In this digital and data-rich era the demand for statistics graduates from industry, the public sector and academia is high, yet the pool of such graduates is small. The recent growth of data science has increased the awareness of the importance of statistics, with the analysis of data and interpretation of the results firmly embedded within this newly recognised field.
This programme is designed to train the next generation of statisticians with a focus on the newly recognised field of data science. The syllabus combines rigorous statistical theory with wider hands-on practical experience of applying statistical models to data. In particular the programme includes:
classical and Bayesian ideologies linear and generalised linear models computational statistics applied to a range of models and applications regression data analysis
Graduates will be in high demand. It is anticipated that the majority of students will be employed as statisticians within private and public institutions providing statistical advice/consultancy.
To be awarded the MSc degree you need to obtain a total of 180 credits. All students take courses during semester 1 and 2 to the value of 120 credits of which compulsory course units comprise 60 credits. Successful performance in these courses (assessed via coursework or examinations or both) permits you to start work on your dissertation (60 credits) for the award of the MSc degree. The dissertation will generally take the form of two consultancy-style case projects or an externally supervised project.
Compulsory courses (60 credits):
Statistical Theory (10 credits, semester 1) Statistical Regression Models (10 credits, semester 1) Bayesian Theory (10 credits, semester 1) Statistical Programming (10 credits, semester 1) Bayesian Data Analysis (10 credits, semester 2) Likelihood and Generalised Linear Models (10 credits, semester 2)
Optional courses (60 credits) include:
Data Analysis (20 credits, semester 1) Introductory Applied Machine Learning (10 credits, semester 1) Text Technologies for Data Science (10 credits, semester 1) Fundamentals of Optimization (10 credits, semester 1) The Analysis of Survival Data (10 credits, semester 2) Stochastic Modelling (10 credits, semester 2) Multilevel Modelling (20 credits, semester 2) Large Scale Optimization for Data Science (10 credits, semester 2) Modern Optimization Methods for Big Data Problems (10 credits, semester 2) Time Series Analysis and Forecasting (5 credits, semester 2) Combinatorial Optimization (5 credits, semester 2) Probabilistic Modelling and Reasoning (10 credits, semester 2)
At the end of this programme you will have:
knowledge and understanding of statistical theory and its applications within data science the ability to formulate suitable statistical models for new problems, fit these models to real data and correctly interpret the results the ability to assess the validity of statistical models and their associated limitations practical experience of implementing a range of computational techniques using statistical software R and BUGS/JAGS
Trained statisticians are in high demand both in public and private institutions. This programme will provide graduates with the necessary statistical skills, able to handle and analyse different forms of data, interpret the results and effectively communicate the conclusions obtained.
Graduates will have a deep knowledge of the underlying statistical principles coupled with practical experience of implementing the statistical techniques using standard software across a range of application areas, ensuring they are ideally placed for a range of different job opportunities.
The degree is also excellent preparation for further study in statistics or data science.
At least a UK 2:1 degree, or its international equivalent, in a numerate discipline such as (but not limited to) mathematics, engineering, computer science, physical or biological sciences, economics or business. Some previous study of probability, statistics and mathematics (equivalent to two years of the Edinburgh Mathematics and Statistics BSc) is required.
Recipient: University of Edinburgh
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