Data Science Methods Autumn (L7) (970G1A)

15 credits, Level 7 (Masters)

Autumn teaching

On this module, you’ll gain the practical tools and techniques needed to analyse and interpret real-world datasets. You’ll cover most aspects of the Data Science Process, including:

  • data wrangling and cleaning
  • exploratory and confirmatory data analysis
  • data visualisation and communication.

You’ll also explore some advanced mathematical tools and techniques data scientists use in their day-to-day lives, such as:

  • regression models
  • classification
  • clustering.

In practical sessions, you’ll develop and apply these tools and techniques to real-world datasets. You’ll use some fundamental data science packages/libraries that are widely used in industry.

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 44 hours of contact time and about 106 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鈥檙e 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鈥檒l 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: