CSC-30041 - Machine Learning Applications
Coordinator: Vishwash Batra Tel: +44 1782 7 33114
Lecture Time: See Timetable...
Level: Level 6
Credits: 15
Study Hours: 150
School Office: 01782 733075

Programme/Approved Electives for 2023/24


Available as a Free Standing Elective





CSC-10058 Introduction to Data Science I
CSC-10060 Introduction to Data Science II

Barred Combinations


Description for 2023/24

Students will develop an understanding of Machine Learning techniques and their application to Data Science. The module will focus on developing the skills of a professional data scientist in the context of designing and evaluating data science workflows for a project utilising machine learning. In addition, students will develop an understanding of the key developments in Data Privacy and Ethical AI and apply their knowledge in the design of their workflow and evaluation of their data science project.

The module provides in-depth training in the use of machine learning tools and techniques that will be used in order to analyse real world data and to deliver valuable insight that can be used to provide business services.

Intended Learning Outcomes

Apply appropriate machine learning techniques to real-world data sets: 1
Develop a complete data science project workflow that demonstrates understanding of ethical design: 1
Evaluate algorithmic decision-making for bias and explainability using performance metrics and fairness testing: 1

Study hours

20 hours of practical: 10 x 2 hour practical sessions
10 hours of lectures
20 hours coursework preparation
100 hours independent learning

School Rules


Description of Module Assessment

1: Report weighted 100%
Structured report outlining planning, implementation and evaluation of a data science project
A 3000 word report of a data science project where learners are asked to develop a machine learning model to analyse and evaluate a data set. As part of the project the learners should design a workflow and demonstrate understanding and application of ethical design concepts throughout, including exploratory analysis and testing for bias and explainability.