CSC-30035 - Module Specification
School of Computer Science and Mathematics
Faculty of Natural Sciences

Programme/Approved Electives for 2024/25

Available as a Free Standing Elective

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2024/25

The module develops the wider skills and behaviours required for a professional data scientists and applies these skills to plan and implement
their own data science work-based project. It requires learners to work collaboratively, both online and in person, to deliver on a work-based
project to a fixed timescale maintaining professional standards for technical development, governance and conduct. The module also provides
learners the opportunity to showcase their work demonstrating good communication and creativity in disseminating data and concepts.

To complete a work-based project that applies and develops key skills and behaviours required of a professional data scientist. The apprentice
will endeavour to identify an existing problem that could be reformulated to a data science solution that will inform and improve organizational
goals. The solution should include demonstration of statistical, data engineering, machine learning and software engineering skills required to
build, validate, implement and evaluate a data science solution. In the process the apprentice will demonstrate a professional approach to the
project by using project management techniques, data science related tools, governance and ethics standards and the ability to collaborate
effectively and with empathy with key stakeholders to complete a full project.
As well as technical skills apprentices will work collaboratively and creatively to present their work that will enable them to effectively
communicate and disseminate their project; develop and maintain collaborative relationships and teams, plan
and organize resources, develop creativity and inquisitiveness in approaches, adaptability and dynamism in being able to respond to varied tasks
and organizational timescales and professional integrity and personal development.

Intended Learning Outcomes

Identify a problem an organization faces and reformulate it to provide a Data Science solution that uses scientific, hypothesis-driven methods, stakeholder engagement and project delivery methods to plan and resource a data science work-based project that enables effective change (meeting KSB S1; S3; S8; B1; B2; B4): 1,2
apply and document appropriate governance methods and software development standards, including security, privacy, quality control, accessibility, code quality and version control (meeting KSB: S2; S3, B2): 3
Complete a work-based project that implements data solutions using data engineering and machine learning methods. tools and programming languages, including statistical analysis, feature selection and validation to build a model that informs and improves organization outcomes (meeting KSB: S3; S4; B4): 3,4
Implement and evaluate a data science solution using relevant software engineering architectures and design patterns (meeting KSB: S5): 4
demonstrate professional integrity and creativity in presenting and communicating work in a hypothesis-driven, impartial, appropriate and truthful manner that enables recommendations to key decision-makers to contribute towards meeting organization goals. (maps tp KSB: S6, B4, B5): 4
demonstrate professional conduct with regard to time management and supervisor contact: 1
complete a thorough review of the background literature, business context and any prior research to provide justification for the
rationale of the project and to inform the hypothesis.: 2
present their project in a manner of their choosing which is suitable for presentation at a student conference. The presentation should be supported by a written executive summary: 4

Study hours

7 hours of lectures
7 hours presentation and demonstration
12 hours university supervisor contact (24 x 30 minutes)
274 hours of work-based project activity (including assessment preparation)

School Rules

None

Description of Module Assessment