Aims:
To carry out a sustained and intensive piece of independent work on topics in stochastic modelling and data science and to
write a substantial report that communicates the results of this work to others interested in the topic.
Detailed Information
Pre-requisites: none.
Location: Edinburgh.
Semester: 3.
Syllabus:
Students can carry out projects on a variety of topics in stochastic modelling and data science. The project or projects
should take the student beyond the courses they have already been taught and examined in on the MSc.
Learning Outcomes: Subject Mastery
On completion of this module the student should:
• be able to access, use and demonstrate an understanding of the appropriate research literature
• have broadened their knowledge of stochastic modelling and data science
• Have improved their skills in reading research papers in the area
• Detailed and critical understanding of a selected recent development in the chosen topic
• Demonstrate expertise in applying a variety of stochastic or statistical techniqeus in the context of the problems
contained within the project(s)
Learning Outcomes: Personal Abilities
• Demonstrate the ability to learn independently
• Manage time, work to deadlines and prioritise workloads
• conduct a sustained and intensive piece of independent work on topics in actuarial science over a period of
weeks
• discuss the detail of their project(s) with their supervisor(s)
• perform numerical calculations using a suitable computer language or package as required for the project(s)
• write well-structured and coherent reports on their work in a way which can be easily be understood by their
examiners
• assess issues with working as part of a team, as required for the project(s)
Assessment Methods: Due to covid, assessment methods for Academic Year 2021-22 may vary from those noted on the official course descriptor. Please see the Computer Science Course Weightings and the Maths Course Weightings for 2020-21 Semester 1 assessment methods.
SCQF Level: 11.
Credits: 60.
Other Information
Help: If you have any problems or questions regarding the course, you are encouraged to contact the lecturer
Canvas: further information and course materials are available on Canvas