a pass/fail basis. See University Teaching

Committee guidance for the limits on Level 6/Honours credit.

ii The credit value gives the notional workload for the module, where 1 credit corresponds to a notional workload

of 10 hours (including contact hours, private study and assessment)

iii Special assessment rules (requiring University Teaching Committee approval)

P/F – the module is marked on a pass/fail basis (NB pass/fail modules cannot be compensated)

NC – the module cannot be compensated

NR – there is no reassessment opportunity for this module. It must be passed at the first attempt

iv AuT – Autumn Term, SpT – Spring Term, SuT – Summer Term, SuVac – Summer vacation

v Independent Study Modules (ISMs) are assessed by a dissertation or substantial project report. They cannot

be compensated (NC) and are subject to reassessment rules which differ from ‘taught modules’. Masters

programmes should include an ISM(s) of between 60 and 100 credits. This is usually one module but may be

more.

Documents

UNIVERSITY OF YORK · Computational Biology are: 1. to provide knowledge of the concepts and methods underpinning research in bioinformatics and computational biology; 2. to provide