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Data Analytics MSc (Distance Learning)

2024-25 (also available for 2025-26)

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This course is eligible for Master's loan funding. Find out more.

Start date

16 September 2024

18 November 2024

17 February 2025

19 May 2025

Duration

2-3 years part-time

Places available (subject to change)

30

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About the course

Reasons to study

  1. Knowledge – Our course is aligned with SAS and will equip you with the ability to evaluate existing and emerging data science technology and apply your knowledge to big data problems.
  2. Professional Links - This course is accredited by the British Computer Society (BCS), the Chartered Institute for the IT industry. This gives you a potential advantage when looking for a job as some employers may ask for graduates with accredited degrees.
  3. Convenience – This course is delivered as a Distance Learning programme, with access to materials readily available online. If you wish to be eligible for a Government student loan you can even undertake this course part-time over a duration of two years.

Does working with big data excite you? Aimed at people who aren’t local to our campus or who are in employment, the Data Analytics MSc at The University of Huddersfield will provide you with the learning experience to get you ready for a satisfying career in data science.

With an option for distance learning, this course meets the demand for experts with advanced skills and knowledge in the following:

  • Statistics
  • Data mining techniques
  • Big data and associated file systems
  • Complex data visualisation

You’ll finish our Data Analytics course with a deeper understanding of how to analyse and visualise complex datasets, evaluate existing and emerging data science technology and provide novel data solutions to stakeholders to improve their decision-making process.

Why study Data Analytics MSc (Distance Learning) at Huddersfield?

Our hands-on Data Analytics MSc (Distance Learning) is designed to meet the demand for a new kind of IT specialist with skills and knowledge in data science. The course provides students the opportunity for professional development and valuable practice.

Our course is SAS certified. The SAS Institute is a multinational enterprise providing analytical solutions, platform and software, and training courses to high profile companies, and to academia. You will follow SAS training as part of your course, which puts you on the path to apply for SAS Certification and also allows you to obtain SAS Digital Badges that you can use in your CV or your online profiles to prove your skills to employers. Our partnership with SAS also gives you access to relevant job opportunities through portals like Handshake UK.

This course is also fully accredited by the British Computer Society (BCS), the Chartered Institute for IT and by completing it, you will have partially fulfilled the academic requirements for registration as a Chartered IT Professional.

Discover more about Distance Learning at Huddersfield.

Course detail

Effective Research and Professional Practice

This module aims to provide you with skills that are key to helping you become a successful computing researcher or practitioner. You will get the opportunity to study topics including the nature of research, the scientific method, research methods, literature review and referencing. The module aims to cover the structure of research papers and project reports, reviewing research papers, ethical issues (including plagiarism), defining projects, project management, writing project proposals and making presentations.

Data Analysis and Statistics

Statistical methodology and statistical practice are very central for data analysis. Statistical methods and statistical implementation are also complementary to machine learning and data mining, covering supervised and unsupervised methods. In this module you will be exposed to current core research topics in data mining, machine learning, and interdisciplinary research in which data analysis plays an essential role. You will explore real world applications in business, e.g. customer analytics, credit scoring, financial forecasting), in health and medical research (e.g. automatic diagnosing, genetic data mining and bioinformatics), and in structured and unstructured data analysis.

Data Visualisation

With ever-increasing advancements in Internet-of-Things, Cyber-Physical Systems, and social media applications, huge volumes of complex and multi-dimensional datasets are being generated every day. Visually analysing these datasets facilitates the transformation of raw data into valuable knowledge and information. The biggest challenge is to articulate suitable solutions of complex analytical problems by visually interacting with the designed artefacts without going into underlying complexities. Tremendous endeavours have been devoted to streamline innovative solutions, novel methods, tools, processes and methodologies to address underlying challenges. This module aims to provide you with core knowledge and deep understanding of advanced theories underpinning data visualisation, best practices in using visualisation artefacts effectively and practical skills in implementing the theoretical knowledge into certain application domains. You will be engaged in practical utilisation of state-of-the-art visualisation tools and methods to understand real-world big data problems, and to rectify complex issues with visual analysis. Topics that will be covered in this module include exploratory data visualisation; data visualisation theories, existing and emerging interactive 2D and 3D visualisation toolkits, and application of visualisation skillset in application specific domains.

Databases for Large Data-sets

The data needs of modern enterprises and organisations require a more flexible approach to data management than that offered by traditional relational database management systems. With organizations increasingly looking to Big Data to provide valuable business insights, it has become clear that new approaches are required to handle these new data requirements. Primarily focusing on non-relational data models, this module introduces you to alternative approaches to modelling the data needs of an organization. It also provides you with an opportunity to use non-relational databases and database technologies to build robust and effective organizational information systems.

Data Mining

Data mining is a collection of tools, methods and statistical techniques for exploring and extracting meaningful information from large data sets. It is a rapidly growing field due to the increasing quantity of data gathered by organisations. There is a potential high value in discovering the patterns contained within such data collections. In this module you will look at different data mining techniques and use appropriate data-mining tools in order to evaluate the quality of the discovered knowledge. You will study approaches to preparing data for exploration, supervised and un-supervised approaches to data mining, exploring unstructured data and the social impact of data mining. You will be expected to develop your knowledge such that you are able to contribute to discussions around current application areas and research topics and to increase your background knowledge and understanding of issues and developments associated with data mining.

Big Data Analytics

The ever-increasing advancements in sensing technologies, network infrastructure, storage and social media have enabled us to acquire an unprecedented volume of data at an explosive rate. As a result, the ability to efficiently and accurately derive human-understandable knowledge from these datasets has become increasingly critical to our digitally-driven society and economy. Under this Big Data phenomenon, tremendous endeavours have been devoted to tackle its underlying challenges through both novel solutions and the evolution of existing methodology. The module aims to provide you with the knowledge and critical understanding of contemporary challenges posed by the big data. The topics covered here include the fundamental characteristics and operations associated with big data; existing and emerging architectures and processing techniques; domain applications of big data in practice. Through this module, you will develop an informed understanding of the principles and practice of big data analytics in both general and application specific contexts.

Machine Learning

Machine Learning techniques are now used widely in a range of applications either stand-alone or integrated with other AI techniques. The Machine Learning module allows you to obtain a fundamental understanding of the subject as a whole: how to embody machines with the ability to learn how to recognise, classify, decide, plan, revise, optimise etc. You will learn which machine learning techniques are appropriate for which learning problem, and what the advantages and disadvantages are for a range of ML techniques. We will consider the widely known data-driven approaches, and specific techniques such as “deep learning”, and investigate the typical applications and potential limitations of these approaches. We will introduce available tools and use them in practical classes, evaluating learning bias and characteristics of training sets. High profile applications of data driven, stand-alone, ML systems will be investigated, such as the AlphaGo method. Where data is sparse, and knowledge is already present in a system, we will investigate methods to improve heuristics of existing AI systems, and to learn or revise domain knowledge. This is essentially the area of model-driven ML, where is often integrated to other reasoning systems.

Case Studies in Data Analytics and Artificial Intelligence

The purpose of this module is to enable you to appreciate the historical, current and future application areas of Artificial Intelligence and Data Analytics in relation to both theoretical and practical aspects and to investigate at least one application area in depth. Case studies discussed in the sessions will provide an exploration of applications in a variety of different areas and will be achieved by combinations of study of current research papers, tutors’ own research and the investigative work of the students within the module.

Individual Project

This module enables you to work independently on a project related to a self-selected problem. A key feature in this final stage of the course is that you will be encouraged to undertake an in-company project with an external Client. Where appropriate, however, the Project may be undertaken with an internal Client - research-active staff - on larger research and knowledge transfer projects. The Project is intended to be integrative, a culmination of knowledge, skills, competencies and experiences acquired in other modules, coupled with further development of these assets. In the case where an external client is involved, both the Client and Student will be required to sign a learning agreement that clearly outlines scope, responsibilities and ownership of the project and its products or other deliverables. The Project will be student-driven, with the clear onus on you to negotiate agreement, and communicate effectively, with all parties involved at each stage of the Project.

Entry requirements

Entry requirements for this course are normally:

  • A BSc, BEng or BA Honours degree (2:2 or above) or equivalent professional qualification in any subject.
  • Applicants with other appropriate professional qualifications and/or experience will be considered on an individual basis.

If your first language is not English, you will need to meet the minimum requirements of an English Language qualification. The minimum for IELTS is 6.0 overall with no element lower than 5.5, or equivalent. Read more about the University’s entry requirements for students outside of the UK on our Where are you from information pages.

Enhance your career

We would expect to see graduates progress to careers in all sectors, such as finance, consulting, manufacturing, pharmaceuticals, government and education. Typical job roles would include, business intelligence, data assurance, quality, finance and sales. You could also go on to further study and the University has many options available for postgraduate research which may interest you.*

* Source: LinkedIn
** Percentage of the University’s postgraduate students go on to work and/or further study within fifteen months of graduating. (HESA Graduate Outcomes 2021/22, UK domiciled, other activities excluded).

97%** Graduates employed

Student support

At the University of Huddersfield, you will find support networks and services to help you get ahead in your studies. Our Distance Learning Unit Team are at hand to make your online learning journey a positive, rewarding and successful one.

The Distance Learning Unit has specialist staff who are committed to ensuring that online teaching and learning material is accessible to all. We can recommend and provide training on assistive technologies and software which can support a range of learning styles and additional needs.

During each module, you will be able to rely on your module tutors to provide any academic support you need.

You will always have access to our online learning facilities and should you need any technical support whilst studying, you will find that many of our student support services and resources are available online or accessible during UK working hours.

Find out more about support services including finance, careers, Library, IT and disability and wellbeing.

Important information

Although we always try and ensure we deliver our courses as described, sometimes we may have to make changes for the following reasons

When you enrol as a student of the University, your study and time with us will be governed by our terms and conditions, Handbook of Regulations and associated policies. It is important that you familiarise yourself with these as you will be asked to agree to them when you join us as a student. You will find a guide to the key terms here, along with the Student Protection Plan.

Although we always try and ensure we deliver our courses as described, sometimes we may have to make changes for the following reasons

Changes to a course you have applied for but are not yet enrolled on

If we propose to make a major change to a course that you are holding an offer for, then we will tell you as soon as possible so that you can decide whether to withdraw your application prior to enrolment. We may occasionally have to withdraw a course you have applied for or combine your programme with another programme if we consider this reasonably necessary to ensure a good student experience, for example if there are not enough applicants. Where this is the case we will notify you as soon as reasonably possible and we will discuss with you other suitable courses we can transfer your application to. If you do not wish to transfer to another course with us, you may cancel your application and we will refund you any deposits or fees you have paid to us.

Changes to your course after you enrol as a student

Changes to option modules:

Where your course allows you to choose modules from a range of options, we will review these each year and change them to reflect the expertise of our staff, current trends in research and as a result of student feedback or demand for certain modules. We will always ensure that you have an equivalent range of options to that advertised for the course. We will let you know in good time the options available for you to choose for the following year.

Major changes:

We will only make major changes to non-optional modules on a course if it is necessary for us to do so and provided such changes are reasonable. A major change is a change that substantially changes the outcomes, or a significant part of your course, such as the nature of the award or a substantial change to module content, teaching days (part time provision), type of delivery or assessment of the core curriculum. For example, it may be necessary to make a major change to reflect changes in the law or the requirements of the University’s regulators or a commissioning or accrediting body. We may also make changes to improve the course in response to student, examiners’ or other course evaluators’ feedback or to ensure you are being taught current best practice. Major changes may also be necessary because of circumstances outside our reasonable control, such as a key member of staff leaving the University or being unable to teach, where they have a particular specialism that can’t be adequately covered by other members of staff; or due to damage or interruption to buildings, facilities or equipment, or pandemics.

Major changes would usually be made with effect from the next academic year, but may happen sooner in an emergency. We will notify you as soon as possible should we need to make a major change and will carry out suitable consultation. If you reasonably believe that the proposed change will cause you detriment or hardship we will, if appropriate, work with you to try to reduce the adverse effect on you or find an appropriate solution. Where an appropriate solution cannot be found and you contact us in writing before the change takes effect you can cancel your registration and withdraw from the University without liability to the University for future tuition fees. We will provide reasonable support to assist you with transferring to another university if you wish to do so.

In exceptional circumstances, we may, for reasons outside of our control, be forced to discontinue or suspend your course. Where this is the case, a formal exit strategy will be followed in accordance with the student protection plan.

The Office for Students (OfS) is the principal regulator for the University.

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