Data Science Methods (L6) (G5222)

15 credits, Level 6

Autumn teaching

On this module you’ll learn the practical tools and techniques required to complete the Data Science Process, including:

data pre-processing
exploratory data analysis
communication and visualisation
mathematical modelling
the development of a data solution or product.

To do this, you’ll develop your programming skills and be introduced to some fundamental data science packages and libraries.

You’ll also be introduced to some advanced mathematical tools and techniques that data scientists use in their day-to-day lives. This includes regression models, classification, and clustering. In practical sessions, you’ll apply these tools and techniques to real-world datasets.

Teaching

50%: Lecture
50%: Practical (Laboratory)

Assessment

100%: Coursework (Peer-review exercise, Portfolio, Report)

Contact hours and workload

This module is approximately 150 hours of work. This breaks down into about 43 hours of contact time and about 107 hours of independent study. The University may make minor variations to the contact hours for operational reasons, including timetabling requirements.

We regularly review our modules to incorporate student feedback, staff expertise, as well as the latest research and teaching methodology. We’re planning to run these modules in the academic year 2026/27. However, there may be changes to these modules in response to feedback, staff availability, student demand or updates to our curriculum.

We’ll make sure to let you know of any material changes to modules at the earliest opportunity.

Courses

This module is offered on the following courses: