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CSC-30055 - Module Specification
School of Computing and Mathematics
Faculty of Natural Sciences

For academic year: 2022/23 Last Updated: 19 January 2023

CSC-30055 - Preparing for the Data Science End Point Assessment
Coordinator: Shailesh Naire Room: MAC2.19 Tel: +44 1782 7 33268
Lecture Time: See Timetable...
Level: Level 6
Credits: 0
Study Hours: 241
School Office: 01782 733075

Programme/Approved Electives for 2022/23

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2022/23


Aims
The module is designed to support the apprentices as they prepare for the Data Scientist End Point Assessment module. It enables the apprentices to apply learning, matched to the relevant knowledge, skills and behaviors outlined in the Level 6 Data Science Apprenticeship standard, to work-based projects. Additionally, the apprentices will have the opportunity to prepare for post-gateway requirements.

Intended Learning Outcomes

Demonstrate the knowledge, skills and behaviors of a professional data scientist in a real-work environment to achieve real-work objectives: 1,4
Demonstrate the skills and behaviours required to plan and execute a work-based data science project, including an evaluation of processes followed with recommendations for future activities: 2
Pass a multiple choice knowledge test to demonstrate understanding of the full data science life cycle and the technologies that gather, store, process, analyze, model, visualise and evaluate data: 3

Study hours

10 hours scheduled lectures
24 hours structured writing sessions
4 hours of tutorial to review progress
2 x 90 minute mock knowledge tests
200 hours work-based project, portfolio and report preparation. and revision for knowledge test

School Rules

None

Description of Module Assessment

1: Portfolio weighted 25%
e-portfolio of work based projects
Develop an e-portfolio, mapped to the apprenticeship standard of 6 to 8 work-based data science projects.

2: Report weighted 25%
Report based on a full data science project
A complete draft of a 7500 report based on a data science work-based project.

3: Exercise weighted 25%
Mock Knowledge Test
Achieve a minimum 40% pass in a 30 question multiple choice test based on knowledge components of the Data Scientists apprenticeship standard.

4: Evaluation of Practice weighted 25%
Mock Professional Discussion
A discussion based on the e-portfolio to demonstrate the competency in data science skills and behaviors outlined in the apprenticeship standard.

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: 19 January 2023