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University of Glasgow: Data Analytics for Government
|Institution||University of Glasgow View institution profile|
|Department||School of Mathematics & Statistics|
While the amount of data being produced is proliferating at a staggering rate, the skills to extract information and the value we receive from it are both relatively scarce. Analytics and data-driven decisioning is playing an increasingly important role in the economy, society and public administration. Designed together with the Office for National Statistics, our MSc in Online Data Analytics for Government is based on our successful online MSc programme in Data Analytics and will provide you with vital skills required to develop your modelling and data handling expertise. You will gain a firm grounding in the principles of learning from data sets, whilst at the same time getting hands-on experience handling, analysing and visualising data, helping your organisation to maximise the insights and value extracted from its data.
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.
A faster study route, which lets you complete the programme in two years, and a slower study route, which lets you complete the programme in four years, is also available.
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. A faster study route, which lets you complete the programme in two years, and a slower study route, which lets you complete the programme in four years, is also available. * Designed for part-time study, this programme allows you to gain an MSc degree from a leading university while you're 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.
An information session for prospective students took place recently, and can be viewed on YouTube.
This flexible part-time programme is usually completed over three years. In the first two years, you will take two courses each trimester. If you are doing the MSc, in the third year you will undertake a project and dissertation.
Core courses (MSc / PgDip)
- SAMPLING FUNDAMENTALS (ODL) (10 credits) This course introduces students to key concepts from probability theory and provides an introduction to survey sampling with a focus on the underpinning probabilistic mechanisms.
- STATISTICAL COMPUTING (ODL) (10 credits) 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.
- DATA SCIENCE FOUNDATIONS (ODL) (10 credits) 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.
- PREDICTIVE MODELLING (ODL) (10 credits) 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.
- ADVANCED PREDICTIVE MODELS (ODL) (10 credits)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.
- STATISTICS IN GOVERNMENT (ODL) (10 credits)The course provides an overview of issues, legal frameworks, ethical considerations, and dissemination practices of official statistics from the UK, EU, and relevant international governments, institutions, and organisations.
- DATA PROGRAMMING IN PYTHON (ODL) (10 credits) 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.
- UNCERTAINTY ASSESSMENT AND BAYESIAN COMPUTATION (ODL) (10 credits) 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.
- INTRODUCTION TO SURVEY RESEARCH (10 credits) The course, beginning January 2022, provides an introduction and overview of survey sampling and design, survey statistics, ethics, types of surveys, data processing and management.
- DATA MINING AND MACHINE LEARNING I: SUPERVISED AND UNSUPERVISED LEARNING (ODL) (10 credits) 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 and support vector machines.
- DATA MINING AND MACHINE LEARNING II: BIG DATA AND UNSTRUCTURED DATA (ODL) (10 credits) 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.
- LARGE-SCALE COMPUTING FOR DATA ANALYTICS (ODL) (10 credits) The course introduces students to deep learning and convolutional neural networks and presents an overview over systems for large-scale computing and big data.
- DATA ANALYTICS PROJECT (ODL) (60 credits) (MSc only) At the end of the programme you will complete a project, giving you the opportunity to put the skills you have acquired throughout the programme into practice. During the project you will solve a real-world data analytics problem using state-of-the-art data science methods.
Students studying for the PgCert only will take the following courses: (see above for course descriptions)
- SAMPLING FUNDAMENTALS (ODL)
- STATISTICAL COMPUTING (ODL)
- DATA SCIENCE FOUNDATIONS (ODL)
- PREDICTIVE MODELLING (ODL)
- STATISTICS IN GOVERNMENT (ODL)
- ADVANCED PREDICTIVE MODELS (ODL)
- DATA MINING AND MACHINE LEARNING I: SUPERVISED AND UNSUPERVISED LEARNING (ODL)
- DATA MINING AND MACHINE LEARNING II: BIG DATA AND UNSTRUCTURED DATA (ODL)
As part of the framework agreement with the Office of National Statistics, learners sponsored by UK public sector bodies can also enrol for individual courses.
The programme equips you with key data and analytical skills required to master the challenges and make the most of the opportunities in today's technology-driven world. The programme is specifically designed for employees in the public sector and will help you accelerate your career and progress to senior roles in data science and analytics.
The University of Glasgow is one of the UK’s most prestigious seats of learning, and the fourth oldest university in the English speaking world. Established in 1451 and recognised for its world-changing research and teaching, the University has inspired thinkers from eminent scientist Lord Kelvin and the father of economics Adam Smith, to Scotland’s First Minister Nicola Sturgeon.
- is ranked 81st in the world in the QS World University Rankings 2023
- is in the top 100 in the world: Times Higher World University Rankings 2023
- 95.9% of students in employment or further study 6 months after graduation …
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