CSC8632 : Data Science in the Wild (Group Project)
- Offered for Year: 2024/25
- Module Leader(s): Dr Tatiana Alvares-Sanches
- Lecturer: Dr Xinhuan Shu
- 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
Real-world industry led challenges in Data Science require an academic foundation in statistics and computer science combined with domain knowledge, practical resourcefulness and research skills. This module aims to equip students with the following knowledge and skills:
- To develop domain knowledge through active learning
- To develop practical experience of operations that influence data sources origins and data streams
- To develop an understand of the ethical, legal and social implications of data science projects
- To develop an understanding of business acumen, commercial risk and professional skills in data science
- To develop an understanding of frameworks for project, time and team management
- To develop research skills in data science
- To develop an ability to work as a member of a development team
- To develop an ability to tackle a significant technical problem
- To develop an ability to develop and apply new technologies
Outline Of Syllabus
This module covers the principles of research and professional skills that are essential to Data Science practice. The taught content is complemented by practical experience where the students embed themselves in a suitable institute or immerse themselves in a research area to gain domain knowledge. Insights gained from this experience are then used to propose a Data Science project within that company, institute or area of research and evaluate the merits of the proposal based on each of taught topics listed.
1. The data science industry
2. Business models, intellectual property and management
3. Project, time and team management
4. Ethics and governance in data science
5. Technical and institutional readiness for data science
6. Research skills and critical evaluation of data and literature sources
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Structured Guided Learning | Lecture materials | 6 | 1:00 | 6:00 | Lectures (Present in Person) |
| Guided Independent Study | Assessment preparation and completion | 1 | 0:30 | 0:30 | Oral Examination |
| Guided Independent Study | Assessment preparation and completion | 5 | 0:30 | 2:30 | Preparation for oral examination |
| Guided Independent Study | Assessment preparation and completion | 27 | 1:00 | 27:00 | Coursework - Project work |
| Guided Independent Study | Independent study | 43 | 1:00 | 43:00 | Background reading |
| Guided Independent Study | Independent study | 5 | 1:00 | 5:00 | Seminar preparation |
| Guided Independent Study | Independent study | 6 | 1:00 | 6:00 | Lecture follow up |
| Scheduled Learning And Teaching Activities | Scheduled on-line contact time | 10 | 0:30 | 5:00 | Help sessions (Present in Person) |
| Scheduled Learning And Teaching Activities | Scheduled on-line contact time | 5 | 1:00 | 5:00 | Seminars (Present in Person) |
| Total | 100:00 |
Teaching Rationale And Relationship
The teaching methods provide a framework for the student to understand and investigate the principles of applying data science in industry. The independent project work then enables the student to embed themselves in a suitable institute or immerse themselves in a research area to gain domain knowledge.
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 | 80 | Group report (80%) report for whole group. Word count: Up to 2,000 words. |
| Report | 2 | M | 20 | Individual written section of Project Report. A single side of A4 paper. |
Zero Weighted Pass/Fail Assessments
| Description | When Set | Comment |
|---|---|---|
| Oral Examination | M | Structured discussion/interview including reflection on the key learning objectives of the coursework project |
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 |
|---|---|---|---|
| Observ of prof pract | 2 | M | Formative assessment will be provided at the end of each day through an oral feedback session. |
Assessment Rationale And Relationship
Domain knowledge, industry awareness and employability are key elements of the data science profession that are driven by self-development. The assessment tests the students’ ability to use key frameworks to explore applications of data science in industry and improve the students’ professional network and employability.
The semi structured interview facilitates a reflective discussion about how individual students have met the learning objectives of the module and how the principles of professional practising in data science were embedded in the student’s practical experience.
The Formative assessment will be provided at the end of each day through an oral feedback session.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CSC8632's Timetable