Teaching & modules
Modules
Courses are divided into modules. You will normally take modules totalling 180 credits.
Year 1 – Required Modules
For the Part-Time MSc:
You are required to take:
- Statistical Theory (15 credits)
For the Full-Time MSc:
You are required to take:
- Statistical Theory (15 credits)
- Multivariate and High Dimensional Statistics (15 credits)
- Bayesian Statistics (15 credits)
- Computational Statistics (15 credits)
- MSc Statistics Project (60 credits)
Year 1 – Optional Modules
For the Full-Time MSc: additionally, you are required to take 60 credits from a range of optional modules, which may typically those listed below.
For the Part-Time MSc: additionally, you are required to take sufficient credits from a range of optional modules in order to bring your total for year one to 60 credits. Optional modules may typically include:
- Probability Theory (15 credits)
- Theory of Complex Networks (15 credits)
- Design of Experiments (15 credits)
- Machine Learning (15 credits)
- Stochastic Processes and Applications (15 credits)
- Stochastic Analysis (15 credits)
- Time Series Analysis (15 credits)
- Elements of Statistical Learning (15 credits)
- Computer Programming for Data Scientists (15 credits)
- Data Mining (15 credits)
- Pattern Recognition, Neural Networks and Deep Learning (15 credits)
- Stochastic Control and Applications to Algorithmic Trading (15 credits)
- Mathematical Biology (15 credits)
Year 2 – Required Modules (for Part-Time MSc only)
You are required to take 30 credits from the following list, if you have not already studied them in Year 1:
- Bayesian Statistics (15 credits)
- Multivariate and High Dimensional Statistics (15 credits)
- Computational Statistics (15 credits)
You will also need to take the following module:
- MSc Statistics Project (60 credits)
Year 2 – Optional Modules
Additionally, you are required to take sufficient credits to bring your total number of credits for the programme to 180 credits. This number will vary depending on how many required modules you took in year one. Optional modules may typically include:
- Probability Theory (15 credits)
- Theory of Complex Networks (15 credits)
- Design of Experiments (15 credits)
- Machine Learning (30 credits)
- Stochastic Processes and Applications (15 credits)
- Stochastic Analysis (15 credits)
- Time Series Analysis (15 credits)
- Elements of Statistical Learning (15 credits)
- Computer Programming for Data Scientists (15 credits)
- Data Mining (15 credits)
- Pattern Recognition, Neural Networks and Deep Learning (15 credits)
- Stochastic Control and Applications to Algorithmic Trading (15 credits)
- Mathematical Biology (15 credits)
Teaching methods - what to expect
We use lectures and group tutorials to deliver most of the modules on the course. Students will also be expected to undertake a significant amount of independent study and coursework.
Typically, one credit equates to 10 hours of work, e.g. 150 hours work for a 15-credit module. These hours cover every aspect of the module.
We will use a delivery method that will ensure students have a rich, exciting experience from the start. Face to face teaching will be complemented and supported with innovative technology so that students also experience elements of digital learning and assessment.
The majority of teaching for this degree takes place at King’s College London Strand Campus.
Assessment
- Coursework
- Written/practical examinations
- Unseen written examinations
- Class tests
- Online quizzes
Students' performance will be assessed through a combination of coursework and written/practical examinations. Forms of assessment may typically include unseen written exams, class tests, online quizzes and coursework submission.