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Data Science Master's Degree - MSc/PgD/PgC

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About the Course

Skilled data scientists are in high demand across all sectors. This accredited Master’s degree in Data Science prepares you to meet that demand with a hands-on, professionally relevant education grounded in real-world challenges and cutting-edge research.

Through an interdisciplinary curriculum and project-based learning with real-world datasets and industry-relevant scenarios, you’ll be equipped with in-demand theoretical knowledge and practical skills to develop data science systems, use software to analyse and synthesise data, and manage all aspects of data science.

You’ll graduate job-ready with a diverse skillset of technical expertise, business insight, and ethical awareness.

Accredited by BCS, The Chartered Institute for IT, for the purposes of partially meeting the academic requirement for registration as a Chartered IT Professional.

Accredited by the Institution of Engineering and Technology (IET) on behalf of the Engineering Council as meeting the requirements for Further Learning for registration as a Chartered Engineer. Candidates must hold a CEng accredited BEng/BSc (Hons) undergraduate first degree to fully meet the CEng registration educational requirements.

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BCS - The Chartered Institute for IT

The Institution of Engineering and Technology Accredited Programme Logo

Institution of Engineering and Technology (IET)

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Compulsory modules:

Big Data for Enterprise (20 credits)
This module will introduce big data management techniques through practical based activities associated with real world issues.

Big Data Technologies (20 credits)
This module aims to provide students with theoretical knowledge and practical skills by covering recent trends to store, process and analyse big data in a distributed environment.

Geospatial Analysis (20 credits)
This module aims to provide students with the opportunity to gain experience and develop skills in handling spatial data, and to both identify and understand any patterns revealed within that data through the application of various techniques in geospatial analysis.

Programming for Data Analysis (20 credits)
The aim of this module is to help students to develop an understanding of programming and problem-solving strategies, as well as to develop, analyse and critically assess solutions to real-world data analysis problems.

Research & Professional Practice (20 credits)
The aim of this module is to equip the student with the skills, knowledge and techniques necessary to produce a dissertation with a research or technical focus.

Data Analytics & Visualisation (20 credits)
This module provides students with hands-on experience with public datasets or user-generated data, focusing on historical, current, and predictive data analysis. It covers data scraping, selection, visualisation, and analytics, integrating these with diverse analysis methods.

Technology Dissertation (40 credits)
The aim of the technology project is for the student to apply knowledge, skills and techniques developed during directed and independent study to solve a real world technology related project. The technology project may take the form of an in-depth research project or the development of a computer system.

Information Security (20 credits)
This module aims to provide an insight into the implementation of data security in computer systems and to encourage students to appreciate the practical and theoretical management principles associated with information security.

To obtain an MSc degree, you must follow and successfully complete a total of 180 credits. PgC (60 credits) and PgD (120 credits) may be awarded as standalone or exit awards.

Our academic staff lead engaging lectures, workshops, and lab sessions, fostering a dynamic learning environment that encourages innovation and critical thinking. Your Programme Director provides further pastoral and academic support, guiding students through their studies and signposting to other services such as career development. Additionally, our wider student support teams – including wellbeing services and technical staff – are on hand to offer comprehensive assistance, ensuring students have the resources and support needed to thrive.

Assessments are created within the framework of Cardiff Metropolitan University EDGE (Ethical, Digital, Global and Entrepreneurship) competencies and they take the form of individual or group coursework, research-based assignments, practical assessments, presentations, reports, class tests and a dissertation/development project.

The specialist knowledge you will acquire through the course will place you in a strong position to pursue a wide range of careers involving the analysis of data, including data analyst, data scientist, IT consultant and managerial roles in industry. The programme also gives you a range of computing skills that could be widely applied to any role within the fields of business and computing.

Applicants should meet one of the following:

  • Possess, or expect to obtain, an undergraduate degree or equivalent, in a relevant area e.g., Computing or Information Systems with a minimum of 2:2 classification.
  • Hold a suitable professional qualification from an appropriate professional body.

Relevance shall be determined by the Programme Director with reference to the applicant’s transcript, and, if required, via an interview.

Equivalence shall be determined by:

  • International Admissions Team for applicants from outside the United Kingdom.
  • The Programme Director for applicants who present professional qualifications such as from the BCS. Such an applicant would be interviewed by the programme director to establish suitability.

English Language Requirements:
Applicants whose first language is not English should refer to English Language Requirements to confirm the level and evidence of fluency required for entry to the programme.

Students with extant level 7 qualifications wishing to enter the course may apply on the basis of RPL for admission with Credit. In such cases the regulations detailed in the Academic Handbook will apply and allows for a maximum RPL of 120 credits on a Master’s programme. In this case the remaining 60 credits would consist of the research methods module and the dissertation.

The admissions process is controlled by Cardiff Metropolitan University’s centralised admissions team in consultation with the Programme Director.

All applications from International students will be subject to an initial assessment of academic qualifications, English Language proficiency and overall suitability for the programme by the International Admissions Teams. However, the final decision remains the responsibility of the Programme Director.

How to Apply:
Applications for this course should be made direct to the University via our self-service facility. For further information please visit our How to Apply pages at www.cardiffmet.ac.uk/howtoapply.

International Applicants:

Before making an application, international students (those outside of the EU), should contact the International Office at Cardiff Met to discuss the necessary procedures in relation to studying with us. For further information visit www.cardiffmet.ac.uk/international.

Tuition Fees and Financial Support:

For up to date information on tuition fees and the financial support that may be available. Please refer to www.cardiffmet.ac.uk/fees.

Part-time fees:

Charges are per Single Module unless specified:
Undergraduate = 10 Credits; Postgraduate = 20 Credits

Generally we find most students will complete 60 credits per year for both Undergraduate and Postgraduate study; to obtain a true costing please clarify this by contacting the Programme Director directly.

For general enquiries please contact the Admissions Team on 029 2041 6044 or email askadmissions@cardiffmet.ac.uk.

For course specific enquiries, please contact the Programme Director, Dr Imtiaz Hussain Khan: IHKhan@cardiffmet.ac.uk

  • Location

    Llandaff Campus

  • School

    Cardiff School of Technologies

  • Starting

    September and January intakes available

  • Duration

    3 years part time.
    12-18 months full time, depending on start date.

We endeavour to deliver courses as described and will not normally make changes to courses, such as course title, content, delivery, and teaching provision. However, it may be necessary for the University to make changes in the course provision before or after enrolment. It reserves the right to make variations to content or delivery methods, including discontinuation or merging courses if such action is considered necessary. For the full information, please read our Terms and Conditions.

Clusters of work desks with office chair and computer screens in office room. Clusters of work desks with office chair and computer screens in office room.

Explore Our Facilities

Data Science & AI Lab

Develop practical skills using high-spec machines for complex data visualisation and machine learning.

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Inside the entrance of Cardiff School of Technologies. Inside the entrance of Cardiff School of Technologies.

Explore Our Facilities

Designed with employability in mind, the School of Technologies offers modern facilities which include a range of industry standard labs and equipment that enhance your practical learning.

Our exclusive social and study spaces provide the perfect setting to work, relax, and connect, allowing students to interact and build a strong community.

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Study Data Science

Solve Real Problems with Real Data

From day one, you’ll engage with real-world datasets and industry scenarios that reflect the complexity of working with big data. Your capstone project will challenge you to apply the skills you have learned to solve a challenging problem or contribute to ongoing research in data science.

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A person stands with arms folded in front of a door. Above the door is a sign that reads Data Science and AI Laboratory. A person stands with arms folded in front of a door. Above the door is a sign that reads Data Science and AI Laboratory.

Study Data Science

Specialise in Emerging Technologies

Stay ahead of the curve with modules that explore the latest trends in data science: Big Data Technologies trains you in platforms like Hadoop, Spark, and Flink for distributed data processing. Big Data Enterprise focuses on strategic data use in business, using tools like MongoDB. Geospatial Analysis builds expertise in mapping and interpreting spatial data, relevant in sectors from urban planning to environmental monitoring.

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Study Data Science

Learn from Leading Researchers

Our internationally experienced teaching staff bring their active research into every session. You’ll learn from experts in AI data-driven modelling, data analytics, (deep) machine learning, data visualisation, blockchain, and bioinformatics.

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Centre for Industry 4.0 and Blockchain Research

This degree is closely linked with the Centre for Industry 4.0 and Blockchain Research (CI4BCR) at Cardiff School of Technologies. CI4BCR is undertaking cutting-edge research on blockchain and other distributed technologies, focusing on data integration, data visualisation, Internet of Things (IoT) and more.

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