CSC-30073 - Module Specification
School of Computer Science and Mathematics
Faculty of Natural Sciences

Programme/Approved Electives for 2026/27

Available as a Free Standing Elective

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2026/27

This module offers the opportunity to prepare you for End Point Assessment through showcasing critical data science knowledge, skills, and behaviors in a portfolio of 6 mini-workplace projects. There can be more projects in the portfolio but 6 is the minimum requirement.
The module has two more simulating key components of EPA i.e. Knowledge test and mock professional discussion. At the end of this module you will be ready to enter EPA gateway.

This module aims to empower Level 6 Data Science apprentices to solve real-world problems using actual data from their company. Students will develop skills in collecting and processing data, and design and implement data science methods such as machine learning, AI techniques, and mathematical modeling. They will apply and evaluate these techniques to address specific business challenges, while collaborating with workplace line managers to ensure alignment with organizational objectives. The module also emphasizes building professional relationships, generating valuable business insights, and preparing a comprehensive portfolio for the End Point Assessment (EPA) to demonstrate the practical application of acquired knowledge, skills, and behaviours in the workplace.

Intended Learning Outcomes

Apply knowledge, skills, and behaviors of a professional data scientist in a real-work environment to achieve real-work objectives.: 2,3
Analyze and execute a work-based data science project, demonstrating the skills and behaviors needed for planning, including an evaluation of processes followed, and synthesize recommendations for future activities.: 2,3
Evaluate understanding of basic mathematics, statistics, data analysis techniques, ethics, security, and governance relevant for data scientists.: 1

Study hours

Active learning hours: 12
Class test preparation: 50
Portfolio development: 188
Professional Discussion including preparation : 50

School Rules

None

Description of Module Assessment