CSC8632 : Data Science in the Wild

Semester 2 Credit Value: 10
ECTS Credits: 5.0


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. Building on taught modules in the first semester 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 understand of frameworks for project, time and team management
- To develop research skills in data science

Outline Of Syllabus

This module covers the principles of research and professional skills that are required to practice Data Science in the real-world. 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
Guided Independent StudyAssessment preparation and completion10:300:30Oral Examination
Guided Independent StudyAssessment preparation and completion50:302:30Preparation for oral examination
Guided Independent StudyAssessment preparation and completion51:005:00Seminar preparation
Guided Independent StudyAssessment preparation and completion61:006:00Lecture follow up
Scheduled Learning And Teaching ActivitiesLecture61:006:00Lectures
Scheduled Learning And Teaching ActivitiesSmall group teaching51:005:00Seminars
Guided Independent StudyProject work271:0027:00Coursework
Scheduled Learning And Teaching ActivitiesDrop-in/surgery100:305:00Help sessions
Guided Independent StudyIndependent study431:0043:00Background reading
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
Report2M100Research project proposal. Word count: Up to 2,000 words
Zero Weighted Pass/Fail Assessments
Description When Set Comment
Oral ExaminationMStructured discussion including reflection on the key learning objectives of the coursework project
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.

Reading Lists