Information for current CDT students
Current ScotDIST PhD students must attend 120 credits of courses addressing data science, programming, machine learning, and computational physics in their first two years of study. Any ScotDIST student can attend approved courses at any of the three member universities to fulfull these requirements, and the courses may be attended internally at the universities, or via the Scottish Universities Physics Alliance.
You do not have to go through the formal assessments for the non-SUPA courses you take - you just need to attend the lectures (this is sometimes referred to as "auditing" them or attending them "class only"). You do need to do the SUPA assignments, which are already comparatively lightweight. If you would like to be assessed then you can ask the course organizer if they are willing to accept you, but this is not required for eíther you or them.
Your supervisor will need to confirm that they think you have sufficiently absorpbed the contents of non-SUPA courses - talk to them about how they would like to do this.
The courses you take should generally provide 60 data science-related credits, 40 subject-specific credits, and 20 transferable skills credits. By ‘credits’ we mean only the number of face to face hours per course that students are credited with - this term usually corresponds to the SCQF credit system.
Suitable courses are run by:
A specific list of relevant courses is below. If you find another course (maybe run elsewhere) that you think should be considered relevant to data-intensive science then ask your supervisor to get in touch with the ScotDIST management.
Data Science Courses
We have identified these courses as specifically relevant to the data science requirements in your programme. Other courses may be relevant as well - please consult with your supervisor and the management if there is a course you think should be included.
- SUPAADA: Advanced Data Analysis
- SUPAPYT: Introduction to Python
- SUPACOO: C++/Object Oriented Programming
- INFR11073: Machine Learning & Pattern Recognition
- INFR10069: Introductory Applied Machine Learning
- INFR11088: Extreme Computing
- INFR11134: Probabilistic Modelling and Reasoning
- CS5001 Object-Oriented Modelling, Design and Programming
- CS5030 Software Engineering Principles
- CS5033 Software Architecture
- CS5044 Information Visualisation and Visual Analytics
- CS5052 Data-Intensive Systems
- IS5102 Database Management Systems
- COMPSCI5059 Software Engineering (M)
- COMPSCI5014 Machine Learning (M)
- COMPSCI5076 Database Theory and Application (M)
- COMPSCI5004 Algorithms And Data Structures (M)
- COMPSCI14064 Big Data: Systems, Programming, and Management (H)
- STATS5016 Big Data Analystics (Level M)
- STATS5014 Bayesian Statistics (Level M)
- STATS5051 Advanced Data Analysis (Level M)
- STATS5028 Statistical Inference (Level M)
- STATS5056 Functional Data Analysis
1. If the course is not on the wiki list then please email your local management (see below) who will check if it meets the requirements.
2. If it's on the list, or after it's been OK'd, sign up for the course - see below for the process, which will depend where the course is being held.
3. Once you this is confirmed then email Sean Farrell copying in your local organizers again, asking him to add the courses to your SUPA profile - he will need the course details and your SUPA user name. 4. Go to the course, pay attention and learn things.
Email Liz Paterson and ask to be registered for the course. She will register you as "class only", meaning that you won't be required to submit coursework, but should attend the lectures. If you especially want to be assessed then please ask the course organizer.
Glasgow students enroling in courses as part of the Maths & Statistics Data Analystics MSc, or the Computing Science Data Science MSc, should use the Moodle pages for these modules to enrol. In the case that this is impossible, e.g. if the MSc coordinators have blocked the pages to anyone not formally enroled on the MSc, then please email the lecturer for the course you want to attend and CC firstname.lastname@example.org. We have an agreement with the MSc coordinators that you can "audit" the courses, i.e. attend lectures and tutorials but not take the final exam -- if this is explained to the lecturer, they should find a way to accomodate you.
St Andrews Sign-up
St Andrews students enrol in local courses by emailing the course organiser and Cc’ing Rita Tojeiro (email@example.com). Coursework will not be formally assessed, but students are expected to complete it and feedback will be provided
Glasgow: Andy Buckley
St Andrews: Rita Tojeiro
A key part of the ScotDIST PhD programme is a six month work placement during the degree at a partner company.
These placements are intended to give students experience of modern data science methodologies, let them apply the skills they learn to new and varied problems, and build links between the companies and universities. The placements can be in any period after the first two years of study, and can be split into two periods of three months at the same company if that is acceptable to all involved.
Students are welcome to seek out and plan placements on their own, or they can be arranged in discussion with ScotDIST. In particular, companies based at the Higgs Centre for Innovation will be a source of opportunities.
If you are a representative of a company and are interested in placing a PhD student, please our Industrial Placements page for further information.