Data Science MSci Individual Project - 2024 entry
MODULE TITLE | Data Science MSci Individual Project | CREDIT VALUE | 30 |
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MODULE CODE | COMM032 | MODULE CONVENER | Dr Mohammed Abdelsamea (Coordinator) |
DURATION: TERM | 1 | 2 | 3 |
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DURATION: WEEKS | 11 | 11 | 1 |
Number of Students Taking Module (anticipated) | 30 |
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In this module, you will analyse and work towards the solution of a selected research problem in Data Science. This is an individual, independent piece of work that will include aspects of research, analysis and implementation to solve the chosen problem. You will work with an individual supervisor and often you will interact with the supervisor’s research group in the area. The diversity of topics in data science and the methods that can be brought to bear on them means that there is a wide range of possible topics and the module provides an opportunity for an in-depth exploration of a topic of particular interest to you.
Pre-requisite Modules: COM3021 Data Science At Scale
This module builds upon the experience gained in the individual project at level 3, allowing you to conduct a more advanced project with a substantial research element. The module aims to put into practice the knowledge acquired from the taught elements of the programme and to give you experience of many aspects of research work, including literature review, planning, experimentation and analysis, interpretation of results, and presentation. You will also gain valuable experience in concisely presenting scientific results via the writing of an academic research paper. Presenting the results of commissioned research in a compact and clear form is an essential research skill in both industry and academia.
On successful completion of this module, you should be able to:
Module Specific Skills and Knowledge:
1 Demonstrate knowledge of a research topic in data science, acquired through a deep and self-motivated exploration of that topic;
2 Design and follow systematically the phases of research project development;
3 Apply sophisticated and appropriate analysis and development techniques at each stage of a project;
Discipline Specific Skills and Knowledge:
4 Show familiarity with the background and context of a new application area;
5 Produce a concise research article;
Personal and Key Transferable / Employment Skills and Knowledge:
6 Conduct independent study, including library and web-based research;
7 Reflect critically on processes and products;
8 Plan an extended project and manage your time effectively;
9 Present your work to a non-specialist audience.
- Students are expected to have weekly meetings with their supervisor and maintain a project log-book which will be handed in along with the final report and assessed as part of the supervisor’s report. Log-book entries should record the subjects discussed and actions agreed, and will be signed and dated by both student and supervisor;
- Students should attend relevant departmental and Institute of Data Science & AI seminars;
- The final report should be written in the style of a research paper suitable for a conference or journal appropriate for the research topic;
- 10% of the marks for the module will come from your supervisor’s report, which will take into account your progress throughout the year, including attendance at meetings and the oral presentation, submission of agreed deliverables including the log-book, demonstration of ambition and initiative.
Scheduled Learning & Teaching Activities | 32 | Guided Independent Study | 268 | Placement / Study Abroad | 0 |
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Category | Hours of study time | Description |
Scheduled Learning and Teaching Activities | 12 | Lectures, Seminars |
Guided Independent Study | 20 | Supervision |
Guided Independent Study | 268 | Self-Study and Background Reading |
Form of Assessment | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Project Outline Talk | 20 minutes | 1,2,4, 6-8 | Oral commentary from supervisor and written feedback using customised marksheet |
Coursework | 90 | Written Exams | 0 | Practical Exams | 10 |
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Form of Assessment | % of Credit | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Literature Review and Plan | 20 | 10 pages | 4.5 | Written using customised marksheet |
Final Report | 60 | 30 pages | All | Written using customised marksheet |
Supervisor’s Report | 10 | N/A | 2,6,7,8 | Oral feedback from supervisor |
Demonstration | 10 | 15 minutes | All | Written using customised marksheet |
Original Form of Assessment | Form of Re-assessment | ILOs Re-assessed | Time Scale for Re-assessment |
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All Above | Coursework (100%) | All | Completed over summer with a deadline in August |
Re-assessment will be by resubmission of the final report only. For referred candidates, the mark will be capped at 50%. For deferred candidates the mark will be uncapped.
information that you are expected to consult. Further guidance will be provided by the Module Convener
CREDIT VALUE | 30 | ECTS VALUE | 15 |
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PRE-REQUISITE MODULES | COM2013, COM3021 |
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CO-REQUISITE MODULES |
NQF LEVEL (FHEQ) | 7 | AVAILABLE AS DISTANCE LEARNING | No |
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ORIGIN DATE | Wednesday 13th March 2024 | LAST REVISION DATE | Wednesday 11th September 2024 |
KEY WORDS SEARCH | Research Project; Literature Review; Data Science; Machine Learning; Statistics; Data Governance; Data Visualisation; Data Exploration |
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Please note that all modules are subject to change, please get in touch if you have any questions about this module.