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Data Analytics at Oxford Brookes University - UCAS

Course options

There are other course options available which may have a different vacancy status or entry requirements – view the full list of options

Course summary

With our MSc in Data Analytics you will learn fundamental theory and practice mathematical and statistical modelling. With special reference to data analysis and visualisation. With recent developments in digital technology, society has entered the era of 'big data'. The UK Government recognises big data as one of the eight great technologies. It has priorities for funding and research and will have a pivotal role in rebuilding and strengthening the economy. The explosion and wealth of available data in a wide range of application domains gives rise to new challenges and opportunities in all areas. One major challenge is how to take advantage of the unprecedented scale of data. And how to gain further insights and knowledge to improve the quality of offered products and services. We designed the MSc in Data Analytics for those currently in employment. And to run alongside the MSc in Data Analytics for Government. It is available to all students, and is not exclusive to any particular employment sector.

Modules

Research and Study Methods (10 credits) This module will equip you with the skills necessary to perform research and employ effective study methods which will underpin your dissertation. Data Science Foundations (10 credits) This module presents an overview of core data science concepts and tools, focusing on real-life data science research questions with practical exposure to R and/ or Python programming as an integral part of the course. Survey Fundamentals (10 credits) This module provides an overview of sampling and estimation fundamentals. Statistical Programming (10 credits) This module introduces core programming techniques in R essential for performing data manipulation, data processing and data analyses of traditional and alternative data sources through practical sessions. Introduction to Survey Research (10 credits) This module introduces the stages involved with planning and undertaking surveys. It will consider the methodological issues that may arise, including errors, and will discuss options for minimising the impact through the survey design. Regression Modelling (10 credits) This module will introduce the basic regression model - residual analysis, model building and selection, and the handling of categorical variables. Also, Logistic regression (binary response regression) will be introduced, assessing the model fit and model building and selection. Finally, Multiple regression and Multivariate regression modelling will be introduced. Advanced Statistical Modelling (10 credits) This module introduces a broad class of linear and nonlinear statistical models and the principles of likelihood inference to a variety of commonly encountered data analysis problems in variety of disciplines. Time Series Analysis (10 credits) This module introduces you to time series and forecasting methods. Introduction to Machine Learning (10 credits) This module provides you with the principles of computer learning and its applications. It covers the fundamentals of machine learning methodologies, implementations and analysis methods appropriate for machine learning applications. Advanced Machine Learning (10 credits) This module builds on the Intro to Machine Learning module. It focuses on Advanced Programming Skills and Neural Computing as an extension of machine learning, natural language processing & multi-media. It considers supervised and unsupervised machine learning algorithms (random forests, neural networks, clustering, Log regression, and support vector machines) alongside more advanced Imaging and multi-media data processing. Introduction to Distributed Systems (10 credits) This module provides an overview of processing data at large scale and parallel processing. It introduces Hadoop and Spark and the use of parallel processing paradigms. Data Visualisation (10 credits) This module builds on the basic data visualisations introduced in the compulsory modules. It will cover information design, interaction design and user engagement; state of the art tools to build useful visualisations for different types of data sets and application scenarios. Dissertation in Data Analytics (60 credits) Students on the MSc are also required to pass complete a dissertation on a data science focussed topic related to their programme of study. The exact content of each dissertation will vary in accordance to the title but will involve you completing a literature review and research of the topic at an advanced level, the preparation of a project proposal, the application of analytical techniques and academic approaches to the generation of alternative solutions and synthesis of a solution for the complex problem in hand, together with the presentation of the solution in oral and written form.

Assessment method

We have designed the assessments on this course to develop your technical skills. This is led by the underlying theory and requirements of the industry. Assessment is 100% coursework and covers a range of activities including:

  • reports
  • data analysis
  • programming
  • presentations.
We encourage you to relate the assessment tasks with professional activities. And to relate your achievements with professional standards. You will have the opportunity to work independently and in groups. Where appropriate, we use self and peer assessment to encourage you to get involved in your own professional development.


How to apply

International applicants

If your first language is not English you will require a minimum IELTS score of 6.0 overall with 6.0 in all components. Please also see the University's standard English language requirements.

Entry requirements

To join this course you'll need a 2:2 bachelor's degree in the physical or social sciences where you have developed analytical knowledge and understanding in mathematical sciences. Typically this includes applicants with knowledge and familiarity with basic computing, mathematics and statistics concepts and methods at bachelor's degree level. Applicants with other qualifications, plus work experience from other fields, who have quantitative skills and familiarity with data analysis and modelling ideas will also be considered. Please also see the University's general entry requirements.


English language requirements

IELTS 6.0 overall with at least 6.0 in each component

If English is not your first language then, please see here for our requirements and accepted alternative English language qualifications

https://www.brookes.ac.uk/international/applying-to-arriving/how-to-apply/english-language-requirements/


Fees and funding

Tuition fees

England £1190 Module
Northern Ireland £1190 Module
Scotland £1190 Module
Wales £1190 Module
EU £18050 Year 1
International £18050 Year 1

Tuition fee status depends on a number of criteria and varies according to where in the UK you will study. For further guidance on the criteria for home or overseas tuition fees, please refer to the UKCISA website .

Additional fee information

Fees quoted are for the first year only. If you are studying a course that lasts longer than one year, your fees will increase each year.
Data Analytics at Oxford Brookes University - UCAS