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

For academic year: 2024/25 Last Updated: 14 May 2024

CSC-30035 - Professionalism in Data Science
Coordinator: Shailesh Naire Room: MAC2.19 Tel: +44 1782 7 33268
Lecture Time: See Timetable...
Level: Level 6
Credits: 30
Study Hours: 300
School Office: 01782 733075

Programme/Approved Electives for 2024/25

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2024/25


Aims
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 in a student knowledge exchange conference 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

1,2
3
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): 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): 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): 4
demonstrate professional conduct with regard to time management and supervisor contact: 1
provide a review of the background literature, business context and any prior research to provide justification for 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

1: Dissertation Plan weighted 25%
Project plan
The apprentice will write a full project plan which will include an initial project proposal and full project plan including planned methods, tools and resources required, evaluation of costs and alternative methodologies, governance and ethical requirements and time-management which identifies key milestones and outcomes. The project plan should also include a mapping to the Skills and Behaviours outlined in the apprenticeship standard and indicated in the ILOs.

2: Literature Review weighted 25%
Project Introduction and Background
The apprentice will write an Introduction that explains the scope and hypothesis of the project and terms of reference and a Background chapter which describes the background literature and prior work or research undertaken to support the development of the hypothesis and project.

3: Dissertation weighted 25%
Project Work Methodology
The apprentice will write a Work undertaken chapter that explains the project management techniques, data science related tools (e.g., data science lifecycle), governance and ethics standards applied in the project. This should include an Annex section which must provide 6-8 pieces of evidence of the application of the above and a mapping of the relevant Skills and Behaviours.

4: Evaluation of Practice weighted 25%
Project presentation
The apprentice will provide a summary presentation of their full project suitable for delivery at the student conference using any method of their choosing (with the exception of a project poster) examples include a simulation, video, oral presentation, PowerPoint show. The presentation should be supported with an executive summary of the project. Awareness of employer confidentiality should be demonstrated and presentation should have written approval from employer for external dissemination.

Please note that for the 2022/23 Academic Year programmes may be adapted to operate in accordance with a flexible, digital approach. This means that there may be some changes made to how modules are delivered and assessed. The latest information will be provided to you by your School in module handbooks. We endeavour to give you as much notice as possible of any changes and details about the circumstance in which changes are made to programmes can be found in the Student Agreement, available at: https://www.keele.ac.uk/student-terms-and-conditions/

Last Updated: 14 May 2024