Unit information: Data Science and Machine Learning in Geography in 2025/26

Unit name	Data Science and Machine Learning in Geography
Unit code	GEOGM0053
Credit points	20
Level of study	M/7
Teaching block(s)	Teaching Block 2 (weeks 13 - 24)
Unit director	Dr. Wolf
Open unit status	Not open
Units you must take before you take this one (pre-requisite units)	None.
Units you must take alongside this one (co-requisite units)	None.
Units you may not take alongside this one	None.
School/department	School of Geographical Sciences
Faculty	Faculty of Science

Unit Information

This unit will enable students to understand and deploy cutting edge data science & machine learning methods for urban data. This includes, but is not limited to:

• Methods for image analysis (support vector regression, neural networks for scene segmentation)
• Methods for data science (dimension reduction & advanced regression)

The unit aims to:

• Solidify learning from the previous course (Introduction to Scientific Computing) by intensifying the use of standard unix tooling & GitHub/version control
• Teach the fundamentals of methods in data science and machine learning that are common in urban studies
• Empower students to use these methods on problems relevant to their dissertations

Your learning on this unit

Upon successful completion of this unit, students will be able to:

1. Apply scientific computing to analyse both image and non-image data.
2. Successfully apply scientific computation tooling and infrastructure (version control & scientific software development methods)
3. Describe and discuss common data science & machine learning algorithms and make their results interpretable.

How you will learn

Computer-lab based lectures (mixture of computer practicals and lectures)

How you will be assessed

Tasks which count towards your unit mark (summative):

A final report with a weighting of 100% detailing the deployment of a specific data science/machine learning method to solve a problem. The reports will be writing in a reproducible manner and will include necessary code, graphs, and data. All ILOs will be tested.

When assessment does not go to plan test test test

Students will be offered alternative assessments for completion in the summer reassessment period, of a similar format to that of the original submissions.

Resources

If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.

If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. GEOGM0053).

How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours of study to complete. Your total learning time is made up of contact time, directed learning tasks, independent learning and assessment activity.

See the University Workload statement relating to this unit for more information.

Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.

The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit. The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates within the Regulations and Code of Practice for Taught Programmes.