ADVANCE YOUR CAREER WITH
The Statistics Group at the University of Glasgow is internationally renowned for its research excellence. Students are able to benefit from this by learning from academics whose expertise covers the analysis of data from a wide range of applications.
Designed for part time study, this programme allows you to gain an MSc degree from a leading university while you are still in full-time employment. Plus, from day one you can start to put your new knowledge to the test at work. You won't have to wait until you've graduated to make a real difference in the workplace.
You will have the freedom to work at your own pace and access to a wide range of learning tools including rich interactive reading material and tutor-led videos. You will also be able to arrange tailored one-to-one sessions with our academic team.
KEY PROGRAMME OUTCOMES
- Demonstrate thorough understanding of the concepts, principles, theories and methods of probability, statistics and machine learning.
- Developing strategies for modelling and analysing potentially large and complex data.
- Communicate and visualise insights gained from data.
- Design and develop software to perform data management, data extraction, statistical analyses and, as far as possible, automate these, using different tools and programming languages such as R, Python, Spark and TensorFlow.
Our students contribute a wealth of professional experience from a variety of sectors including finance, the pharmaceutical industry, banking, IT services and government statistical services amongst others.
Our students come from all over the world, bringing a rich mixture of personal experiences, cultural backgrounds and strong sense of internationalism to our student community. The open and welcoming atmosphere amongst students and staff creates an inspiring yet supportive environment for collaborative learning, and personal and professional growth.
This flexible part-time programme is completed over two years. You will take two courses each trimester.
Programme alteration or discontinuation
The University of Glasgow endeavours to run all programmes as advertised. In exceptional circumstances, however, the University may withdraw or alter a programme. For more information, please see: Student Contract
An in-depth introduction to the statistical software package SAS, including the use of Structured Query Language (SQL). This course covers all the features required for SAS certification as a Certified SAS Base Programmer and a Certified SAS Statistical Business Analyst.
Looking at models which can account for a non-normal distribution of the response and/or the fact that data is not independent, but correlated. You will gain an overview of different generalisations of linear regression models and become acquainted with the theory of exponential families. You’ll also be introduced to generalised linear models and the concept of a time series.
This course will introduce you to different approaches to learning from data, with a focus on interval estimation, hypothesis testing and frequentist and Bayesian model-based inference. You will then learn how to implement these statistical methods using R.
This course will introduce you to predictive modelling using multiple linear regression as a showcase. It will present some of the distributional theory underpinning the normal linear models and the associated methods for testing and interval estimation. You will also find out how the design matrix of a linear model can be constructed to accommodate categorical covariates or, through basis expansions, non-linear effects.
Provides a structured development of probability theory and its use to construct stochastic models. Your learning will place emphasis on the theory of random variables and random vectors to help solve real-life problems. The pace of the course is brisk, as it begins from the assumption that you have little prior exposure to probability yet reaches advanced concepts by the end.
Designed to introduce you to programming in the statistical software environment R. You’ll be introduced to basic concepts and ideas of a statistical computing environment and trained in programming tools which use the R computing environment. The course provides computational skills which will support other courses on the programme and you will learn the fundamental concepts in scientific programming.
The course introduces you to applications of data analytics in business and industry and introduces students to the social, ethical, legal, and professional issues arising in data science. It also delivers experience in the communication and presentation of results.
This course will provide you with a grounding in data mining and machine learning methods used in big data scenarios. You will also learn methods for analysing networks and unstructured data, as well as formal methods for social network analysis and quantitative text analysis.
This course will introduce you to object-oriented programming and Python as a generic programming language and its use for data programming and analytics. You will learn to use Python libraries that are relevant to data analytics such as scikit-learn, NumPy/SciPy and pandas.
The course focuses on high-performance computing and presents an overview of big data systems. You’ll be introduced to Julia as well as fundamental concepts in high-performance computing with a focus on parallelisation. You’ll also be trained in the efficient implementation of computationally expensive data-analytic methods, and introduced to enterprise-level technology relevant to big data analytics such as Spark, Hadoop or NoSQL databases.
Develops the foundations of modern Bayesian statistics and demonstrates how prior distributions are updated to posterior distributions in simple statistical models. You’ll be introduced to advanced stochastic simulation methods such as Markov-chain Monte Carlo. You’ll also find out how to fit Bayesian models using high-level software for Bayesian hierarchical modelling such as BUGS or STAN.
An introduction to machine learning methods and modern data-mining techniques, with an emphasis on practical issues and applications. You’ll be introduced to different methods for dimension reduction and clustering (unsupervised learning), a range of classification methods beyond those covered in the Predictive Modelling course. You’ll also learn about neural networks, deep learning, kernel methods, support vector machines and Gaussian processes.
To be accepted to this programme, you must have:
A first degree equivalent to a UK upper second class honours degree, normally with a substantial mathematics component (at least equivalent to Level-1 courses in Mathematics and Level-2 courses in Calculus and Linear Algebra at the University of Glasgow)
Graduates who only have the equivalent of A-level Mathematics can also be admitted to the programme. However, such candidates are required to work through self-study material provided and complete a pre-sessional course in Elementary Mathematics (scheduled in the two weeks preceding the start of the teaching period of semester 1)
Previous study of Statistics or Computing Science is not required
If English is not your first language, the University sets a minimum English Language proficiency level. This is an IELTS overall score of 6.5 with no sub-test less than 6. If you do not have an IELTS test certificate, equivalent scores in other recognised qualifications may be accepted
To apply to this programme:
You must apply online. As part of your online application, you need to submit the following:
- A copy (or copies) of your official degree certificate(s), if you have already completed your degree
- A copy (or copies) of your official academic transcript(s), showing full details of subjects studied and grades/marks obtained
- Official English translations of the certificate(s) and transcript(s)
- One reference letter on headed paper
- Evidence of your English Language ability (if your first language is not English)
- Any additional documents required for this programme (see Entry requirements for this programme)
Please check that you meet the entry criteria for this programme before you apply.
You have 42 days to submit your application once you begin the process. You may save and return to your application as many times as you wish to update information, complete sections or upload supporting documents, such as your final transcript or your language test.
Key Dates UK/EU
- Application Deadline
- Start Date
- 14 Sep 2020
- 28 Sep 2020
- Home/EU: £10,000*
- International: £10,000*
*Total cost, incremental payment schedule available. Fee information is subject to change and is for guidance only.
How much does the programme cost?
Part time fees 1,667 per 20 credits
Can I get help to fund my studies?
You may be eligible for help with the cost of the programme.
What it's like to study online
- 100% online for complete flexibility
- Our part-time online programmes are ideal if you're working full-time or have family commitments.
- Connect to campus from anywhere
- All you need for our online programmes is a device with internet access.
- Gain a global perspective
- As an online student, you'll be part of an international community of academics and learners.
- Learn from the experts
- Our world-class teaching and research staff will help you realise your potential.
- Interact with everyone
- Community building and collaborative learning is a key focus of our online programmes.
- Access a multitude of resources
- Study using a range of materials, including recorded lectures, live seminars, videos, interactive quizzes, journal articles and ebooks.
- Regular Assessments
- During each course you will be assessed in a variety of ways which could include essays, discussions, blogs, online presentations, interactive quizzes, assignments or group work.