MSc Modules and Assessment
Students on the MSc in Statistics take eight lecture-course modules, two of which are compulsory (core) modules:
An Introduction to Statistical Practice
The two core modules above provide a strong foundation in statistical methods, both theoretical and practical, for the rest of the MSc course. The Statistical Methods module includes an initial 'mock exam' intended for students to use as a focus for revision, and as an introduction to the UK style of written examination for those with little or no such experience. The Introduction to Statistical Practice module introduces statistical computing, using R, through hands-on practical classes on the analysis of real data from a variety of scientific and other disciplines; and develops such skills are report-writing, statistical graphics, etc.
The remaining six modules are chosen from a wide range of options, subject to availability, to suit the interests of individual students. The options include:
Bayesian Forecasting and Intervention with Advanced Topics
Applied Stochastic Processes with Advanced Topics
Medical Statistics with Advanced Topics
Designed Experiments with Advanced Topics
Multivariate Statistics with Advanced Topics
Bayesian Statistics and Decision Theory with Advanced Topics
Statistical Genetics with Advanced Topics
Advanced Topics in Data Science
The Advanced Topics in Data Science module is made up of 3 sub-modules, each sub-module giving a rapid treatment of a specific area of current interest in Data Science. The particular topics vary from year to year.
To complete the MSc, a student also undertakes a substantial project under the supervision of a Department member, and writes a dissertation reporting the results. Such projects can be in any of the areas covered by the MSc, including applied statistics, statistical methodology, computational methods, probability etc.
The following reading material will help you in making a good start on the MSc course.
Statistical Methodology (preparation for ST903):
The textbook by Casella, G., & Berger, R. L. Statistical inference. (Belmont, CA: Duxbury) is a good primer in particular for the core module ST903
Wasserman L.,All of Statistics: A Concise Course in Statistical Inference, Springer
An Introduction to Probability and Statistical Inference (second edition), by G.G. Roussas
Applied Statistics, Data Science and Learning R (preparation for ST952):
If you like some interdisciplinary sciences combined with Statistics, there is very accessible book, written by two leading biostatisticians/informaticians/data scientists http://web.stanford.edu/class/bios221/book/
Alternatively, the more standard path to learning R is here:
A good read is also the textbook by Julian Faraway on Linear Models with R (Chapman & Hall/CRC) http://www.utstat.toronto.edu/~brunner/books/LinearModelsWithR.pdf
If you are otherwise preoccupied and haven’t got the time, we hope that you may enjoy looking at these materials later. Please feel reassured that you are not required to study these resources before you start a course at Warwick in the Autumn.
Assessment is initially made for each module separately: some modules have an element of continuous assessment through coursework, but the majority of modules assessed through written examinations in May and June or, for some modules, January.
The performance of MSc students in their core and optional modules combined is then examined by an examinations board consisting of academic staff plus an External Examiner appointed from another university.
Dissertations are examined in the Department and then by the External Examiner. The MSc degree is awarded subject to satisfactory standard in the taught modules and dissertation. Students who do outstandingly well in their taught modules and the dissertation may be awarded the MSc with Distinction or Merit.