DSC8102 : Individual Project in Applied Data Science
- Offered for Year: 2025/26
- Module Leader(s): Professor Murray Pollock
- Owning School: Mathematics, Statistics and Physics
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 3 Credit Value: | 60 |
| ECTS Credits: | 30.0 |
| European Credit Transfer System | |
Aims
The aim of this module is to enable students to put their specialist skills, knowledge, and understanding into practice through the medium of a significant individual project and written dissertation. This can take the form of either:
(1) a major individual research project, or
(2) a consultancy type project for a client identified by the project supervisor and students, where available.
Specifically, the module aims to equip students with the following knowledge and skills:
• To deepen the knowledge and skills acquired in the programme through practice.
• To develop an awareness of the range and limitations of technologies available.
• To enhance research skills and awareness of the professional literature.
• To develop an awareness of the open problems in Data Science.
• To develop your own specialist expertise in the project topic
• To develop planning and communication skills to deliver a substantial project outcome
Outline Of Syllabus
Dissertation projects might involve working within one of the university’s established research groups in applied data science, or elsewhere in collaboration with another industrial or academic partner. Students will be supervised, throughout their project, by an experienced scientist, manager or academic within the applied discipline. These supervisors provide advice on the approaches and methods that are best suited to the problem; on collection/analysis of data; and guidance in producing a well-written dissertation.
Students will be required to complete a research proposal document as part of the project planning and design process.
Students present their findings both as a full written dissertation and to a generalist audience through a presentation.
Students will either select a project from a list offered by potential supervisors or propose and refine a project proposal with an academic supervisor. Projects will involve learning about some unfamiliar aspect of Data Science. In every project there will be a research component and a strong design, programming and/or analytic element.
1. Project Definition and Planning:
a. Bounding and clarifying a problem for research.
b. Background research; identifying and critically analysing relevant literature and information sources.
c. Decomposing a problem and forming a project outline in terms of goals and criteria for success.
d. Identifying resources and tooling required.
2. Supervision: Each project has a lead supervisor and second supervisor.
Additional supervision support may be provided by an industrial partner. The student and lead
supervisor will meet regularly throughout the period of the project.
3. Research: Background research will be undertaken in the selected topic using the skills
developed in earlier modules with access to the library and online resources.
The supervisor will advise on quality of sources and standards in the topic area.
4. Development and Analytic Skills: The core of the project will involve carrying out the
project plan largely independently, but with guidance from the supervisors.
5. Dissertation: A dissertation will be prepared, describing the technical background, the
work undertaken, the analysis of results and directions for further work. Guidance on the
style and content of an academic dissertation will be provided by means of workshops and
through the supervisor.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 24 | 1:00 | 24:00 | Interim report writing |
| Guided Independent Study | Assessment preparation and completion | 70 | 2:00 | 140:00 | Dissertation writing |
| Guided Independent Study | Assessment preparation and completion | 20 | 1:00 | 20:00 | Preparation and completion for oral presentation |
| Guided Independent Study | Project work | 100 | 3:00 | 300:00 | Project work |
| Scheduled Learning And Teaching Activities | Workshops | 5 | 2:00 | 10:00 | Cohort workshops on dissertation material (literature review, writing a dissertation) |
| Scheduled Learning And Teaching Activities | Dissertation/project related supervision | 6 | 1:00 | 6:00 | Supervisor meetings (4 hours of drop in sessions and 2 hours of individual support) |
| Guided Independent Study | Independent study | 50 | 2:00 | 100:00 | Background research |
| Total | 600:00 |
Teaching Rationale And Relationship
The project is undertaken with a lead academic supervisor, working in association with one of the research groups across the university or with a collaborating industrial research laboratory (in which case it will be supervised jointly). There will be a designated second academic supervisor to offer additional assistance.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Dissertation | 3 | M | 80 | 16 pages excluding appendices. To be written in a professional report style or equivalent piece of work |
| Oral Presentation | 3 | M | 20 | Presentation including demonstration (20 mins) |
Zero Weighted Pass/Fail Assessments
| Description | When Set | Comment |
|---|---|---|
| Research proposal | M | Interim report within first 4 weeks to assess aims and planned approach – 5 pages excluding appendices |
Assessment Rationale And Relationship
The interim report provides a means of assessing the project aims, methodology and planning.
The presentation provides for an assessment of students' skills at communicating work to a broader audience.
The dissertation provides for an assessment of professional skills in research and development, analysis, and detailed technical communication.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- DSC8102's Timetable