CSC-20067 - Module Specification
School of Computing and Mathematics
Faculty of Natural Sciences

Programme/Approved Electives for 2020/21

Available as a Free Standing Elective

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2020/21

Learners will develop an understanding of the fundamentals of Machine Learning and its 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, learners 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.

To provide an introduction to the concepts of statistical data analysis and machine learning. The module provides in-depth training in the use of machine learning tool kits in Python to analyse real world data and to deliver valuable insight that can be used to provide business services.

Intended Learning Outcomes

explain the fundamental vocabulary and concepts in machine learning: 1
apply machine learning to data using packages such as Scikit-Learn: 1
develop a complete data science project workflow which 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 online lectures
20 hours coursework preparation
100 hours independent learning

School Rules

None

Description of Module Assessment