• Offered for Year: 2025/26

• Module Leader(s): Professor Barry Hodgson
• Lecturer: Dr Tatiana Alvares-Sanches

• 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.

Aims

To immerse students in the process of designing innovative solutions to contemporary societal and industrial challenges using data-driven approaches. To explore and critique leading-edge data science techniques, the opportunities and challenges of implementation in real-world settings and the associated ethical and legal considerations.

Outline Of Syllabus

Group innovation component:
Students will undertake self-directed group project work in ‘bootcamp’ style to provide a data-driven innovation solution to a previously unseen organisational or business challenge. The curriculum will include:
· Experiential-based collaborative working.
· Identifying customers and stakeholders.
· Identifying customer needs and understanding the customer journey.
· Deriving appropriate insights from relevant data.
· Developing a Value Proposition.
· Building a Business Model around the Value Proposition.
· Selecting and developing a Minimum Viable Product from multiple Value Propositions.
· Communicating with clients – making an ‘elevator pitch’.

Frontiers component:
Students will be challenged to undertake short ‘flip-learning’ projects, led by National Innovation Centre for Data (NICD) expert data scientists, to provide a technical solution to problems posed by NICD clients. The curriculum will cover:
· Scoping and specifying data science projects.
· Introduction to the data science product life cycle.
· Research into appropriate data science techniques to address the problem.
· Application of those techniques in the development of prototype solutions.
· Presentation and reporting of proposed solutions to clients and peers.
Each project will be based on a previous NICD project, with NICD data scientists on-hand to compare student approaches with the actual approach taken by NICD staff.

Legal skills – sessions will include (but not limited to)an overview of relevant legal frameworks, data protection, privacy, and security, equality and non-discrimination: the regulation of algorithmic bias

Student showcase - student presentations

Teaching Methods

Teaching Activities

Teaching Rationale And Relationship

The rationale for the Group and Frontiers components is as follows:

The purpose of the ‘boot camp’ approach to the group component is to intensively immerse the students in an experience that is somewhat outside of a ‘typical’ academic setting – introducing students to a real-world problem they will not have seen before and encouraging them to think about that problem together in their groups in innovative and novel ways. The presentation-based format gives the students something to work collectively towards, and fosters a sense of achievement in the development of a viable solution approach.

The Frontiers element will primarily employ a ‘flip’ learning style presenting case studies over three-week block periods. In week 1 students will be introduced to a particular business problem that NICD data scientists have faced, along with initial guidance as to how that problem may be addressed. They will then be expected to work in groups to research potential solutions, presenting these back to NICD data scientists in week 2, where further guidance to finesse or refocus the chosen approach will be provided. In week 3 the groups will convene to present and debate their solutions and receive feedback and critique from NICD data scientists as formative assessment. NICD data scientists will also detail how the Innovation Centre actually tackled the problem, with discussion of the approach across groups.

There will be ‘N’ (probably N=3) case studies presented over the course of the module. An additional, interim lecture will also be provided by NICD data scientists, outlining the general approach the Innovation Centre takes to try to ensure client success with data science, and to introduce other project topics not covered by the three main case studies (e.g. newer AI techniques applied to active projects). The last week of the module will be allocated for summative assessment by formal group presentation on one of the topics covered in the case study blocks.

The rationale for the Law component, the rationale is as follows:

Workshops are a combination of interactive lectures and dedicated skills activities, intended to inform students regarding the legal frameworks relevant for data science and the risks of algorithmic bias, with scenarios used relevant to data science practices. These sessions are intended to cement the core learning objectives and skills objectives for the legal component. These sessions are guided through the directed research and reading activity preceding each session, in which students are expected to read some of the relevant legislative frameworks as well as academic commentary chosen for the ability to communicate to students not necessarily having prior legal knowledge. After each workshop, there is a skills practice session, in which scenarios are presented on Canvas, and students then respond to a series of MCQs about that particular scenario, picking the most appropriate answers. Automatic feedback is provided for students, which gives indications of where knowledge and application are strong, and where there is a necessity to either return to their workshop notes or seek additional guidance and support.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment

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.

Assessment Rationale And Relationship

For the Group and Frontiers components, the rationale is as follows:

The primary assessment of the group and frontiers elements are by group presentation. This assesses both the technical solution provided by the group for the ‘frontiers’ element, and the innovative solution generated by the ‘boot camp’ element. The assessment will require the students to demonstrate the practicality and feasibility of both, as well as how the solutions fits the needs and goals of the client. The presentation also provides an indication of how cohesively the group has worked during the module and that they are able to communicate their approach to the client (who may not necessarily be technically oriented).

The formative diary element gives the student a chance to reflect on what they have learnt and to provide their own comment on how they have found group working, how effective their collaboration with their peers has been and personally critique their own solution from the main presentation.
The two assessments are intended therefore to be somewhat diverse (expressive group work vs. personal, internal reflection) and cover the multitude of intended learning and skill outcomes for the module.

Legal skills scenario question:
This scenario question, answered under exam conditions, is intended to be a short, 750 word piece worth 25% of the overall grade, which allows students to demonstrate their knowledge and understanding of the relevant legal frameworks, as well as demonstrate analysis through the application of this knowledge to a specific legal scenario that builds upon the workshops. In this scenario, students are provided with some details regarding a particular use of data in the context of data science work that presents potential legal and/or ethical issues. Students are expected to identify the issues arising in the scenario, apply the relevant legal frameworks, and then identify what obligations may exist, and what actions may be taken to remedy the problems identified. This allows for the testing of both the knowledge and skills-related outcomes, building upon the workshops, guided readings, and skills exercises.

Reading Lists

• DSC8004's Reading List

Timetable

• Timetable Website: www.ncl.ac.uk/timetable/
• DSC8004's Timetable