CSC8633 : Group Project in Data Science (Inactive)
- Inactive for Year: 2024/25
- Module Leader(s): Dr Wanqing Zhao
- Lecturer: Dr Huizhi Liang
- Owning School: Computing
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
| European Credit Transfer System | |
Aims
In this module you will have the opportunity to design, build, deploy and support a data science system. You will have access to technical guidance from faculty members but will essentially work as a small independent team under your own initiative. This provides an opportunity to practice the technical skills developed in other modules, and to develop new skills of cooperative working and organisation.
Specifically, the module aims to equip students with the following knowledge and skills:
- To gain and reflect on the experience of applying the techniques taught in preceding modules to develop
a data science system.
- To gain experience of working in groups and to design and implement software under time and resource
constraints, practising relevant professional skills.
Outline Of Syllabus
1. Team Working:
Teams will be selected by the faculty to create teams with mix of skills and abilities for a range of roles. Each team has a faculty member who can advise on the problem specification but will not generally intervene in the group. At the end of the project, there will be an opportunity to debrief with faculty members and to share experience of good and bad practice in team working.
2. System Synthesis and Analysis:
Based on an initial specification of functional and non-functional requirements each team will develop a system which considers: the availability of data sources and existing knowledge, the professional, legal and ethical issues relevant to the subject area of the project, the potential modelling techniques and the trade-off between performance and accuracy and the end user and ongoing reliability and usability of the system and its results. Development will involve research, requirements elicitation, modelling, and analysis. Selection of development methods and tools will be done by each group.
3. Reporting:
There will be regular reporting of technical progress and a large final technical deliverable for the group. The project constraints will define the details in final technical deliverables for the group. Students will also prepare a short individual report outlining their contributions and the lessons that they have learned from the project in terms of their own continuing professional development.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 30 | 1:00 | 30:00 | Writing up the project and preparation for the group presentation and oral examination. |
| Guided Independent Study | Directed research and reading | 10 | 1:00 | 10:00 | background reading |
| Scheduled Learning And Teaching Activities | Practical | 1 | 0:30 | 0:30 | Individual Oral examination (PiP or synchronous online) |
| Scheduled Learning And Teaching Activities | Practical | 6 | 1:30 | 9:00 | Regular help sessions, progress review and discussion (PiP or synchronous online) |
| Scheduled Learning And Teaching Activities | Practical | 2 | 1:00 | 2:00 | Group presentations (PiP or synchronous online) |
| Guided Independent Study | Project work | 47 | 1:00 | 47:00 | Work on the group project |
| Scheduled Learning And Teaching Activities | Module talk | 1 | 1:30 | 1:30 | Module introduction (PiP or synchronous online) |
| Total | 100:00 |
Teaching Rationale And Relationship
Students will develop: teamwork and communication skills through group learning; presentation skills through giving presentations; computational and statistical writing skills through private study, group learning and writing a report. The nature of the projects will allow students to consolidate their learning from the previous taught modules, and to begin to develop specialised knowledge and practical skills in the analysis and interpretation of Data Science.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 2 | M | 50 | Group report prepared by each group, typically accompanied additional material online. Maximum page limit 10 pages. |
| Report | 2 | M | 30 | Individual report documenting the teamwork process embedded in the group project. Maximum page limit 4 pages. |
| Oral Presentation | 2 | M | 20 | Presentation slides and group activity to which each member is expected to contribute equally. This will take place towards the end of the module. |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Oral Examination | 1 | M | Individual oral examination documenting the teamwork process embedded in the group project. |
Assessment Rationale And Relationship
The group report and presentation will assess the technical product of students' group project.
The individual report tests the students’ ability to use key frameworks to explore how principles of teamwork and project management were embedded in the project as well as identifying the areas of personal development in the key competencies that were developed as part of the group technical contribution.
The individual oral exam is to evaluate the student’s engagement in the technical development and understanding of the overall group project.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CSC8633's Timetable