MSc in Online Data Analytics for Government

WHY THIS PROGRAMME

• 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'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.
• 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.  
• 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.

PROGRAMME STRUCTURE

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 (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 (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 (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 (10 credits)
  The course provides an introduction and overview of survey sampling and design, survey statistics, ethics, types of surveys, data processing and management.
• 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 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.

CAREER PROSPECTS

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.

More Information