Postgraduate taught 

Data Analytics for Government MSc/PgDip/PgCert: Online distance learning

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.

  • MSc: usually 36 months part‑time
  • PgDip: 24 months part‑time
  • PgCert: 12 months part‑time

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 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 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.

PgCert

Students studying for the PgCert only will take the following courses: (see above for course descriptions)

Core courses

Optional courses

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.

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.

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.

Fees & funding

Tuition fees for 2021-22

MSc

UK / EU / International

  • £15,000 (total cost, incremental payment schedule available)
  • Part-time fees: £1,667 per 20 credits

PgDip

UK / EU / International

  • £10,000 (total cost, incremental payment schedule available)
  • Part-time fees: £1,667 per 20 credits

PgCert

UK / EU / International

  • £5,000 (total cost, incremental payment schedule available)
  • Part-time fees: £1,667 per 20 credits

Additional fees

  • Fee for re-assessment of a dissertation (PGT programme): £370
  • Submission of thesis after deadline lapsed: £350
  • Registration/exam only fee: £170

Fee information is subject to change and is for guidance only

Bespoke pricing arrangements apply to students sponsored by UK public sector bodies as part of the framework agreement with the Office for National Statistics.

Funding opportunities

Alumni Discount

In response to the current unprecedented economic climate, the University is offering a 20% discount on all Postgraduate Research and full Postgraduate Taught Masters programmes to its alumni, commencing study in Academic session 2020/21. This includes University of Glasgow graduates and those who have completed a Study Abroad programme or the Erasmus Programme at the University of Glasgow. The discount applies to all full-time, part-time and online programmes. This discount can be awarded alongside other University scholarships.

The scholarships above are specific to this programme. For more funding opportunities search the scholarships database

Entry requirements

To be accepted for this programme, you must have:

  1. 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 A-Level or Higher Mathematics, or equivalent, may also be admitted to the programme, however only subject to successfully completing an assessment of their mathematical skills before being admitted to the programme. Training material which prepares students for the assessment will be made available to applicants.

  2. Graduates who achieved a degree classification equivalent to a UK lower second class honours degree, or similar, but who have substantial experience in a profession which involved a significant amount of programming, data management, data analysis or mathematical modelling might be also admitted to be programme. Such applicants might also be required to successfully complete an interview (as well as successfully completing the assessment of their mathematical skills, if required).

Previous study of Statistics or Computing Science is not required.

English language requirements

Important information for entry in Autumn 2020 and January 2021

For applicants whose first language is not English, the University sets a minimum English Language proficiency level.

International English Language Testing System (IELTS) Academic module (not General Training)

  • overall score 6.5
  • no sub-test less than 6.0
  • or equivalent scores in another recognised qualification:

Common equivalent English language qualifications

All stated English tests are acceptable for admission for both home/EU and international students for this programme:

  • ibTOEFL: 90; no sub-test less than:
    • Reading: 20
    • Listening: 19
    • Speaking: 19
    • Writing: 23
  • CAE (Cambridge Certificate of Advanced English): 176 overall; no sub-test less than 169
  • CPE (Cambridge Certificate of Proficiency in English):  176 overall; no sub-test less than 169
  • PTE Academic (Pearson Test of English, Academic test): 60; no sub-test less than 59
  • Trinity College London Integrated Skills in English: ISEII at Distinction with Distinction in all sub-tests

For international students, the Home Office has confirmed that the University can choose to use these tests to make its own assessment of English language ability for visa applications to degree level programmes. The University is also able to accept an IELTS test (Academic module) from any of the 1000 IELTS test centres from around the world and we do not require a specific UKVI IELTS test for degree level programmes. We therefore still accept any of the English tests listed for admission to this programme.

Pre-sessional courses

The University of Glasgow accepts evidence of the required language level from the English for Academic Study Unit Pre-sessional courses. We also consider other BALEAP accredited pre-sessional courses:

FAQs

What do I do if...

my language qualifications are below the requirements?

The University's English for Academic Study Unit offers a range of Pre-Sessional Courses to bring you up to entry level. The course is accredited by BALEAP, the UK professional association for academic English teaching; see Links.

my language qualifications are not listed here?

Please contact the Recruitment and International Office using our enquiry form


 

For further information about English language requirements, please contact the Recruitment and International Office using our enquiry form

How to apply

To apply for a postgraduate taught degree you must apply online. We cannot accept applications any other way.

Please check you meet the Entry requirements for this programme before you begin your application.

Documents

As part of your online application, you also need to submit the following supporting documents:

  • 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)
  • A copy of the photo page of your passport (Non-EU students only)

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.

For more information about submitting documents or other topics related to applying to a postgraduate taught programme, check Frequently Asked Questions

Guidance notes for using the online application

These notes are intended to help you complete the online application form accurately; they are also available within the help section of the online application form. 

If you experience any difficulties accessing the online application then you should visit the Application Troubleshooting/FAQs page.

  • Name and Date of birth: must appear exactly as they do on your passport. Please take time to check the spelling and lay-out.
  • Contact Details: Correspondence address. All contact relevant to your application will be sent to this address including the offer letter(s). If your address changes, please contact us as soon as possible.
  • Choice of course: Please select carefully the course you want to study. As your application will be sent to the admissions committee for each course you select it is important to consider at this stage why you are interested in the course and that it is reflected in your application.
  • Proposed date of entry: Please state your preferred start date including the month and the year. Taught masters degrees tend to begin in September. Research degrees may start in any month.
  • Education and Qualifications: Please complete this section as fully as possible indicating any relevant Higher Education qualifications starting with the most recent. Complete the name of the Institution (s) as it appears on the degree certificate or transcript.
  • English Language Proficiency: Please state the date of any English language test taken (or to be taken) and the award date (or expected award date if known).
  • Employment and Experience: Please complete this section as fully as possible with all employments relevant to your course. Additional details may be attached in your personal statement/proposal where appropriate.

Reference: Please provide one reference. This should typically be an academic reference but in cases where this is not possible then a reference from a current employer may be accepted instead. Certain programmes, such as the MBA programme, may also accept an employer reference. If you already have a copy of a reference on letter headed paper then please upload this to your application. If you do not already have a reference to upload then please enter your referee’s name and contact details on the online application and we will contact your referee directly.

Application deadlines

Please note that applications for programmes delivered online will be accepted up until they commence in September 2020.

Apply now