| Unit name | Introduction to Scientific Computing |
|---|---|
| Unit code | GEOGM0054 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Professor. Tranos |
| 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 |
The unit will enable students to effectively utilise widely used tools in Data Science by introducing them to the fundamentals of scientific computing. Such tools include, but are not limited to, Linux shell and command line usage, GitHub and version control, SciPy, basic programming with Python for spatial applications, use and creation of metadata, parallelising code. This is a technical unit and the emphasis will be on how to use these tools using a variety of applications both from physical and human domains.
The unit aims to:-
Upon successful completion of this unit, students will:
1. Be able to use scientific computing to solve Geographic Data Science problems
2. Understand the complexity and the fundamentals of scientific computing
3. Collaboratively develop and deploy scientific software with version control on Unix-based systems.
4. Be able to develop high-performance computing strategies to do data science at scale
5. Become proficient in producing reproducible workflows.
6. Be able to explain, understand, and employ analytical methods to analyse earth observation imagery
7. Understand basic theory behind numerical optimization and its relationship to model comparison and hyperparameter optimization.
10 two-hour computer-lab based lectures (mixture of computer practicals and lectures)
Formative: (i) Open a Pull Request on GitHub to contribute to a project. (ii) Write a post about a computational problem on Stack Overflow.
Summative: Two 2,000-word reports (each worth 50% of the unit mark) describing the use of different scientific computing tools (e.g. linux and HPC, GitHub and version control, SciPy and basic programming with Python, parallelising code). The reports will be written in a reproducible manner and will include the necessary code.
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. GEOGM0054).
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.
