| 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 | |
| School/department | School of Geographical Sciences |
| Faculty | Faculty of Science |
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:
The unit aims to:
Upon successful completion of this unit, students will:
1. Be able to use scientific computing to analyse both image and non-image data.
2. Have full mastery of scientific computation tooling and infrastructure (version control & scientific software development methods)
3. Understand common data science & machine learning algorithms and make their results interpretable.
10 two-hour computer-lab based lectures (mixture of computer practicals and lectures)
Two 8-page reports (33% and 67% of unit mark respectively) 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.
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 Faculty workload statement relating to this unit for more information.
Assessment
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. If you have self-certificated your absence from an
assessment, you will normally be required to complete it the next time it runs (this is usually in the next assessment period).
The Board of Examiners will take into account any extenuating circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
