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Data Analytics for Government

We are pleased to offer four core modules from the MSc Data Analytics for Government (MDataGov), available to study on a standalone basis for continuing professional development (CPD).

We have developed this programme in conjunction with the Office of National Statistics (ONS).

Modules are only open to UK public sector employees. Please contact us if you are unsure if this applies to you.

These modules are suitable for those working in public sector bodies in the UK who want to upskill or further their career, and who are happy to study alongside full-programme MSc students at the University.

Each module is worth 10 credits; we are working towards a system that will enable you to accumulate credits towards a postgraduate qualification over a period of time, should you wish.

The MSc is also available as one-year full-time programme. UK public sector employees are eligible to study the full MSc at a discounted price of £8,730 (for study during the 2020/21 academic year).

Credits

10 credit module (reference MAT032)

Dates

Autumn semester

Cost

£550 (for the 2020/21 academic year)

Assessment

Written examination 80% (two hours)

Coursework 20% (two pieces of written work)

It will be assumed that you will be taking the assessment for the module.

Outline description

In this module we cover the fundamentals of survey statistics. In particular:

  • standard methods of drawing samples from finite populations
  • how to make inferences about population characteristics
  • survey-based estimation of population totals and related quantities
  • regression estimation for modelling relationships between variables
  • the principles and methods used to compensate for non-response following survey data collection
  • calibration methods for household surveys
  • index numbers.

Students should be comfortable with A-level standard statistics before beginning this module.

Objectives

On successful completion of the module, you should be able to:

  • estimate means, totals, proportions, and ratios of population variables from data collected using standard sampling methods
  • adjust estimates to compensate for the effects of unit non-response
  • use calibration to improve estimates from household surveys
  • assess the suitability of a survey for a given estimation problem.

Skills you will practise and develop

  • Data analytics: the collection of data using surveys and the estimation of population variables.
  • Mathematical reasoning: calculation of probabilities for sampling events.

Syllabus content

  • Estimation for simple random sampling, stratified sampling and cluster sampling
  • Regression and ratio estimation
  • Non-response and imputation of missing values
  • Calibration
  • Index numbers.

Delivery

You will be required to attend a two-hour lecture each week, and there will be another optional one-hour session every fortnight.

Some handouts will be provided in hard copy or via Learning Central, but you will be expected to take notes of lectures.

You are also expected to undertake at least 50 hours private study, including preparation of worked solutions for exercise classes.

Credits

10 credit module (reference CMT314)

Dates

Autumn and spring semesters

Cost

£550 (for the 2020/21 academic year)

Assessment

There will be two points of assessment in this module.

The first will be an individual assessment worth 30% of the module marks, consisting of a set of programming exercises designed to assess competency in basic programming tasks.

The second assessment will be worth 70% of the module marks and will consist of a demonstration of data analysis skills presented within a reflective portfolio, which will discuss critical issues within data science as they relate to the analysis carried out, and reflection on your understanding of the field.

It will be assumed that you will be taking the assessment for the module.

Outline description

  • This module will introduce core data science concepts, including understanding of the different types of data sources available (administrative data, survey data, open data, big data, etc)
  • How to collect data, including innovative data collection methods, e.g. web scraping
  • Understanding the challenges with unstructured data
  • How to treat different data types; how to undertake basic data analysis (structured and unstructured data)
  • How to present data through basic data visualisations.

Objectives

On successful completion of the module, you should be able to:

  • use the Python programming language to complete a range of programming tasks
  • critically analyse and discuss methods of data collection
  • extract textual and numeric data from a range of sources, including online
  • reflect upon the legal, ethical and social issues relating to data science and its applications.

Skills you will practise and develop

  • Fundamental programming in Python
  • Reading and Writing common data formats
  • Data analysis using appropriate libraries.

Syllabus content

  • Basic programming in Python: Fundamental data types, program control structures, basic language features
  • Data extraction and importing; analysis using common libraries (e.g. Pandas, Numpy/Scipy)
  • Natural language processing using common libraries (e.g. NLTK, SpaCY)
  • Retrieving data from online sources (web scraping, APIs)
  • Data Science applications
  • Legal issues relating to data science (GDPR)
  • Social and ethical issues relating to data science.

Delivery

This module will be delivered through a series of online video tutorials and hands-on interactive laboratory sessions, including exercises and coding demonstrations, with supporting in-person lectures, tutorials and supervised lab sessions.

A timetable will be released closer to the time. As a guide, these face-to-face sessions will be held on the same day each week.

Credits

10 credit module (reference CMT315)

Dates

Autumn and spring semesters

Cost

£550 (for the 202/21 academic year)

Assessment

There will be one point of assessment in this module.

This assessment will be worth 100% of the module marks and will consist of a demonstration of data analysis skills presented within a reflective portfolio, which will discuss critical issues within data science as they relate to the analysis carried out, and reflection on your understanding of the field.

It will be assumed that you will be taking the assessment for the module.

Outline description

This will be a practical module, which will consider programming with structured and unstructured data and statistical analysis of this data. You will learn how to analyse both numeric and textual data using a range of computational programming languages.

Objectives

On successful completion of the module you should be able to:

  • use code to extract, store and analyse textual and numeric data
  • carry out data analysis and statistical testing using code
  • critically analyse and discuss methods of data collection, management and storage
  • analyse and visualise textual and numeric data from a range of sources, including online.

Skills you will practise and develop

  • Data analysis using appropriate libraries.

Syllabus content

  • Basic programming in Python: Fundamental data types, program control structures, Object Oriented Programming and other basic language features
  • Data extraction and importing; analysis using common libraries (e.g. Pandas, Numpy/Scipy)
  • Descriptive statistics
  • Hypothesis testing
  • Natural language processing using common libraries (e.g. NLTK, SpaCY)
  • Retrieving data from online sources (web scraping, APIs).

Delivery

This module will be delivered through a series of online video tutorials and hands-on interactive laboratory sessions including exercises and coding demonstrations, with supporting in-person lectures, tutorials and supervised lab sessions.

A timetable will be released closer to the time. As a guide, these face-to-face sessions will be held on the same day each week.

Credits

10 credit module (reference to be confirmed)

Dates

Spring semester

Cost

£550 (for the 2020/21 academic year)

Assessment

This module will be assessed through one 1.5 hour examination during which students will answer two exam questions. It will be assumed that you will be taking the assessment for the module.

Outline description

This module provides an overview of issues and ideas concerning the scope and organisation of official statistics, as well as its processes and products.

The module provides a general foundation for the more detailed study of these elements and identifies links with other relevant disciplines.

Objectives

On successful completion of the module, you should be able to:

  • critically evaluate legal and administrative frameworks of UK statistics
  • Show clear understanding of quality control, dissemination and ethical issues relevant to the production and management of official statistics.

Skills you will practise and develop

You will have a broad overview of the fundamental issues underlying the organisation of official statistics and be able to apply this knowledge in discussing the relative merits of alternative approaches.

Syllabus content

  • Overview of the importance of statistics, policy and administrative uses
  • History of the development of official statistics in the UK
  • UK fundamental principles: EU law for statistics
  • UK Framework for National Statistics: roles and responsibilities; Nature of National Statistics Acts; The UK Statistics and Registration Service Act; The benefits/dis-benefits of a UK Statistics Act
  • Code of practice: Contents and intentions
  • Models for quality, quality framework and public trust in official statistics
  • Scientific principles for official statistics
  • Role of organisations, standards and peer review
  • Organisational issues: Centralisation, decentralisation, devolution; management of National Statistical Offices: professional Issues, ethics, professional bodies.

You will need a good understanding of all the above but will also be able to take any of the themes further through additional reading.

Delivery

The module will be delivered in four 6-hour days spread across one to two weeks. Teaching will be delivered through interactive lectures and seminars.

A timetable will be released closer to the time.

Entry requirements

You must be a UK public sector employee.

You must have a 1st or upper 2nd class UK honours degree or equivalent in a numerate subject, such as mathematics, operational research, statistics, computer science, management science, economics, engineering or a suitable science degree, or equivalent professional experience.

If you are applying solely on the basis of your professional experience then you must have been working in a relevant role for at least a year. If you are unsure as to whether your qualifications or professional experience are relevant then please contact admissions@cardiff.ac.uk.

Applicants whose first language is not English must meet our English Language requirements.

You must provide us with certificates and transcripts relating to previous qualifications (where relevant), a personal statement, and (where applicable) proof of your English language proficiency.

How to apply

Please use the online application form to apply for one or more modules.

We have provided some Guidance Notes to help clarify and simply your application experience.

Applications received before Friday 4 September 2020 will be considered for the 2020-21 intake. Applications received after this date will be considered for the 2021-22 intake.

If your organisation is funding the study of your module(s) then you will need to confirm this by uploading a simple sponsorship letter which clearly states the total amount (£) your employer/department will pay towards your studies, so please organise this before you apply.

It will be assumed that you will be taking the assessment for each module. We are currently working on a process for those who do not want to wish to take the assessment, and we will provide further details during induction.

Contact us

Please contact the Admissions team for further guidance on the application process:

Student enquiries