Departments of Chemistry and Mathematics MSc Chemoinformatics

C h e m o i n f o r m a t i c s - the application of mathematical and computational techniques to the analysis of
chemical data
Departments of Chemistry
and Mathematics
MSc
in
Chemoinformatics
Student Handbook
2010-11

CONTENTS
1. Welcome
2. Purpose of this handbook
3. Chemoinformatics
4. Being an MSc student
5. Nature of the MSc course and the assessments
6. Assessment for the MSc degree
7. The course structure
8. Module descriptions
9. Handing in written assignments, practical assignments and the dissertation
10. Lateness penalties
11. Size restrictions
12. Disclosure of marks/feedback meetings
13. Marking criteria
14. Mark record sheets and course transcripts
15. Appeals procedure
16. Academic misconduct: University guidelines
17. The York Award
17. Careers
18. Facilities
Library
Computing
Photocopying
Telephones
19. The Department of Mathematics
20. Staff involved with the MSc and their research interests
21. Supervisors
22. Welfare Support Services
23. Contact Details
24. Presentations
25. Dissertation Guidelines

1. Welcome
First of all, welcome (or welcome back!) to the Mathematics department at York.
2. Purpose of this handbook
This handbook is designed to give you information about the
MSc in Chemoinformatics.
Our intention is to make the course structure and the assessment process as clear as possible.
If you have any queries about the course as a whole please contact:
For General Queries Nick Page: njp503@york.ac.uk
For Examination and Assessment Queries Assessment secretary: maths-exams@york.ac.uk
For Queries about Computing Facilities Henning Bostelmann: hb540@york.ac.uk
For Queries about External Placement Julie Wilson: julie@ysbl.york.ac.uk
This handbook also contains a variety of other information concerning the University, Careers
Issues, The Department of Mathematics, Library and other facilities; and also briefly lists
opportunities for developing scientific and personal contacts.
You should make sure that you have the following documents, from which further information
can be obtained:
Graduate Prospectus
Ordinances and Regulations of the University
For general enquiries the staff of the university Graduate Schools Office, which is situated in
the Student Services Building, between Computing Service and Vanbrugh College, will
always be glad to help you if they can, or to tell you where to go next. The telephone number
for enquiries is 4684. The Assistant Registrar responsible for graduate students is Philip
Simison (telephone number 2141). Other information is available on the World Wide Web at
http://www.york.ac.uk/admin/gso
Your supervisor will be able to help with queries on the course and the general interests of
graduate students in the university are looked after by the Students' Union and the Graduate
Students' Association.
3. Chemoinformatics
Chemoinformatics is a multi-disciplinary science at the interface of chemistry, mathematics
and computer science, that involves the organisation, retrieval, management and processing of
chemical information. New technology, advances in methodology and increased

computational power have led to an enormous increase in the amount of data generated in
analytical and computational chemistry. The manipulation, interpretation and presentation of
chemical information underpin contemporary scientific research and the chemical,
biochemical and pharmaceutical industries are increasingly reliant upon intelligent data
management and analysis. Industry today requires people adept at statistical treatment of data,
data analysis, database design, information management and computer programming who also
know their chemistry.
The need for mathematically trained scientists to deal with this wealth of data has been
recognised and chemoinformatics has been identified as an EPSRC industrial sector priority.
However this is not yet reflected in the postgraduate courses available and chemoinformatics
training is usually limited to a single module within a theoretical chemistry course. Courses at
UMIST and Sheffield have been set up recently in response to an EPSRC initiative; the
department of Information studies at Sheffield offers an MSc in Chemoinformatics and the
department of Chemistry at UMIST offers an MSc entitled Cheminformatics. Both of these
courses are targeted towards chemistry graduates.
The course is suitable for mathematics and computer science graduates with an interest in the
application of mathematical and computational techniques to chemical problems as well as
mathematically–inclined students with a chemistry, physics or biology background.
The proposed programme exploits the skills and interests in the departments of Mathematics
and Chemistry and addresses the need for interdisciplinary skills. It connects the advances in
mathematics and statistics with the needs of industry and the life sciences while providing the
students with the chance to develop generic skills and to undertake a substantial research
project to be carried out either at the University or in industry. The course is appropriate for
those students wishing to pursue research in this area and for those seeking a career in industry
with employment opportunities likely in the pharmaceutical, chemical and information
industries. The job market in chemoinformatics is buoyant as judged by the number of
advertisements for vacancies. It is anticipated that the majority of students will attend full time
for a year, but there is flexibility to allow part-time study over two years with a variable
programme structure.
If you have ideas for a placement then please do follow up those ideas and also keep us
informed. In any case you should read the Careers advice in section 19 of this document.
4. Being an MSc student
You will probably find that your life as a graduate student is rather different from what you
were used to as an undergraduate. However, the differences are unpredictable and will vary
with the individual. The following are some of the possibilities that you should be prepared
for.

You may already be expecting a discontinuity in the nature and the amount of mathematics
that you have to learn. This is similar to the discontinuity between school and university
mathematics. You may be used to acquiring mathematical knowledge only from lectures; if
so, you will have to acquire the habit of reading mathematical books and papers. You should
try to develop the skill of being selective in the way you cover material; it is often not
necessary to know every detail of every proof, and even when you do need to understand the
details it can be helpful to start with a preliminary reading in which you concentrate on
definitions and the statements of theorems, skipping the proofs.
You are one of a group of students working in the same area and if you tended to work on
your own as an undergraduate, you might find that the MSc is a more social activity than you
had expected. It can be very helpful and enjoyable to discuss problems and to share ideas with
others. This is still true even if you are the sort of person who needs to be on their own to
think effectively. You can still pick up the bones of an idea or a problem from conversation,
without feeling that you really understand it, and then go away and think about it by yourself
until you do understand it. It might be only after doing this that you have some contribution to
make to a discussion. On the other hand, it can be very helpful in the development of your
own ideas to talk about them to other people. Even if they don't respond, the act of expressing
your ideas can help to clarify them to yourself. Unnoticed mistakes often come to light this
way.
If you are to get the maximum benefit from the course, there are two important don'ts to bear
in mind: never be afraid to admit your ignorance, and never be afraid of asking a silly
question. These are easy to state, and hard to follow. But you should try.
Notice Boards
The notice boards in the corridors show a lot of information that may be of interest to you.
There is also a graduate notice-board: you should get into the habit of looking at this regularly.
Seminars
Seminars and other contributions from visiting speakers will be arranged specially for the
MSc, and you will be notified of those.
Also the department regularly invites mathematicians from other places to give seminars on
their work; these usually take place on Wednesday afternoons at 4:30 pm in a room to be
advertised. Tea is served beforehand, at 4 pm (probably in either Goodricke Senior Common
Room or in the Departmental Coffee Room G109). A list of each term's seminars is produced
at the start of the term. Your supervisor will alert you to speakers who may be relevant to
your interests. You should also see the departmental research list at
http://www.york.ac.uk/depts/maths/research/welcome.htm

5. Nature of the MSc course and the assessments
The course comprises:
Lectures, seminars, computer workshops, a dissertation, a short presentation summarising
your dissertation and (if possible) a placement.
Placements
These are not guaranteed for everyone but they are intended to be an important part of the
course. If you do go on a placement outside the University of York, the dissertation should
ideally be written around an industrially-inspired project while on that placement.
Placement opportunities are currently being sought and an up-to-date list of opportunities
currently available will be maintained by Dr Julie Wilson and circulated from time to time.
Lectures
Outline syllabuses for those modules in the Autumn Term are available from the lecturer
responsible for teaching the module and some cases on a website (to be announced).
Seminars, laboratory workshops
Seminars and laboratory workshops will be an integral part of the lecture courses.
Assessments
The assessments comprise:
marked written assignments,
marked practical assignments or projects,
an assessment of the dissertation, and
formal examinations.
Assessments arising from the marked written and practical assignments will accumulate
throughout the first two terms of the course. For some courses there will be a formal closed
examination. A brief summary of the assessments is given below.
Written assignments and practical assignments
These will be marked and returned by the lecturer giving the course.
Marking guidelines
General guidelines for marking Masters work are given below. You should also read the
assessment details given in the module synopses of your course outline.

Project reports
Abstract (5%) Introduction (20%) Methods (20%) Results and Analysis
(25%)
80– 100 Excellent.
Correct length.
Informative,
Grammatical
and concise.
70 – 79 Very good.
Correct length.
Informative and
Mainly
grammatical
and concise.
60 – 69 Good.
Some what less
Grammatical
and concise.
Very clearly defines the
question. Fully
evaluates the current
knowledge
through appropriate
references. Critical,
thoughtful and incisive.
Defines and evaluates
the question well
through a good selection
of references. A good
summary of the subject.
Defines and evaluates
the question well with a
reasonable selection of
references but with a
less well constructed
storyline than for above.
Very clear and precise
description of very well
chosen and cleverly
designed methods.
An elegantly designed
programme with good quality
documentation.
Clear and precise description
of well-chosen and well
designed methods.
An elegantly designed
programme with good quality
documentation.
Reasonable description of
methods but less well
chosen or designed than for
above. A well designed and
documented programme.
Excellent and entirely
appropriate analysis
Excellent description that
goes beyond the obvious.
Programme works and
goes beyond specification
requirements.
Very good and appropriate
analysis with sensible
deductions. Programme
works and
specification requirements
clearly met.
Good analysis but may be
inappropriate in places.
Misses some evidence.
Programme mainly
worked.
Discussion (25%) Reference list (5%)
Clever, complete and
wise discussion of
results in relation to those
of others.
Excellent link to aims of the
project and
impressive further ideas.
Good discussion of results
in relation to those of
others. Good
link to aims of the project
and sound further ideas.
Reasonable discussion
of results in relation to
those of others.
Reasonable link to aims
of the project but some
impractical further ideas.
Appropriate number of
references that are well
formatted, and
consistently cited.
Appropriate balance of
general and specific
references and evidence
of
extensive literature
searching.
Good number of
references
which are well formatted,
and
consistently cited.
Appropriate balance of
general and specific
references and evidence
of
some literature searching
Reasonable number of
references which are
mainly well formatted, and
consistently cited.
Inappropriate balance of
general and specific
references. Limited
evidence
of literature searching.

50 – 59 Reasonable.
Some
sentences
wordy or
ungrammatical.
Adequate
description of
methods but
unclear in
places.
40 – 49 Weak.
More than 20
words too
long/short.
Wordy and
ungrammatical.
0 – 39 Distinctly
inadequate or
absent
Adequately defines the
question through
adequate references
with a poorly structured
storyline.
Poorly defines the
question by not placing
it in context by the use
of a poor selection of
references. Weak
storyline and limited
understanding.
The purpose of the
research is very unclear.
Very poor choice of
references. Lack of
understanding.
Adequate selection or
design of methods but some
poor choices.
A reasonably well-designed
programme.
Unclear description of
methods that were poorly
chosen or poorly designed.
Reasonable attempt at
programme design.
Very unclear description of
methods. Very limited
selection of poorly chosen
methods. Poorly designed
programme.
Adequate but somewhat
inappropriate analysis.
Programme mainly
worked.
Inadequate or mainly
inappropriate analysis.
Programme doesn't meet
several of the
specifications or only
partially works.
Very weak or no real
analysis
which was not appropriate.
Programme doesn't meet
many of the specifications.
Adequate discussion of
results in relation to
those of others. Limited link
to aims of the project and
impractical
further ideas.
Inadequate discussion of
results in relation to those
of others.
Inadequate or no link to
aims of the project with
impractical or no further
ideas.
Very weak discussion
with little reference to
the results of others and no
link to the aims of the
project.
Reasonable number of
references with format
and citation weaker than
above.
Does not have the
appropriate balance of
general and specific
references. Little
evidence of literature
searching.
Inappropriate number of
references with poor
format
and citation Does not
have the appropriate
balance of general and
specific references.
Distinctly inadequate or
absent.

Report presentation
80 – 100
70 – 79
60 – 69
50 – 59
40 – 49
0 – 39
Beautifully written. Very clear. Very few spelling or grammatical errors which are minor. Very
attractive layout which is clear and entirely appropriate. Excellent use of figures and tables
which are referenced in the text. Figures easy to understand and with complete legends.
Consistence in style. Appropriate appendices.
Well written and fairly clear. A few spelling or grammatical errors. Reasonably attractive.
Possibly some inconsistency in style but generally nice layout. Good figures and tables which
are referenced in the text. Most of the figures easy to understand. Appropriate appendices.
Adequately written but with some laboured or poorly written sections. Adequate use of figures but which may be
hard to understand and/or untidy in places. Some spelling or grammatical errors. Inconsistency in style. Possibly
some unnecessary appendices which those that should be in the main body of the work.
Poorly written and rather scruffy. Many spelling or grammatical errors. Inconsistency in style.
Little thought to layout. Inadequate use of figures which are hard to understand, untidy and may not be referred to in
the text. Inappropriate use of appendices.
Poorly written and seriously flawed use of English. Many spelling and grammatical errors which make the work hard
to understand. Poor layout. Figures inappropriate or absent, difficult to understand, very untidy and not referred to in
text. Inappropriate use of appendices.
Very poorly written and seriously flawed use of English which makes the work nearly
impossible to understand. Many spelling and grammatical errors. Very poor layout. Figures
inappropriate or absent, difficult to understand, very untidy and not referred to in text.
Inappropriate use of appendices.

Supervisors Assessment of Attitude and Approach
80 – 100
70 – 79
60 – 69
50 – 59
40 – 49
0 – 39
A great deal of initiative and maturity shown with minimal supervision required.
Highly capable with a real interest in developing technical and/or research skills as
appropriate. Worked very hard and was very willing to be involved in all aspects of
the work. Where relevant, very good team member with excellent interactions with
members of the lab. Very well organised and efficient.
Good initiative and maturity shown with limited supervision required. Capable and
interested in developing technical and/or research skills as appropriate. Worked hard
and was willing to be involved in all aspects of the work. Where relevant, good team
member with good interactions with members of the lab. Well organised and
efficient.
Reasonably good initiative and maturity shown but requiring more supervision and
guidance than above. Reasonably capable and interested in developing technical
and/or research skills as appropriate. Worked fairly hard and was willing to be
involved in most of the aspects of the work.
Where relevant, reasonable team member with acceptable interactions with
members of the lab.
Reasonably well organised and efficient.
Required some direction and help with the work and solving problems. Adequately
capable and interested. Student worked sufficiently hard but could have done more.
Where relevant, acceptable team member and interactions with members of the lab
adequate. Organisation and efficiency adequate but could have been better.
Required a lot of direction and help with the work and solving problems. Little effort
or commitment, poor time keeping. Only superficial interest shown. Where relevant,
poor team member. Poor organisation and inefficient working.
Required pushing to do the minimum. Inadequate effort and commitment. No
interest shown.
Where relevant, no integration with the team. Unorganised and inefficient. Failed to
appear with no prior explanation.

Dissertation
Your dissertation will be the main fruit of your year of work as a graduate student, and
naturally you will want it to be something you can be proud of. (However, you should not let
your own high standards tempt you to delay the writing of your dissertation, or to try to make
it better than it should be for the degree for which you are submitting.) The dissertation will be
written either on placement or at York. There will be an individual at any placement who will
also help supervise the dissertation.
The University's requirements for the format and binding of dissertations and theses are set out
in the pamphlet Regulations relating to Theses, which can be obtained from the Graduate
Office. These regulations are enforced when the dissertation is lodged with the Library. Since
we do not do this the expectations of the Mathematics department for the MSc dissertations
are somewhat more flexible
Three printed copies of each dissertation should be handed in.
These will normally be assessed by the dissertation supervisor and a second marker, and by
the external examiner. As far as length of the dissertation goes:
Your dissertation should normally consist of between 50 and 80 A4 pages.
A guideline for the marking scheme of the dissertations is available at the end of this
document as part of item 25 on Dissertation Guidelines.
University requirements for the presentation of dissertation theses.
Dissertations for the degrees of MSc shall be presented in accordance with the requirements
given in the University guidelines:
http://www.york.ac.uk/admin/gso/exams/thesis/requirements.htm
The requirements refer primarily to printed copies. Electronic copies shall be identical in
presentation to the printed copies submitted or deposited.
Quality of reproduction
All copies shall be legible and of good print quality.
Text should be set with even or proportionate spacing between words. Word division at the
ends of lines should be avoided if possible. In typescript or printout, one-and-a-half line
spacing or equivalent shall be used, although double spacing may be used if necessary. Lines
that contain mathematical formulae, diacritical marks or strings of capital letters may need
additional space. It should be clear when a new paragraph is starting and where matter in the
text is being quoted.
Tables

If there are relatively few tables, each shall appear as near as possible to the first reference to it
in the text. If there are frequent references to tables, or if there are many tables, they may be
collected together at the end of the text, possibly as an appendix.
Each table shall, if possible, appear complete on one page. A table shall be neither spaced out
to fill the available space nor reduced to fit a small space. Tables shall normally be in the same
orientation as the main text.
Each table shall have a number and title. The number shall precede the title. The title shall
describe the content of the table. If a table occupies more than one page, its number shall be
given on each page.
The character size used in tables shall be large enough to allow the table to be reproduced
without risk to legibility. The presentation of a series of tables shall be consistent in character
size, use of space and other typographic treatment.
Headings
Headings shall be used to indicate the hierarchical structure of the text. There shall normally
be not more than four levels, including the chapter headings as the first level. Each level shall
be distinguished from the others by position or typography, or both. The space that precedes
and follows a heading shall be not less than the space between paragraphs. Headings shall not
normally be centred (except, possibly, for chapter and part headings).
Illustrations
An illustration should normally appear near the first reference made to it in the text. The
desirability of grouping illustrations at the back of a volume or in a separate volume shall be
considered if they need to be compared with one another, are referred to frequently in the text,
or need to be separate because of their nature, e.g. their size or method of production.
Illustrations shall be of a technical quality that reproduces well.
Every illustration, including appendices and material that cannot be bound, shall be included
in the list of illustrations with page numbers or other identification.
Each label within an illustration shall be either so positioned that the part it applies to cannot
be confused with any other, or linked to the part by a thin line. The lettering shall be large
enough and clear enough to remain legible when the illustration is photographed and
subsequently copied. A short legend shall appear beneath each illustration.
Numbering
Arabic numerals shall normally be used for numbering all sequences within a dissertation.
Page numbers shall be visibly clear of the text. The pages of the thesis or dissertation shall be
numbered in a single sequence beginning with the title page, which shall be counted but not
numbered, and including pages that carry tables, illustrations, appendices, etc.

Chapters shall be numbered from the start to the finish of the thesis or dissertation.
Appendices shall be numbered in a separate sequence from that used for chapters.
Illustrations shall be numbered consecutively in a single sequence, generally without
distinguishing between different kinds of illustration. Tables within the text shall be numbered
consecutively in a single sequence, separate from illustrations.
References
A bibliographical reference shall be given for every work, published or unpublished, cited in
the text. References may be identified by one of two methods, either:
by numbers typed as superscripts, or, if on the line, in brackets, immediately following the
relevant word or phrase in the text; or
by citing the author's name and the date of publication in brackets immediately following
the relevant word or phrase in the text.
Sequence of material
Material shall be arranged in the following sequence:
Title and subtitle. The title page of every volume shall give the following information in the
order listed:
the full title of the thesis or dissertation and any subtitle;
the total number of volumes, if more than one, and the number of the particular volume;
the full name of the author, followed, if desired, by any qualifications and distinctions;
the qualification for which the thesis or dissertation is submitted (e.g., PhD or MA);
the name of the University;
the name of the Department or Centre in which the research was conducted;
the month and year of submission.
Abstract. The abstract shall follow the title page. It shall provide a synopsis of the thesis or
dissertation, stating the nature and scope of work undertaken and the contribution made to
knowledge in the subject treated. It shall appear on its own on a single page and shall not
exceed 300 words in length. The abstract of the thesis or dissertation may, after the award of
the degree, be published by the University in any manner approved by the Senate, and for this
purpose, the copyright of the abstract shall be deemed to be vested in the University.
List of contents. It shall list in sequence, with page numbers, all relevant sub-divisions of the
thesis or dissertation, including the titles of chapters, sections and subsections; the list of
references; the bibliography (if any); the list of abbreviations and other functional parts of the
whole thesis or dissertation; appendices (if any); and the index (if any). If a thesis or
dissertation consists of more than one volume, the contents of the whole thesis or dissertation
shall be shown in the first volume and the contents of subsequent volumes in a separate

contents list in the appropriate volume.
List of tables, illustrations, etc (if any).
List of accompanying material (if any).
Preface (if any).
Acknowledgements (if any).
Author's declaration. When submitting the thesis or dissertation, the author shall draw
attention to any material contained in it that has been presented before. If the thesis or
dissertation is based on joint research, the nature and extent of the author's individual
contribution shall be stated. The declaration shall follow the acknowledgement, under a
separate heading.
Text , divided into sections (Introduction; Materials and Methods; Results and Discussion,
Conclusions).
Appendices (if any). Appendices may consist of material of considerable length or of lists,
documents, commentaries, tables or other evidence that, if included in the main text, would
interrupt its flow. The presentation of appendices, including character size, shall be consistent
with the style of the main text.
Definitions (if any). Definitions of any terms specific to the thesis or dissertation, including
abbreviations and codes used in illustrations, shall be given.
Glossary (if any). Terms that require explanation shall be defined in a glossary, which shall
include a key to any abbreviations used. For an abbreviation not in common use, the term shall
be given in full at the first instance followed by the abbreviation in brackets.
List of references.
Index (if any).
6. Assessment weightings for the MSc degree
Marking of examinations is generally out of 100 with a pass mark of 50%.
Marks from returned assessments are considered to be “un-moderated” and are subject to
alteration by examiners until the final examiners‟ meeting at the end of the course. An external
examiner assists with the formal assessment process providing an independent view when
borderline cases are considered and also giving advice on the whole assessment procedure.
Your overall results will be classified by one of the three possible outcomes:

Distinction : 70% or above overall weighted average and 70% or above mark on
dissertation.
Pass : 50% - 69% overall weighted average.
Fail: less than 50% overall weighted average.
These criteria state the necessary conditions for a Distinction and a Pass. When these
necessary conditions are met, it remains at the discretion of the BoE whether or not to award
the respective degree classification.
The criteria for being recommended for a Pass with Distinction are as follows. Candidates will
need to:
(i) attain a final mark of at least 70%,
(ii) achieve a mark of at least 70% in the dissertation, and
(iii) satisfy the external examiner from their coursework, examination results and
dissertation that they are of a standard worthy of distinction.
The award of the MSc degree depends upon completion of all aspects of the taught course
(comprising 100 credits) and the completion of a dissertation. The dissertation for the MSc
will comprise 80 credits. The total number of credits taken must be 180.
Important: Your performance in the taught part of the MSc will be evaluated around the end
of the Easter Vacation. If you have scored less than 50 in the taught part of the course, you
may not be allowed to proceed to the dissertation stage. The MSc Steering Committee will
decide if you are able to continue in this case.
Size restrictions
Word counts should be given on all reports, essays or other assessed work that
specifies a word limit. If you do not include a word count on a project report, five marks (of
one hundred) will be deducted from the presentation component of the assessment.
Misrepresentation of the word count will be treated as Academic Misconduct. The word count
should include the main text but not the abstract, tables, figures, reference list or appendices.
You should also bear in mind that essays that exceed the word limit may also contain
irrelevant information, repetition and/or other features that could affect your mark. Work that
exceeds the specified word length will be penalised. The work will be marked as normal (out
of 100), then one mark will be deducted for every 200 words above word limits between 4000
and 8000 or every 100 words above word limits of 4000 or fewer. The penalty applies only to
that component of assessment, for example, a project report that exceeds the word limit, not
the project mark as a whole.
Project reports require an abstract of approximately 250 words.
7. The course structure
The course is split into three terms: Autumn, Spring and Summer.
In the Autumn term students will take the following taught modules with associated practical
classes:

Research and Presentation Skills (10 credits)
Statistics and Probability (10 credits)
Introduction to Signal Processing (10 credits)
Introduction to Programming (PYTHON) (10 credits)
Structural Bioinformatics (10 credits)
In the Spring term the courses expand upon the topics of the first term including more
advanced theory and techniques. The mandatory taught modules and practical classes are:
Chemometrics (10 credits)
Introduction to Machine Learning (10 credits)
Biocomputing and Web Applications (10 credits)
Chemical informatics (10 credits)
In addition students will choose one module from the following:
Current Research Topics (10 credits)
Stochastic Processes (10 credits)
The Summer term is devoted entirely to the work towards the MSc dissertation. Placements
may be within the University or be a project supervised by someone from one of our industrial
partners.
Abbreviations: JW (Julie Wilson, Maths/Chemistry), JL (Jason Levesley, Maths), ZC (Zaq
Coelho, Maths), RL (Richard Law, Biology), GD (Gustav Delius, Maths), GM (Garib
Murshudov, Chemistry), IM (Ian McIntosh, Maths), JC (James Cussens, Computer Science),
JP (Jon Pitchford, Biology), RA (Rashid Asher, Computer Science), BW (Bo Wang, Maths),
YZ (Yuriy Zakharov, Electronics), RH (Rod Hubbard, Chemistry), KC (Kevin Cowtan,
Chemistry).

Module
Title
Transferable and
Generic Skills
Statistics
and Probability
Introduction to Signal
Processing
Introduction to
Programming
(PYTHON)
Structural
Bioinformatics
Chemometrics
Introduction to Machine
Learning
Current Research
Stochastic Processes
Biocomputing and Web
Applications
Chemical Informatics
TOTAL 100 *
(*) You should choose 10 credits of suitable modules to complement the compulsory ones in
the Spring term.
Do not hesitate to approach the organiser of a module if you have a query.
8. Module descriptions
Credits Term Written
Assign-
ments
Projects Exam Taught
by
10 Autumn Yes Yes No JL, ZC,
RL, GD,
10 Autumn Yes Workshop
assessments
10 Autumn Yes No Yes
JP, IM
Yes GM
10 Autumn Yes Yes No JC
YZ
10 Autumn Yes Yes No RH
10 Spring Yes Yes No JW
10 Spring Yes No No JC
10 Spring Yes Yes No JW
10 Spring Yes No Yes JP, BW
10 Spring Yes Yes No KC
10 Spring No No Yes GM
Dissertation 80 Approximately April to August; 50-80 pages

00580026 Transferable & Generic Skills Autumn term, 10 Credits
Module Organiser: Jon Pitchford, Room: Biology S Block, Tel: 8559, e-mail:
jwp5@york.ac.uk
Aims: This module enables the development of personal transferable competencies required
for the management role. Particular emphasis is given to the skills needed to function
effectively as a scientist working to apply mathematics to real-world problems. A series of
interactive sessions in the first week of teaching (before formal lectures begin) provides initial
training in the main subjects, and these skills are further developed in the other Masters
modules.
Learning objectives: Acquire basic research skills, skills in writing mathematics, team work
skills and presentation skills.
Syllabus:
Teambuilding fundamentals;
Group projects and presentations;
Literature resources;
Library tour, safety at work;
Typesetting software: Microsoft Word and LaTeX;
Giving talks, writing styles.
Recommended texts: Given during the course of the module.
Teaching: Teaching takes place during the orientation week. The remaining credits are
accounted for by the requirement that the students become effective users of several technical
software packages: G.I.Sci. software, Maple, Matlab, R, SPSS, and UNIX applications. Most
of this learning is undertaken in the students' own time during the Autumn and Spring terms,
with guidance from course lecturers, problems classes, web based materials and textbooks.
Assessment: There are no assessments for this module.

0580050 Statistics and Probability Theory Autumn term, 10 Credits
Module Organiser: Garib Murshudov (Chemistry), Room: B/K/065, Tel: 8253, e-mail:
garib@ysbl.york.ac.uk
Aims: Information flow and exchange is an integrated part of the modern life. Size and
amount of data sets are ever increasing. Data analysis is an important part of understanding a
variety of data types. This course aims to introduce the most popular data analytical
techniques and their application to students using one of the available packages (examples are
given using the package R).
Learning objectives: At the end of the course students should be aware of the role of
statistics and be familiar with a number of practical techniques from data analysis.
Syllabus: In the first part of the course, basic statistical terms and concepts are introduced. In
the second part, the most widely used data analysis techniques are described and illustrated.
Basic Introduction to R: this lecture introduces basic commands from the statistical
package R.
Role and place of data analysis: this gives a basic introduction to data analysis.
Maximum likelihood: maximum likelihood and related concepts such as observed and
Fisher information are described.
Elementary hypothesis testing: the idea behind hypothesis testing is explored.
Re-sampling techniques: some of the techniques for bias reduction and prediction error
estimation are described. These include jackknife, bootstrap, cross-validation.
Linear and generalised linear model: classic linear model for normal distribution case
and its generalisation for the natural exponential family is described.
Fixed, random and mixed effects models
Elements of design of experiment
Basics of ANOVA: purpose and ways of analysing ANOVA tables are described.
Log-linear and logistic models: cases when data are from Poisson and binomial
distribution are described.
Teaching:
Lectures: 20 x 2 hr lectures.
Practicals: 10 x 1 hr practicals.
Private study: 43 hrs.
Assessment: 7 hrs.
Assessment: An all day open (computing) exam in week 10 of the Autumn term (90%).
Coursework (10%).
Recommended texts:
Intelligent data Analysis, Berthold M. and Hand D.J.
Introductory Statistics with R, Dalgaard P.
Experimental Design, Cochran W.G. and Cox G.M.
Prerequisites: None.
0780170 Introduction to Signal Processing Autumn term, 10 Credits

Module Organiser: Yuriy Zakharov (Electronics), Room: P/K/001, Tel: 2399, e-mail:
yz1@ohm.york.ac.uk
Aims: This module introduces the students to the fundamental concepts of signal
processing:analog and digital signals and systems, Fourier series, sampling, statistical signal
processing and parameter estimation.
Learning objectives: At the end of this module students are expected to:
Understand signal sampling and reconstruction
Analyse continuous and discrete-time signals and systems in the time and frequency
domain
Understand cocepts of autocorrelation, convolution and linearity
Understand statistical properties of signals
Understand principles of parameter estimation in noise
Syllabus:
Analogue and digital signals
Signal sampling and reconstruction
The sampling theorem and Nyquist interval
Random processes, probability density function, correlation and spectral density
Systems, linearity and time-invariance
Impulse and frequency responses
Convolution, Fourier series, Discrete Forier Transform (DFT) and Fast Fourier Transform
(FFT)
Fundamentals of linear parameter estimation and spectrum estimation
Least squares and maximum liklihood estimates
Teaching:
Teaching:
Lectures: 10 x 2 hr lectures.
Practicals: 2 x 1 hr practicals.
Private study: 75.5 hrs.
Assessment: 2.5 hrs.
Students will receive handouts, tutorial questions and revision questions.
Recommended texts:
Lathi, B.P. "Signal Processing and Linear Systems", 2003, Oxford University Press,
ISBN 0195219171.
Kay, S.M. "Fundamentals of Statistical Signal Processing: Estimation Theory",
Prentice Hall, 1993.
Assessment: This module is assessed by a 2 1/2 hour closed book examination in January.
Prerequisites: Knowledge of simple probability theory and matrix algebra.
0680102 Introduction to Programming (PYTHON) Autumn term, 10 Credits

Module Organiser: James Cussens(Computer Science), Room: BS/111, Tel: 8396, e-mail:
james.cussens@cs.york.ac.uk
Aims: The main aim of the module is to demonstrate the fundamental principles of computer
programming, including object-oriented methods. A subsidiary aim is to teach the Python
programming language, particularly for applications in bioinformatics and databases.
This module introduces students to computer programming. Students will learn the principles
of programming, in particular object-oriented methods. The Python programming language
will be used throughout. Bioinformatics tasks will be used as examples of problems soluble by
writing computer programs. Database programming will also form an important component of
the module.
Learning objectives: At the end of this module successful students will: understand the basic
principles of computer programming; be able to write computer programs of moderate
complexity; write their own programs to solve a range of bioinformatics tasks; understand the
principles of object-oriented methods; understand how to incorporate third-party code
(particularly Biopython) into their own programs; be able to write programs which interface
with database systems.
Syllabus:
Core Computer Science
1. Introduction to computer programming
2. Variables, expressions and statements
3. Functions, conditionals and recursion
4. Fruitful functions and iteration
5. Strings and lists
6. Tuples and dictionaries
7. Files and exceptions
8. Classes and objects
9. Class methods and composition
10. Inheritance
Algorithmics
1. Writing loops
2. Dynamic programming
3. Recursion and sorting
4. Algorithms on graphs
5. Dijkstra's algorithm
Applications programming
1. Using Biopython
2. Regular expressions
3. Database programming
4. CGI programming

5. Simple GUIs with Tkinter
6. Introduction to XML processing
Practicals
1. Getting started with Python
2. Basic programming
3. Using built-in data types
4. Files, exceptions and classes
5. Object oriented programmming
6. Using Biopython
7. Database programming
8. CGI & Tkinter programming
9. XML
Teaching:
Lectures: 21 x 1 hr lectures.
Practicals: 9 x 2 hr practicals.
Private study (including assessment): 61 hrs.
Recommended texts:
How to Think Like a Computer Scientist: Learning with Python, by Allen Downey, Jeff
Elkner and Chris Meyers. Freely available from: http://www.greenteapress.com/thinkpython/
Introduction to Programming using Python, by Katja Schuerer, Catherine Letondal and Eric
Deveaud. Freely available from: http://www.pasteur.fr/formation/infobio/python
Learning Python, by Mark Lutz & David Ascher, 2nd edition, O'Reilly, 2003, £24.95
Assessment: Open - The assessment involves writing computer programs to solve specified
problems, and is done over a period of two weeks. The assessment is not divided into sections
assessing distinct aspects of the module.
Prerequisites: None.

0281105 Structural Bioinformatics Autumn term, 10 Credits
Module Organiser: Rod Hubbard, Room: B/k/176, Tel: 8267, e-mail: rod@ysbl.york.ac.uk
Aims: To provide an introduction to the fundamental principles and patterns in protein
structure, structure determination, and sequence-structure-function relationships, and also the
basic methods of structural bioinformatics - structure prediction, threading and homology
modelling. To enable students to use molecular graphics analyses to relate protein structure to
mechanism and biological function.
This module provides an introduction to the fundamental principles of protein structure,
structure determination methods, sequence-structure-function relationships and the various
bioinformatics approaches to analysis and interrogation of protein structure. The module
contains lectures and demonstrations, but the majority of the time is spent in computer-based
workshops.
Learning objectives:
A revision of basic principles of protein structure primary, secondary, super-secondary,
tertiary and quarternary structure
An ability to use a molecular graphics system to analyse protein structure and to use
classification schemes to characterise protein folds
To have an outline understanding of the key stages in the determination of protein structure
by X-ray crystallography and NMR methods
An understanding of how protein structure relates to function
The ability to compare protein sequences and structures and identify how change in
sequence leads to change in structure and function
How to construct homology models of proteins
To be familiar with threading, fold and structure prediction methods, their application and
limitations
Syllabus:
This module consists of a series of lectures, demonstrations and workshop sessions to
introduce you to the study and analysis of protein structure.
Each session will be between 3-4 hrs in length, depending on the constraints of the timetable.
The following synopsis describes the overall flow of topics; detailed division between sessions
will depend on how the course develops
Session 1
You will learn how to use the graphics systems. We will then review the elements of
protein structure covering very rapidly notions of sequence, peptide bond and the chemistry of
the individual amino acids. We will use physical models to emphasise the importance of noncovalent
interactions in biological systems (van der Waals, electrostatics, hydrogen bonds) and
how this gives rise to conformational flexibility in the peptide chain. This principle underlies
the Ramachandran diagram and is the origin of the different types of secondary structure
(helix, sheet and turns). You will build a physical model of an alpha helix and then use the
graphics systems to look at the helix in a real protein.
Session 2

We will begin with a discussion about how computers hold models of protein structure and
then use the graphics systems to perform a simple set of calculations to produce the
Ramachandran diagram.
There is then a series of worksheets introducing how to look at proteins and interpret
function. You will use the graphics systems to produce scientifically meaningful
representations of some protein structures. Three families of protein will be looked at –
sialidase, anti-lysozyme Fab and HIV-protease.
Session 3
We will (in very outline terms) discuss how protein structures are determined by the
techniques of nmr and x-ray crystallography. This will include a tour of the X-ray labs.
The workshop will demonstrate how some of the features of structures can be influenced by
the experimental methods used to discover them. This will include discussion of symmetry,
solvent, disorder, B values, crystal packing and how to interpret NMR structures. This will
also introduce you to some of the tools on the web for searching public databases of protein
information. There will be a short discussion on data formats – heritage, data content and
issues.
Session 4
Comparison of structures – given two proteins, producing a structure and/or sequence
based alignment of their structures. Short assessed project on a particular protein, relating the
information in a scientific paper to the structure on the screen. This will consolidate the
visualisation and comparison work of the first set of workshops.
Session 5
You will begin with some multiple sequence alignments – comparing the sequences and
structures of series of molecules and demonstrating the underlying relationship –similarity
of sequence means similarity of structure.
Session 6
Discussion on progress in structure determination and how this has influenced the
understanding of patterns in protein structures. Discussion on secondary structure prediction.
An introduction to homology modelling. Using the relationship between sequence and
structure to construct models of proteins on the basis of sequence similarity. Homology
modelling exercise.
Session 7
Presentations and tutorials from the Molecular Structures Database (MSD) project at EBI
Session 8
An introduction to the ideas of domains and folds looking at some protein structures.
Exploring the CATH and SCOP databases. Introduction to the ideas of threading scoring
potentials and the methods used. Threading workshop. Discussion of structural genomics.
Session 9 and 10
Assessed mini-project. Using available web resources to explore possible sequence

structure-function relationships in a particular protein.
Recommended texts:
Branden and Tooze. An introduction to protein structure, ISBN 0815323042, Garland
Science
Most biochemistry textbooks, Stryer is recommended
Teaching:
Workshops: 5 x 3 hr workshops.
5 x 4 hr workshops.
Private study (including assessment): 65 hrs.
Note that attendance at workshops is compulsory and will be recorded.
Assessment: Two assessments. The first is a mini-project to assess how well students can use
the basic principles of structure representation taught in the course to interpret protein
structures. The student produces a short (2 sides of A4) report that uses 2-3 molecular graphics
images to describe how aspects of the structure of the protein(s) provides insight into key
features of the mechanism of action and function of the protein. The second is to use a
combination of molecular graphics, sequence alignment, structural databases and, where
appropriate, homology modelling, to produce a short (4 sides of A4) report analysing sequence
structure-function relationships in a set or family of proteins.
Prerequisites: None.

03811047 Chemometrics Spring term, 10 Credits
Module Organiser: Julie Wilson (Mathematics/Chemistry), Room: B/S/108, Tel: 8282, email:
julie@ysbl.york.ac.uk
Aims: This module introduces the concepts and methods of multivariate data analysis and
visuualisation commonly applied to biological and chemical data.
Learning objectives: At the end of this module students are expected to:
be familiar with basic concepts such as similarity, metrics, state spaces and ordination;
understand the goals of dimensionality reduction and clustering
understand the importance of pre-processing
be able to perform multivariate analyses (e.g. Principal Component (and Coordinate)
Analysis and (hierarchical) Cluster Analysis) using the program R
understand the importance of visualisation for the interpretation of large datasets
Syllabus:
Data pre-processing
Principal component analysis
Factor analysis
Multidimensional scaling
Correspondence analysis
Cluster analysis
Canonical Correlations
Discriminant analysis
Wavelet analysis
evolutionary programming
Teaching:
Lectures: 10 x 2 hr lectures.
Practicals: 4 x 2hr practicals.
Private study (including assessment): 72 hrs.
Recommended texts:
Krzanowski WJ and Marriout FHC. (1994) Multivariate analysis. Vol 1 and 2. Kendallâ€s
library of statistics.
Mardia,KV, Kent, JT and Bibby, JM (2003) Multivariate analysis
Assessment: Open - Assessment is via a written assignment done over a period of two-weeks.
Prerequisites: Knowledge of simple probability theory and matrix algebra.

0690502 Introduction to Machine Learning Spring term, 10 Credits
Module Organiser: James Cussens (Computer Science), Room: BS/111, Tel: 8396, e-mail:
james.cussens@cs.york.ac.uk
Aims: The main aim of the module is for students to develop the skills and understanding
necessary for the application of selected machine learning (ML) and statistical techniques in
bioinformatics. The selected techniques are: profile HMMs, nearest neighbour, decision trees
and artificial neural networks. Another aim is for students to gain a better understanding of
probabilistic and statistical reasoning so that they can apply maximum likelihood and
Bayesian approaches to the estimation of probabilities. Students should become familiar with
the HMMER profile hidden Markov model software; and to become familiar with the C4.5
system.
This module gives an introduction to the field of machine learning for solving biological
problems. Specific topics include: profile hidden Markov models, decision tree learning,
nearest neighbour classification and artificial neural networks. Profile hidden Markov models
are 'compiled' versions of multiple sequence alignments and can be used for searching
databases of sequences. The other three topics are central approaches to machine learning not
just in bioinformatics but also in other areas.
The course will as often as possible use software and libraries that are freely available for
download on the Internet. Thus enabling a student to work on or off campus.
Learning objectives: By the end of the module you should be able to:
Select appropriate machine learning and statistical approaches for specific applications.
Understand core HMM algorithms: computation of forward and backward probabilities, the
Viterbi algorithm
Be able to use nearest neighbour classification
Be able to use decision trees for classification
Be able to use artificial neural networks for pattern recognition
To be able to apply the HMMER systems
To be able to apply the C4.5 system
To use maximum likelihood estimation of probabilities
To use Bayesian approaches to probability estimation
To use constructive induction to extract features from sequences
Syllabus:
Lectures:
Multiple alignments, families of sequences and profile hidden Markov models
Profile HMMs for ungapped regions of a multiple alignment
Profile HMMs for multiple alignments with gaps
Estimation of probabilities
Forward and backward probabilities in HMMs
The Viterbi and Baum-Welch algorithms for HMMs
Significance of hits. Local and global alignment with HMMER
Introduction to Inductive Learning

Decision Tree Learning
Decision Tree Learning (cont.)
Constructive Induction
Nearest Neighbour Methods
Computing Similarity
Similarity (cont.)
Introduction to pattern recognition
Neural network architectures for pattern recognition
Application: Identification of promoter sequences
Application: Protein classifcation rule extraction
Application: Classification of microarray gene data
Practicals:
HMMer tutorial
Estimation probabilities
Forward and backward probabilities
The Viterbi algorithm
Using C4.5
Constructive induction
Recommended texts:
Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids Richard
Durbin, et al, Cambridge University Press (Paperback - 1998 £23.99)
Teaching:
Lectures: 20 x 1 hr lectures.
Practicals: 9 x 2 hr practicals.
Private study (including assessment): 62 hrs.
Assessment: Open - Assessment is via a written assignment done over a period of two weeks.
There is a section on profile hidden Markov models (40%), a section on machine learning
(40%) and a section on artificial neural networks (20%).
Prerequisites: None.

0381045 Current Research Spring term, 10 Credits
Module Organiser: Julie Wilson (Mathematics/Chemistry), Room: B/S/108, Tel: 8282, email:
julie@ysbl.york.ac.uk
Aims:
This module aims to provide a comprehensive knowledge base to understand and appraise
contemporary chemoinformatics research, and to understand its applications.
Learning objectives: At the end of this module students are expected to:
appreciate major theoretical and conceptual issues in current chemoinformatics research
understand how research methods relate to underlying theoretical and conceptual
assumptions and how these can influence research outcomes
make informed choices of appropriate research methods for specific research questions.
Teaching:
Seminars/workshops: 9 x 3 hr.
Private study (including assessment): 73 hrs.
Recommended texts:
Reading lists will be provided during the module. Some lecturers require you to have read
some relevant material before the session.
Assessment: Assessment is by way of a 1000-word assignment submitted by the end of Week
1 of the summer term. Write a critical review of an issue of your choice associated with any of
the presentations given in the current academic year. The review should include relevant
references.
Prerequisites: None.

0381046 Chemical Informatics Spring term, 10 Credits
Module Organiser: Garib Murshudov (Chemistry), Room: B/K/065, Tel: 8253, e-mail:
garib@ysbl.york.ac.uk
Aims: Small molecular compounds are essential in understanding function of
macromolecules. The search for compounds similar structural and chemical properties in large
databases is a first step in drug design. The course aims to introduce the design, organisation,
retrieval, visualisation and some use of chemical information.
Learning objectives: At the end of the course students should be aware of the organisation of
and search in small molecular databases. They will also learn some of the techniques to
generate three-dimensional structures from one or two-dimensional representations.
Syllabus:
1. Chemical structure representation.
2. Resources.
3. QSAR - quantitative structure activity relationship.
4. Graph and sub-graph isomorphism algorithms.
5. Smiles - Simplified Molecular Input Line Entry System.
6. Molecular Dimension Limited (MDL) file format for chemical connectivity.
7. Chemical Structure similarity.
8. Fingerprints and search for substructure similarity.
9. Generation of 3D structures from 2D representations.
10.3D structure similarity.
11. Elements of molecular descriptors.
Teaching:
Lectures: 12 x 2 hr lectures.
Practicals: 6 x 1 hr practicals.
Private study (including assessment): 70 hrs.
Assessment: The module will be assessed by a short project carried out over a two-week
period.
Recommended texts:
An introduction to chemoinformatics. Andrew R. Leach and Valerie J. Gillet.
Chemoinformatics, Edited by Casteiger J. and Engel T.
Prerequisites: None.

0580500 Stochastic Processes Spring term, 10 Credits
Module Organisers:
Jon Pitchford, Room: Biology S Block, Tel: 8559, e-mail: jwp5@york.ac.uk
Bo Wang, Room: G/043, Tel: 3096, e-mail: bw527@york.ac.uk
Aims: The module is designed to raise awareness of the differences between deterministic and
stochastic approaches to mathematical modelling, and to provide the students with the
necessary techniques to investigate processes in which random effects are important. Students
will see how stochastic modelling methods are being applied to practical problems,
particularly in biological and ecological systems. Where possible the core syllabus is
augmented by research seminars given by biologists and mathematicians. Students will also
work in groups to present seminars on recent research papers.
Learning objectives: At the end of the module you should:
have a sound knowledge of the theory of Markov chains and their dynamics
understand the behaviour and the analysis of some simple stochastic paradigms,
including random walks, branching processes, Poisson processes, and birth-death
processes.
have a broad overview of the ways in which mathematics have been used to enhance
understanding of ecological dynamics and other processes susceptible to stochastic
effect.
be able to tackle problems in ecological modelling and more broadly in mathematical
modelling in general.
Syllabus:
First part of course (BW): These lectures present a generalised and comprehensive
description of Markov chains, demonstrate widely applicable methods for their solution, and
discuss their applications in some detail. Topics covered include definitions and classifications
for Markov chains (with examples), stationary distributions and recurrence, continuous-time
Markov chains, and Markov chain Monte Carlo methods.
Second part of course (JWP): Revision of sets, events, and probability (part 1). SRW
example (gambler's ruin); Revision of sets, events, and probability (part 2); Network
reliability example; SRWs and difference equation methods for their solution; Generating
functions
Applications of generating functions to SRWs; Simple branching processes; Age
dependent branching processes, and Poisson processes; Simple birth processes; Simple
death processes, and general birth-death processes.
In addition, there will be at least three student-led seminars based on recent mathematical
modelling research papers (typically involving random walks, branching processes, and
stochastic differential equations).

Recommended texts:
G Grimmett and D Stirzaker, Probability and Random Processes, Oxford.
E Renshaw, Modelling Biological Populations in Space and Time, Cambridge.
Teaching:
Lectures: 9 x 2 hr.
Seminars: 9 x 1 hr.
Private study (including assessed coursework) : 70 hrs.
Assessment: 3hr examination

0281203 Biocomputing and Web Applications Autumn term, 10 Credits
Module Organiser: Kevin Cowtan (Chemistry) Room: BK/064, Tel: 8254, e-mail:
cowtan@york.ac.uk
Aims: To introduce the basic elements of the programming language Python, including:
values, variables and expressions; data types; strings; arrays and vectors; conditions and loops;
methods; recursion; classes. To provide tools and experience in the use of existing
computational biology resources and the construction of new resources using local and
distributed computing techniques.
This module will build on the introduction to the programming language Python and introduce
local and distributed data organisation and manipulation as applied to computational biology
problems. As useful as word processors, databases and other tools might be, the one thing that
makes computers so important is our ability to program them to perform a huge variety of
tasks, from solving equations to processing and analysing data. In addition, the large databases
produced today by genomics efforts present significant problems of data description and
organisation, and are often distributed across local and external computing resources. This
module is an introduction to programming as a means of solving practical problems, using the
python programming language, and will explore techniques for data organisation and methods
for accessing local and remote databases through JDBC and web services portals.
Learning objectives: At the end of this module students will: be able to create a Python
program to solve a problem of moderate complexity; have the foundation for studying
advanced concepts of Python programming; understand the purpose and benefits of data
modelling; be able to design a database and describe it using a schema; access from Java using
JDBC; understand the common markup language XML and its schema; be able to model data
and processes using UML; have an overview of GRID computing protocols; the SOAP
protocol and WSDL; experience in using and web services; awareness of available web
services for computational biology.
Teaching:
Lectures: 13 x 1 hr; 4 x 2 hr.
Practicals: 4 x 3 hr; 9 x 2 hr.
Private study (including assessed project) : 49 hrs.
Lectures:
1. Scientific algorithms in python.
- basic algorithms
- searching data
- indexing data
- numerical analysis
- network data
- data clustering
- routefinding
- advanced DB connectivity

2. DB design
- analysing the problem
- designing a database
- optimising the design
- interacting with object oriented programs
3. Basic web applications with mod_python
- basic pages
- introducing dynamic elements
- forms and interaction
- persistence
- DB applications
4. Advanced web applications with google apps
- basics
- building complex applications
5. Grid computing and web services, with examples
- creating web services
- calling web services
- EBI examples
Recommended texts:
Q. Charatan and A. Kans. Java: The First Semester. McGraw-Hill, 2001, ISBN: 0077097572
Core Servlets and Java Server Pages (Marty Hall): available as free download from:
http://pdf.coreservlets.com/, or from Prentice Hall, 2000, ISBN: 0130893404
Assessment: Open - The assessment involves writing computer programs to solve specified
problems, and is done over a period of two weeks. The assessment is not divided into sections
assessing distinct aspects of the module. One short report on protein identification workshop
(50% weighting) and data analysis problem sheet on tandem MS fragmentation of peptides
(50% weighting). The student should submit their protein identification work on the form
provided, and two hard copies and one soft copy (on floppy disk or CD) of their data analysis
answers, to the Biology Graduate Office. The student should use only their candidate
identification number on all work submitted for assessment.
Prerequisites: Introduction to programming (Python).

9. Handing in written assignments, practical assignments and the dissertation
Written assignments and practical assignments
All assignments should be handed in to the module organiser at the time and place specified.
The dissertation
Three printed copies must reach the Mathematics Departmental Office (G127) by 16.30 on
Friday 19th August 2011. You should also hand in a CD-R containing your dissertation in
both Adobe PDF format and Postscript format to the Mathematics Departmental Office. Please
use a CD-R pen to write clearly your name on your disk.
The dissertation must be handed in, in person, or by a person nominated by you; and must be
signed in to confirm the date and time. Postal or electronic submissions will not be accepted.
Late submissions will incur a penalty unless there are extenuating circumstances.
Dissertations will remain in the Department of Mathematics until the final assessments have
been approved. See also the Dissertation Guidelines as item 25 of this document.
10. Lateness penalties
Each day or part day that an assessment is late will incur a 10% penalty. This includes
weekends and bank holidays when the office is not open. For example, if work is awarded a
mark of 30 out of 50, and the work is up to one day late, the final mark is 25. After five days,
the work is marked at zero. Note however, the penalty cannot take the mark into a negative
result. Please do not ask for deadlines to be extended: the answer will be no, with the
exception of illness supported by a medical note or other urgent good cause supported by a
written submission from your supervisor. It is your responsibility to plan your dissertation
around these deadlines.
11. Size restrictions
Your dissertation will be judged on content. The guide in the mentioned before of
50 - 80 pages for an MSc dissertation
is only a rough guide concerning length. The over-riding requirement is for excellent content.
The assessment will be based on understanding, clarity, insight, innovation and presentation.
12. Disclosure of marks/feedback meetings
Marks for individual assignments will be given out as soon as possible. You are encouraged
to discuss your performance with the module organiser and/or your supervisor. Verbal
feedback sessions will also be organised as required.
13. Marking criteria

The criteria used for marking the dissertation will be made available in a separate document in
due course. These will help you understand what is required for each type of assessment. The
dissertation mark to be used in the computation of your final average mark in the course is a
weighted average of an agreed mark between first and second markers of the content of your
dissertation, a mark given by your dissertation supervisor which accounts for effort and
dedication, and finally a mark for your presentation.
14. Mark record sheets and course transcripts
The Department of Mathematics keeps a running record of your marks as the course proceeds.
This will be available for inspection in the Departmental Office (G/127) and will enable you to
keep a check on your marks. At the end of the course a transcript is prepared for each student
THAT gives a breakdown of the assessment.
15. Appeals procedure
This is only an outline of the procedure and is condensed from University Ordinances and
Regulations.
A. Students wishing to appeal against their result
The university regulations are quite clear that there is no appeal available against the
judgement of the examiners (Regulation 2.8c). There is only appeal against the decision to
allow resits or if there is prima facie evidence that one or more of the following has occurred:
seriously inadequate supervision; examinations conducted unfairly or improperly or examiners
showed prejudice against the student.
If an appeal is desired then it must be made in writing to the Registrar within four weeks of
being notified of the decision.
B. Students wishing to appeal against the mark of a specific assessment
Again the university regulations state that there is no appeal against the judgement of the
examiners (Regulation 2.8c). However, if a student wishes to draw the attention of the external
examiner to a particular mark, which they believe to be in error, then they should write to the
examinations officer stating their case.
All special cases, including late submissions, will be marked with no allowance made but will
be brought to the attention of the external examiner. Any particular circumstance that might
affect performance in an examination should be made clear, in writing, to the examinations
officer. This should be done as near to the time of the assessment as possible (Regulation
5.4h). In the case of illness a doctor's note is required (Regulation 5.4g). These circumstances
will be brought to the attention of the external examiner and, if necessary, raised at the Board
of Studies before it considers examination results.
There should be no correspondence with the external examiner. All material will be passed on
by the examinations officer before the viva voce examinations and final examiner's meeting.
All appeals will be treated in strictest confidence.
16. Academic misconduct: University guidelines

You are responsible for ensuring that your work does not contravene the University‟s rules on
academic misconduct, which are set out in regulation 5. The University takes a very serious
view of such misconduct and penalties will be applied to students who are found to have
attempted to mislead examiners. Forms of academic misconduct include:
cheating deliberate failure to comply with the rules governing examinations, e.g. by
making arrangements to have unauthorised access to information;
collusion assisting another individual to gain an advantage by unfair means, or receiving
such assistance yourself;
fabrication misleading the examiners by presenting work for assessment in a way which
intentionally or recklessly suggests that you have collected factual information
which has not in fact been collected, or falsifies factual information;
personation producing work to be submitted as that not of yourself but of another, or
assuming the identity of another individual in order to deceive the examiners,
or soliciting another individual to act or appear as yourself, or to produce work
on your behalf;
plagiarism incorporating within your work without appropriate acknowledgement material
derived from the work (published or unpublished) of another.
The penalties for academic misconduct will depend on the seriousness of the offence.
Students found guilty of academic misconduct may, for example, have their degree class
reduced, fail their degree or be asked to leave the University.
If you have any queries about what constitutes academic misconduct, and in particular about
the proper attribution of material derived from another‟s work, you should seek advice from
your supervisors or tutors.
The important University regulations on plagiarism and collusion are reproduced below.
(i) Candidates must not by implication or otherwise represent the work of others as their
own. All sources, whether published books and articles or unpublished material of any
kind must be explicitly acknowledged, and quotations and close paraphrases clearly
attributed.
(ii) Candidates must not by implication or otherwise represent work done in collaboration
with others as their own unaided work, nor may any member of the University,
whether or not he or she is a candidate in the examination, knowingly allow his or her
work to be used without acknowledgement by examination candidates.
(iii) The examiners will take full account of any breach of the requirements in (i) and (ii)
above in determining a mark for the work affected. In serious cases this may result in
a mark of zero for the paper or papers concerned with consequent effects on the
assessment of the candidates overall performance, even failure in the examination as a

whole.
If, in the opinion of the examiners, the case is of particular gravity, they may also
recommend disciplinary action.
The penalties available in such cases are:
(a) Suspension or exclusion from the University
(b) A lowering of the class of degree to be awarded
(c) Withholding the award of a degree
(d) In the event of failure or action under (iii) (b) above, withdrawal of
any entitlement to repeat the examination concerned.
17. The York Award
As part of your career planning (see 19 below) you might wish to consider enrolling for the
York Award. The York Award is a certificate awarded to the University to people who
complete a significant programme of personal development during their time as a student at
York.
The programme offers courses aimed at developing skills such as communication (including
presentations and negotiating), numeracy, improving own learning, working with others,
information literacy, time management, etc.
More information is available at
http://www.york.ac.uk/services/careers/skills.cfm
or in a brochure available from the Student Skills Development Unit (in the Careers Service
building).
18. Careers
Placements are not guaranteed but (provided they do happen for you) might give a good
access to a job after you have completed the course. Nonetheless you should maximise your
chances of pursuing a career of interest to you by following the path below.
Find your way to the Careers Service and discover how it can help you choose and get
the right job or further course.
Complete the “Personal Development Record” for your supervisor and begin to add up
what you have to offer the world beyond education - use the self assessment exercises
available at the Careers Service and pick up a copy of “Planning Your Future”
Explore the extensive range of information on jobs and courses, at home and overseas,
held at the Careers Service.
Use every opportunity to confirm you know all you need to know about your chosen
option; lots of Careers Service events, especially in the Autumn term, will give you
direct access to potential employers. Collect a copy of the events programme from the

Careers Service or from the Graduate Students Association.
Always collect, or read on the Web, the Careers Service Vacancy Bulletin containing
details of vacancies advertised for graduates, including those employers who conduct
their initial interviews on campus; don‟t miss options with early application times such
as teacher training, Civil Service recruitment competitions, financial careers and
overseas research scholarships - the recruitment season begins in September for the
following Autumn.
Attend training sessions on application form, CV and interview technique.
Consult your referees about your plans and give them a copy of your CV or Personal
Development Record completed for your supervisor.
If you have questions or concerns about what to do next arrange to see a Careers
Adviser, either “drop in” to see the duty adviser or make an appointment for a personal
interview.
The Careers Service is located between the Language Centre and the large car park on the
Central Hall access road off the University Road. You are welcome to call, on 2685 (internal),
or visit when we are open. Most of our facilities are available on a “self-service” basis with
help from our Receptionist, Information Officer or duty Careers Adviser.
For information on Opening Hours and Duty Careers Adviser availability, please visit the
webpage http://www.york.ac.uk/services/careers
19. Facilities
Computer room
G/120 has been set up with computers for you to use on the MSc course. You will be sharing
this facility with students on the MRes course. TESA or Onity security cards will be given to
you to access this room and the department.
Computing and printing
When you register you will be automatically registered with the Computing Service and have
an account on the central system. Access to this is available via the dedicated terminals in the
MSc Computer Room, via terminals in each college building, or via the networked PC in the
departmental office. There will be a dedicated "MSc printer" in the MSc Computer Room.
Printing will be free up to a point and then you will be charged per sheet. The detailed
regulations will be specified later.
Library
When you register you will be automatically registered with the library and be able to borrow
books. As a graduate student you may find that journals will become as important to you as
books; these can only be borrowed at weekends. The articles you need can be photocopied (at
your own expense) in the library. You may well find that you need to read papers in journals
that our library does not take. These (as well as books that are not in the library) can be

obtained by requesting an Inter-Library Loan; in the case of a journal article, this will result in
a photocopy being sent to you.
The library has a clearly signed Information Desk, where you can ask where to find the
Mathematical Books collection as well as the Mathematical Journals.
Telephones
Internal telephones can be used freely. Note that four-figure telephone numbers are internal
university numbers. They can be converted into York telephone numbers by prefixing 43.
The York code is 01904.
Other courses
Extra lecture courses are sometimes arranged by individual research groups and you will be
informed of any that are relevant.
20. The Department of Mathematics
The decision-making body in the department is the Board of Studies, which consists of all
teaching staff in the department, three elected undergraduate students and an elected graduate
student. Students are not present for the discussion of “starred items" of business, which are
items concerning individual students or members of staff. Graduate studies are considered by
the Mathematics Graduate School Committee, which reports to the Board of Studies. The
committee is chaired by the Graduate Tutor, who is responsible for the welfare of graduate
students.
Head of Department: Professor Stephen Donkin
Graduate Tutor: Professor Paul Busch
Chair of the Board of Studies: Dr Jason Levesley
Members of the Graduate School Committee:
Professor Stephen Donkin, Professor Paul Busch (Chair), Professor Zdzislaw Brzezniak, Dr
Chris Fewster, Dr Chris Wood, Dr Ian McIntosh.
Departmental Administrator: Mrs Cathy Moore
Secretaries: Mrs Sue Adams
Mrs Chris Higgins (Monday am, Tuesday, Wednesday,
Thursday am, Friday am)
Mrs Christine Cockett (Monday to Friday am)
Ms Stella Fisher
Mr Nicholas Page
21. Academic staff involved with the MSc and their research interests
e-mail Room Tel.
Julie Wilson julie@ysbl.york.ac.uk B/S/108 8282

(Chemometrics, biostatistics and image processing)
James Cussens james.cussens@cs.york.ac.uk B/S/111 8396
(Machine learning, inductive logic programming)
Garib Murshudov garib@ysbl.york.ac.uk K/066 8252
(Statistical study of macromolecular crystal structures)
Asher Rashid asher@cs.york.ac.uk CS/203F 2767
(Machine Learning)
Jon Pitchford jwp5@york.ac.uk B/S/109 8282
(Mathematical biology and ecology, stochastic processes)
Bo Wang bw527@york.ac.uk G/043 3096
(Computational statistics, stochastic analysis)
Rod Hubbard rod@ysbl.york.ac.uk B/K/176 8267
(Structure based drug discovery)
Yuriy Zakharov yz1@ohm.york.ac.uk P/K/001 2399
(Signal Processing for Communications)
Kevin Cowtan cowtan@ysbl.york.ac.uk B/K/264 8253
(Automated protein structure solution)
22. Supervisors
You will have been informed of your supervisor by October 2009.
23. Welfare Support Services
The University‟s Welfare Network is designed to provide students with quick and easy access
to a variety of sources of help and advice on all aspects of life as a student. Personal
supervisors in academic departments are responsible for overseeing both academic progress
and general welfare. In addition each college has a welfare team which includes the Provost
and a College Dean who has special responsibility for student welfare. Every full time student
is a member of a college, and students may approach their college welfare team for help and
advice whether or not they are resident in the college at the time. Central support services
available to all students include the Student Counselling Service, the Adviser on Disabilities,
the Welfare Information Officer, the Equal Opportunities Adviser, the International Office, the
Student Support Funds Co-ordinator and the First Contact Network (which offers support in
cases of harassment). In addition administrative offices such as the Undergraduate Office, the
Graduate Schools Office and the Accommodation Office all provide information and advice.
Welfare support is also available through the student-run organisations, particularly the
Students‟ Union and the Graduate Students Association. Information about the welfare
network and its co-ordination is widely disseminated, so that students seeking assistance in
any quarter can, if necessary, be referred quickly to those with the specialist knowledge and
skills to help them. The Student Support Services Handbook, issued to all incoming students,
describes the main contributors to the Welfare Network, and includes information about the
Campus Nursery, the Health Centre and the Chaplaincy, which offers a contact for all faiths.
Comprehensive information on welfare within the University can also be found at
http://www.york.ac.uk/admin/gso/gsp/living/welfare.htm

24. Contact Details
When you are about to finish the MSc course, you are required to complete a form giving
details of how to be contacted after completion of your course, for the purposes of graduation
invitation and to receive the formal transcripts of the course.
We will require you to write a report on your experience as an MSc student, which will be
added in our website.
For security reasons, please do not forget to return to the Mathematics Departmental Office
your TESA or Onity security access card before you leave.
25. Presentations
After you hand in your dissertation, you will be informed of the day in which you should give
a presentation highlighting the main points of the content of your dissertation and your
conclusions. This will take place in a lecture room, where there will be a laptop attached to a
data projector for you to use. You will have 10 minutes to give your presentation and there
will be 5 minutes available at the end for possible questions from the audience. Typically the
audience will consist of the external examiner, the MSc teaching staff, invited members of
staff and some of your MSc colleagues.
In case you wish to use your own computer, that is fine, but come in advance to test whether it
works with the data projector on the day. If you are not using your own computer, then it is
expected that you prepare your presentation in PowerPoint or Adobe PDF and bring a CD-R
containing the file of your presentation.
Attention: Please do not bring a floppy disk (this is an unreliable media and considered
obsolete), also the laptop provided on the day is not going to be a networked computer, so you
will not be able to download your presentation from your university network account, so make
sure to burn a CD-R with your presentation in advance.
26. Dissertation Guidelines
The intention of this section is to give you some advice on how you should write your MSc
dissertation. However, it is not a strict rule that you should take literally. The style in which a
dissertation is written may reflect the subject it is about, and since the MSc course has many
different directions one may pursue, the dissertation may differ in length and style. Also, you
may have to write your dissertation when you are in placement, where placement means to do
a project outside the Mathematics Department of the University of York, which can be a
project supervised by someone from another department in the University of York, or from
someone working in one of our industrial partners.
In the final section of this document, we give you information that is passed to the first and
second markers of your dissertation as well as to the external examiner of our MSc. These are
our dissertation marking guidelines and it gives you a good idea of what the markers will be
looking for in your dissertation to evaluate its quality and attach a corresponding mark.

We finish this initial section with highlights of some information already present in the MSc
handbook and with some additional information. Your dissertation should normally be in A4
paper format of length:
About 50 to 80 pages long.
Important Note: It is compulsory that your dissertation should have a cover page containing
the title of the dissertation with your name underneath. Also there should be a statement
saying: Dissertation submitted for the MSc in Data Analysis, Networks and Nonlinear
Dynamics, Department of Mathematics, University of York, UK. (With month and year of
submission underneath.)
This year we are asking you to submit three copies of your MSc dissertation with deadline
4:30pm on Friday 20th August 2010 to be handed-in in the General Office G/127 in
Mathematics. The copies of your dissertation should be spiral soft-bound with transparent
front cover and blue back. You can use the University Printing Unit of the University of York,
located in the Market Square of Heslington Campus. They would be able to do the binding
while you wait, however, note that there may be others that will require the same service at the
same time. So you should organise yourself in advance to avoid congestion. Each copy will
cost you around £3 to be bound.
You are also required to submit a CD-R containing your dissertation in both Postscript and
PDF formats, and you have until 4:30pm Friday 20th August 2010 to hand-in your disk to the
General Office in Mathematics. As mentioned earlier, do not forget to write your name on the
disk using a CD-R pen.
Writing the dissertation
As mentioned above, the dissertation may have very different styles depending on the subject
it covers. What will follow are simply general guidelines.
Usually you have gathered research about a topic or various interconnecting topics during the
work towards the dissertation. In many cases, the dissertation has a practical element, i.e. you
may need to write some programs to carry out some calculations, tests, etc. It is expected that
you would spend an initial part of your dissertation covering some of the background material
of the subject your research topic is all about. Then you would describe the topic or topics of
research in more detail, and then if there are some work that you have carried out on your own
or not, you should complete with some conclusions, maybe suggesting further work or further
reading.
It is expected that you will have a contents page for your dissertation, where the headings of
chapters and/or sections are clearly laid out. This gives a quick way for the reader to have an
impression of the content organisation of your dissertation.
It is important to note the following general aspects for the dissertation. If you make a
comment about a result or calculation, then you should give it a reference (in case it is referred
somewhere in the literature), otherwise any comments you make will be attributed to yourself.

If you state something that is your own work or your own opinion, then make it clear for the
reader. When covering background material, make clear the references you have used to write
that part of your dissertation.
If your dissertation contains programming material, then it is good if you attach the source
code as an Appendix to your dissertation. If you have written the code yourself, state it clearly,
and if you modified someone else‟s code then also state it clearly. If you use code that has
been downloaded from the internet, you should explicitly mention the name of the authors,
and the webpage where the code was obtained. If it is a long program, then you can mention it
in the text and in the references, without explicitly including the code in an Appendix.
Finally, as mentioned in the first week of your course in the Transferable and Generic Skills
module, there is no obligation to write your dissertation in LaTeX and you can use Microsoft
Word or other typesetting package. Remember that the presentation of your dissertation is one
aspect which receives marks and counts for the overall quality assessment of your dissertation.
Your dissertation supervisor will be able to help you with various aspects of the writing-up,
which has been mentioned above. However, in order to make the most of their help, you need
to submit regular updates of your progress, and when close to submission, you should give
enough time for your supervisor to comment on an almost complete draft of your dissertation.
Do not leave to write-up your dissertation in the last two weeks or less before submission.
There are good and bad examples of submitted dissertations, which you can see in the Library
from previous MSc students. Ask your supervisor for advice on how to write your dissertation.
Dissertation Marking Guidelines
These are the Marking Guidelines for the dissertation supervisor and second marker (as well
as the external examiner, when considering borderline cases).
INSTRUCTIONS FOR MARKERS:
Please mark the dissertation out of 100.
You are encouraged to annotate comments and corrections on a separate piece of paper. You
will be asked to write a short report about the quality of the dissertation and the reasoning for
your chosen mark. The following is a general guideline:
1-29: No useful results obtained. Inadequate or incorrect descriptions of the methods or
theory involved. Little or no understanding of what the work was about. Mathematical errors.
30-39: Very limited results of poor quality. Poor description of the methods or theory
involved. Very limited understanding of the work. Very limited mathematical skill displayed.
40-49: Some worthwhile results of modest quality, but not presented at all well. Reasonable
description of the methods or theory involved. Signs of misunderstanding of the work

involved. Limited ability in developing sound applications of the methods and theory. Limited
mathematical understanding.
50-59: Reasonable number of results of satisfactory quality, presented in a comprehensible
way. Evidence of reasonable understanding of the basis of the methods or theory, and how to
apply them. Evidence of an ability to utilise accurate relevant mathematical arguments within
applications.
60-69: A good number of results of satisfactory quality presented in a lucid and thoughtful
way. The methods and theory well described and understood with clear evidence and of ability
to apply them in a sensible and appropriate way. Evidence of an ability to utilise sound
mathematical arguments with applications.
70-100: A high quality dissertation in which the results (of good quality and quantity) are
presented with clarity and precision, the methods and theory are well described and
understood, and there is very clear evidence of the student‟s ability to apply these in an
appropriate manner, thoughtfully and incisively. Substantial evidence of an ability to utilise
sound mathematical arguments in applications.
Further guidance on marking in the range 70-100 is given below:
Awarding excellence marks in the range 70-100
The basic criteria for marks greater than 70 are that the candidate shows signs of the
following:
E1 Some originality and creativity – clever ideas, novel combination of ideas;
E2 Knowledge of facts or opinions or methodologies not directly taught, but gleaned by the
student from reading and/or his/her own work;
E3 Critical ability – an ability to make valid criticisms of evidence, views or arguments;
E4 An ability to synthesise a fine argument from many sources – evidence of an ability to
combine many ideas and/or opinions to build a convincing argument. Evidence of the
ability to apply mathematical reasoning accurately and sensitively within the relevant
context.
As a guide it is suggested that these „excellence indicators‟ can be used as follows to assign
marks:
70-79: The candidate shows one (marks nearer to 70) or two (marks nearer to 80) of the
excellence indicators E1 to E4.
80-89: The candidate shows two (marks nearer to 80) or three (marks nearer to 90) of the
indicators E1 to E4.
90-100: The candidate shows all of the indicators E1 to E4.

The above can clearly only represent guidelines in forming your judgement. There may be for
example other factors influencing the quality and quantity of results that need to be taken into
account in assessing the dissertation. The dissertation supervisors should make such
circumstances clear in their reports for second markers.
The dissertation supervisor gives a mark out of 20 towards the effort and dedication of the
student in the work during the dissertation, and should write a short paragraph about the
reasoning behind the mark. This information should be available to the second marker, prior to
the independent marking of the dissertation.
Finally, a good presentation in the dissertation is also important as it is one of the skills we
seek to develop in the MSc students. At the end of the course, the students should give a 10
minutes oral presentation based on their dissertations. The oral presentation will be marked
out of 5 marks. Note that although the presentation mark counts little towards the degree, for
students that are borderline cases this could be an important element. Also, since the external
examiner may not be able to read all the dissertations, it is important that you give a good
impression of the work done in your dissertation since the external examiner will attend all the
presentations.
The dissertation mark is a weighted average of the agreed mark between first and second
markers, the effort and dedication mark given by the dissertation supervisor and the
presentation mark.