The project is an essential component of the Masters courses. It is a substantial piece of full-time independent research in some area of data science. You will carry out your project under the individual supervision of a member of CDT staff.
The project will occupy a large part of your time during the Spring semester, and 100% of your time from late May/early June — once your examinations have completed — until mid-August. A dissertation describing the work must be submitted by a deadline in mid-August.
You are expected to choose a project at the end of Semester 1. Students are expected to find their own projects in consultation with supervisors, rather than choosing from a predefined database. Staff have indicated areas of interests but these form the basis for discussion and are not a matter of project selection. The procedure for this is:
The overall schedule is: You will meet with supervisors and select a project just after classes end in Semester 1. Once you have selected a project, we recommend that you get a head start on your project over the winter break. During Semester 2, you will work approximately 50% on coursework and 50% on your project. After classes end in Semester 2, you will have a revision period for your exams — during this period we recommend that you focus on your exams. Once the exams complete, you should return to your project work, spending 100% time on it until the final deadline in mid-August.
Here are the important dates and deadlines for 2017-18:
As part of choosing a project, you will also choose a supervisor. Your supervisor gives technical advice and also assists you in planning the research. Students should expect approximately weekly meetings with their supervisor. Backup supervisors may be allocated to cover periods of absence of the supervisor, if necessary.
At the beginning of April, you will submit an interim report about how your project has gone so far. This should be about 6 pages. This report will not form part of the mark; it is solely for feedback. The report should describe the research problem that you are considering, explain why it is important, what methods you expect to use, how you expect to evaluate your methods, what results you have been able to obtain so far, and what your plans are for the summer. You should write this in such a way that you can re-use the text in your final MSc project report.
The MSc project is designed to be a first research project that prepares you for the more extended work that you will do in your PhD. The project is intended to be novel research — we hope that in some cases the MSc projects will lead to publishable results, although this is not required and will not always be possible, depending on the nature of the project. Your supervisor should help you identify a topic that has the potential to lead into a larger PhD project, should you decide to continue research in the area.
That said, it is not required that your PhD research be in the same area as your MSc research. Some students will indeed continue their PhD work with the same research area and supervisor as their MSc. Others will choose a different PhD supervisor. Both of these outcomes are expected and are perfectly fine.
Of course if you do already have a good idea about your intended PhD topic, you will want to take this into account when selecting your MSc topic — whether it be to choose a topic in the same area, or to choose a topic that will provide you with complementary experience.
Some students may wish to undertake a project which relates to the activities of one of our external parterns. Alternatively, some projects that supervisors suggest to you may have a natural relationship with one of the CDT partners. This is encouraged. A student undertaking such a project will still need to find an academic supervisor who is willing to take on the project. During the project phase, students working on such projects have both an academic supervisor and a designated contact at the partner organization. The Data Science Research Day in September is a good time to build links with partners.
We encourage you to discuss your projects with other CDT first year students, talk informally about your progress, and get advice from your peers about any issues. Last year this happened as part of the CDT Tea meetings; this year, we will discuss whether to continue this or to have more formal tutorials.
The project is only assessed on the basis of a final written dissertation. Additional material, such as the code you submit, may be taken into account in case of doubt, but you should make sure that all the work you have done is carefully described in the dissertation document. Dissertations will typically conform to the following format:
In addition, the dissertation must be accompanied by a statement declaring that the student has read and understood the University's plagiarism guidelines.
In the acknowledgments section of your dissertation, in addition to thanking anyone that you wish, you should also acknowledge the funding sources that have supported you during the year. Please follow these instructions for acknowledging your funding sources. You should get to know them well as you will also need to follow them for every paper that you publish during your PhD.
Students should budget at least four weeks for the final dissertation writing-up phase. The length of the main body of the dissertation should be around 40 to 50 pages. Where appropriate the dissertation may additionally contain appendices in which relevant program listings, experimental data, circuit diagrams, formal proofs, etc. may be included. However, students should keep in mind that they are marked on the quality of the dissertation, not its length.
The dissertation must be word-processed using either LaTeX or a system with similar capabilities. The LaTeX thesis template can be found via the local packages web page. You don't have to use these packages, but your thesis must match the style (i.e., font size, text width etc) shown in the sample output for an Informatics thesis.
Many projects will require computing resources. Please see the CDT handbook for information about what computing resources are available to CDT students.
If a project requires anything more, this needs to be requested at the time of writing the proposal, and the supervisor needs to explicitly ask for additional resources if necessary (start by talking to the CDT projects organizer, below).
Technical problems during project work are only considered for resources we provide; no technical support, compensation for lost data, extensions for time lost due to technical problems with external hard- and software as provided will be given, except where this is explicitly stated as part of a project specification and adequately resourced at the start of the project.
Students must submit their project by the deadline in mid August (see above). Students need to submit hard copy, electronic copy and archive software as detailed below.
Projects are assessed in terms of a number of basic and other criteria. Only the dissertation is used for assessment. Knowledge of these criteria will help you to plan your project and also when writing up. They include:
The project involves both the application of skills learned in the past and the acquisition of new skills. It allows students to demonstrate their ability to organise and carry out a major piece of work according to sound scientific and engineering principles. The types of activity involved in each project will vary but all will typically share the following features:
You may have noticed that there is both a 90pt version of the project (RTDS) and a 120pt version (RTDS+). The 120pt version is for students who have a previous Master's degree in an area relating to data science, and therefore want to take fewer classes and a larger project. If you wish to choose this option, you must speak to the CDT Year 1 organizer; see the MSc by Research Course Handbook for more information.
The RTDS+ project works the same as the RTDS project, except that: (a) You are expected to have selected a supervisor by Oct 1; (b) You should work on your project part-time in the autumn, in addition to the standard time in the spring and summer; and (c) The markers will look to see evidence of more work or a more advanced project, commensurate to the additional amount of time you have had. For example, a larger project might make a larger research contribution, apply more advanced methodology, contain more extensive experimental evaluation, etc.
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