CS350 Data Science Project
CS350-30 Data Science Project
Introductory description
The third year project is an extended, individual piece of work which forms a core element of the Data Science degree. Students choose a topic and find a supervisor in the summer term of their second year.
This module is not available to students from other departments.
Module aims
To provide experience of undertaking a significant individual research or development exercise from conception through to design, execution and delivery.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Students select a project during Term 3 of the second year by submitting an outline of the proposed project and finding an academic member of staff to supervise it. A detailed project specification is submitted during the first term. A written progress report on the current state of the project is submitted later in the first term for 5% credit (combined with the specification). A presentation of the final results of the project is given to the assessors during the second term for 15% credit. A detailed written report of the project is submitted early in the third term for 80% credit. The project is not a taught module but a major design and development exercise for the student carried out under supervision.
Learning outcomes
By the end of the module, students should be able to:
- - Plan and manage a significant individual project, including detailed considerations of resources, timetabling and professional issues.
- - Build a substantial software system from design to documentation; to perform a substantial analysis of data from initial hypotheses to well-supported inferences; or to carry out a substantial research project from methodology to conclusions.
- - Present their work orally, with appropriate use of audio-visual aids and interactive demonstrations, and respond to questions effectively.
- - Produce a substantial technical report and reflective writing.
Indicative reading list
Please see Talis Aspire link for most up to date list.
View reading list on Talis Aspire
Research element
The entire project may be based around undertaking a significant research exercise from conception through to design, execution and delivery.
Subject specific skills
The individual project involves consolidating, combining and applying a wide variety of subject specific skills gained in the rest of the degree course so far.
Transferable skills
- Technical - Technological competence and staying current with knowledge
- Communication - Verbal, listening, writing, technical communication skills, using different medium for communicating
- Critical Thinking - Problem-solving, analysis of possible solutions etc
- Multitasking - Soft skills such as time management, organization skills etc
- Creativity - Ability to harness creative ideas and turn them into tangible and strategic products/solutions
Teaching split
| Provider | Weighting |
|---|---|
| Computer Science | 50% |
| Statistics | 50% |
Study time
| Type | Required | Optional |
|---|---|---|
| Lectures | 8 sessions of 1 hour (3%) | |
| Seminars | 2 sessions of 1 hour (1%) | |
| Project supervision | 20 sessions of 30 minutes (3%) | |
| Practical classes | (0%) | 2 sessions of 2 hours |
| Online learning (independent) | 2 sessions of 1 hour (1%) | |
| Private study | 278 hours (93%) | |
| Total | 300 hours |
Private study description
Private study and independent learning in this module includes:
- Research into the subject area of the project, and into available existing solutions.
- Planning and managing own work.
- Preparing for and learning from supervision meetings.
- Designing, solving, programming, testing and evaluating own software artefacts or research outcomes.
- Preparation of the written reports and the oral presentation.
- Reflecting on feedback received on the progress report and the oral presentation.
Other activity description
Evaluation Day.
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Students can register for this module without taking any assessment.
Assessment group A5
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Progress Report | 5% | Yes (extension) | |
|
Progress report on project totalling 2000 - 3000 words maximum. This assessment is eligible for self-certification (extension). |
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| Oral Presentation | 15% | No | |
|
Duration - 25 minutes. This assessment is worth more than 3 CATS and is, therefore, ineligible for self-certification. |
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| Final Report | 80% | No | |
|
Final report on project totalling 12000 - 18000 words. This assessment is worth more than 3 CATS and is, therefore, ineligible for self-certification. |
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Assessment group R4
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Resit Report | 100% | No | |
|
Final project report totalling 12000 - 18000 words. This assessment is worth more than 3 CATS and is, therefore, ineligible for self-certification. |
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Feedback on assessment
Written Specification: Oral feedback from supervisor within 2 weeks of submission. Progress Report: Feedback via Tabula within 20 university working days. Oral Presentation: Feedback via Tabula within 20 university working days.
Pre-requisites
This module is not available to students from other departments.
Courses
This module is Core for:
- Year 3 of USTA-G302 Undergraduate Data Science
- Year 3 of USTA-G304 Undergraduate Data Science (MSci)
- Year 4 of USTA-G303 Undergraduate Data Science (with Intercalated Year)
Further Information
Terms 1, 2 and 3
30 CATS (15 ECTS)
Note: The format and content of modules CS310, CS344, CS350 and CS351 are currently identical.