• Inactive for Year: 2024/25

• Module Leader(s): Dr Paul Goodman

• 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

This module has a two-fold aim: 1) to introduce students to the realities faced by practitioners of data science, giving insight into the professional skills required to tackle those realities, and 2) to give students knowledge of how cutting-edge data science, machine- and artificial-intelligence are being used to address real-world problems faced by businesses and institutions.

Outline Of Syllabus

In order to achieve the two aims, the module leverages the project-based experience of the technical staff of the National Innovation Centre for Data (NICD). Course content will be generated from a variety of case studies of past NICD projects, combined with training and workshop material produced both for clients of those projects and for the wider business community, to provide a roster of current ‘commercial trends’ in data science. For example, course content could include (but not be limited to) the following:

* Scoping and specifying data science projects.
* Use of deep learning for computer vision tasks.
* Automated report generation.
* Statistical modelling versus machine learning.
* Use of reinforcement learning in business.
* Practical use of Large Language Models (LLMs) and Natural Language Processing (NLP).
* Use of Low Code and No Code solutions.
* Exploring AI on Edge Computing and Internet of Things (IoT) devices.
* Developing descriptive data products.
* Responsible AI, AI assurance and ethical issues in AI.

The module will employ a ‘flip learning’ process:

* The module will be broken into five, two-week, sub-modules, each one based around a
particular case-study or innovative data science technique.
* A brief introduction to a case-study and associated data science problem will be given
in week 1, along with access to relevant material from NICD to study.
* Students will be expected to work in groups on addressing the problem over the following week.
* In week 2, groups will present their findings to NICD technical staff for comment and discussion

Teaching Methods

Teaching Activities

Teaching Rationale And Relationship

The module will be offered twice: once in Stage 2 and once in Stage 3, with a particular syllabus in a given academic year being based on five topics chosen from the current range of completed or ongoing projects at the Innovation Centre and based on the training materials developed alongside such. In the following year an alternate syllabus will be chosen, with content alternating between the two sets, subject ot content review and refresh.

The approach of having two, alternating annual syllabuses, with both Stage 2 and Stage 3 simultaneously taking one syllabus reduces the burden on NICD technical staff in delivery of content, whilst also enabling the content to be kept current as data science rapidly develops and matures as a field. Course content will primarily be based on NICD’s commercial offerings of ‘Masterclass’ workshops, restructured to facilitate a more linear learning and coursework-based approach.

The module uses a structure based on each syllabus being comprised of three ‘sub-modules’, taught via ‘flip-learning’. The structure of the flip-learning approach forces students to self-discover knowledge over the course of each sub-module. Each sub-module will comprise of a self-contained, three-week teaching component: Week 1 – There is an introduction to a particular topic or problem space by a lecturer and appropriate member of NICD staff. Week 2 – Students work over week one and week two to develop a group presentation of the topic, which is then delivered to the lecturers and appropriate NICD technical staff members. Week 3 – A feedback/discussion session is held where groups are given feedback from lecturers/NICD technical staff members as to their presentations, and the practicalities of their solutions. Week 3 gives a chance for groups to discuss and critique each other’s solutions.

A ‘case-study' lecture will be provided as an ‘interlude’ between the second and third sub-module, to both provide students with a break from the group-based activities, and to introduce them to the kind of real-world projects technical data scientists at NICD get involved with on a daily basis

Assessment Methods

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

Exams

Zero Weighted Pass/Fail Assessments

Assessment Rationale And Relationship

Group-based presentations promote collaborative working, allowing the students to share ideas whilst contributing individually to the final result. The approach fosters teamwork skills – which are essential pre-requisites in many professional data science settings.

The flip-learning/presentation-based approach allows the module to leverage the technical experience of NICD’s data science team in providing valuable feedback to the students on their considered approaches, without unduly impacting the commercial, project-work being undertaken by NICD.

The presentation-based approach to flip-learning assessment also provides ample time for feedback and reflection from both group peers and from lecturers and technical staff. Exposure to at least one formative assessment presentation prior to the final summative assessment should enable students to reflect and improve on their presentation skills.

Reading Lists

• DSC2001's Reading List

Timetable

• Timetable Website: www.ncl.ac.uk/timetable/
• DSC2001's Timetable