DSC3008 : Contemporary Topics in Data Science and AI
- Inactive for Year: 2026/27
- Module Leader(s): Professor Philip James
- Lecturer: Professor Jaume Bacardit, Professor Nick Wright, Professor Barry Hodgson
- Owning School: Mathematics, Statistics and Physics
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
This module develops your understanding of current and emerging developments in data science, and artificial intelligence through engagement with advanced real-world applications, contemporary research, and industrial practice. You will investigate a range of specialist topics. The emphasis is on critically evaluating advanced methods, understanding their assumptions and limitations, interpreting their application within complex practical contexts, and developing the ability to adapt to rapidly evolving developments in data science and artificial intelligence.
Outline Of Syllabus
Topics covered by this module will draw from current leading-edge developments in research and industry and may therefore vary from year to year. Topics may be informed by contemporary research activity, industrial case studies, and emerging developments within data science and artificial intelligence, including material drawn from NICD case studies and collaborative projects.
Indicative topics may include:
AI entrepreneurship, innovation, and technology-driven opportunities
Societal impacts of automation, artificial intelligence, and emerging digital technologies
Emerging AI regulation, governance, standards, and responsible AI practice
Open-source AI ecosystems and collaborative AI development
High-performance, distributed, and scalable computing for AI systems
Edge AI, embedded intelligence, and on-device inference
Federated learning and privacy-preserving machine learning
Multi-agent systems, human–AI interaction, and AI for simulation and digital twins
Applications of Data Science and AI, including quantum, biomedical, cyber-physical systems, healthcare, and robotics
Advanced applied statistics and probabilistic modelling
Computational inference, simulation-based methods, and uncertainty quantification
Machine learning and statistical approaches for complex real-world data analysis
Guest lectures, industrial case studies, and critical evaluation of contemporary research papers and emerging developments in data science and AI
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 10 | 2:00 | 20:00 | Lectures |
| Guided Independent Study | Assessment preparation and completion | 1 | 10:00 | 10:00 | Preparation of formative work to a different audience in preparation of portfolio submission. |
| Guided Independent Study | Assessment preparation and completion | 1 | 30:00 | 30:00 | Preparation and completion of portfolio |
| Structured Guided Learning | Lecture materials | 1 | 30:00 | 30:00 | Reading through lecture materials – preparing / follow up |
| Guided Independent Study | Assessment preparation and completion | 1 | 30:00 | 30:00 | Preparation and completion of oral presentation |
| Guided Independent Study | Assessment preparation and completion | 1 | 30:00 | 30:00 | Preparation and completion of report |
| Guided Independent Study | Directed research and reading | 1 | 30:00 | 30:00 | Wider reading as per provided reading list |
| Scheduled Learning And Teaching Activities | Practical | 10 | 2:00 | 20:00 | Supervised practical sessions focused on hands on implementation |
| Total | 200:00 |
Teaching Rationale And Relationship
This module includes an encounter with the leading edge of research, industry and practice.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Portfolio | 2 | M | 30 | Collection of five, one-page reports, reflecting on the set of topics presented during the module. |
| Report | 2 | M | 40 | An extended report of six pages outlining a topic of the students choice in more detail |
| Oral Presentation | 2 | M | 30 | Presentation of up to eight minutes, based upon the students chosen topic |
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 |
|---|---|---|---|
| Reflective log | 2 | M | Establishing a Linkedin profile, and submitting appropriate posts based upon the components of the portfolio. |
Assessment Rationale And Relationship
The portfolio assessment enables students to engage critically with a range of contemporary topics presented throughout the module. Through a series of short reflective reports, students will evaluate emerging developments, applications, and debates within data science and artificial intelligence, drawing connections between research, industrial practice, and societal impact. The portfolio supports ongoing critical reflection and the development of informed perspectives across multiple areas of the field. (MLO1, MLO2, MLO5)
The extended report provides students with the opportunity to investigate a selected topic in greater depth through independent research, analysis, and critical evaluation. Students will synthesise material from contemporary research, industrial practice, and technical developments to produce a structured and evidence-based discussion of a topic related to modern data science or artificial intelligence. The assessment supports the development of independent learning, critical analysis, and technical communication skills. (MLO1, MLO2, MLO3, MLO5)
The oral presentation assesses students’ ability to communicate informed and evidence-based perspectives on advanced topics in data science and artificial intelligence to specialist and non-specialist audiences. Students will present and justify their analysis of a selected topic, demonstrating critical understanding, clarity of communication, and the ability to respond appropriately to discussion and questioning. (MLO3, MLO4)
The formative reflective log supports the development of reflective and professional communication skills through the creation of a professional profile and the preparation of short reflective posts linked to topics explored within the module. This activity encourages students to engage with contemporary developments in a public-facing and professionally relevant format.
The assessment structure supports the development of critical evaluation, independent investigation, communication, and reflective learning skills appropriate to advanced and rapidly evolving areas of data science and artificial intelligence.
Alternative assessment:
For students with a Student Support Plan (SSP) who have alternative assessment as a reasonable adjustment and/or students with supported Personal Extenuating Circumstances (PEC), it may be necessary to set an alternative assessment as presented below. Due to the professional requirements of some programmes, alternative assessments may not be available even where students are eligible.
Where appropriate, alternative arrangements for the oral presentation may include an individual recorded presentation and/or an alternative written reflective submission aligned to the same learning outcomes.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- DSC3008's Timetable