Data Science and Analytics (MSc)

How will I be taught?

You’ll be located in Abacws, a world-leading facility designed in collaboration with students and academic staff to create interdisciplinary, flexible and creative workspaces, with innovative teaching areas being a key feature.

You’ll learn through a blend of lectures, practical workshops, group activities, and independent study, designed to give you both the theoretical understanding and the hands-on experience needed to apply data science and analytics in practice.

Each module includes interactive sessions, case studies, and practical exercises using widely used industry-standard data science and analytics tools.

Programming skills and the use of relevant software packages will be taught in our dedicated computer suites. We often invite industry experts to give presentations, which our students are welcome to attend.

You’ll also develop your skills through collaborative project work, presentations, and independent research, culminating in a substantial dissertation project supervised by an expert in the field. This typically involves working with an external company or organisation.

Throughout the programme you will be encouraged to apply your learning to real-world data and challenges, ensuring that you graduate with the confidence and ability to use data science and analytics techniques effectively.

How will I be assessed?

The taught modules within the programme are assessed through a wide range of in-course assessments, which may include written reports, practical assignments, oral presentations, exam papers and class tests.

Feedback on coursework may be provided via written comments on work submitted, by provision of ‘model’ answers and/or through discussion in contact sessions.

The individual dissertation project enables you to demonstrate your ability to build upon and exploit knowledge and skills gained in earlier stages of the programme. Furthermore, it provides the opportunity for you to exhibit critical and original thinking based on a period of independent study and learning.

How will I be supported?

We pride ourselves on fostering a supportive and inclusive learning environment in which all students are encouraged to succeed. Each module is led by an experienced academic who provides guidance and feedback throughout the course.

All our students are allocated a personal tutor who is there to support you during your studies and can advise you on academic and personal matters that may be affecting you.

You will have access to the Student Futures team, who support students across the University with careers, placements and global opportunities, including access to a dedicated careers adviser. With a strong focus on employability, you can benefit from expert guidance on career planning, job searching and applications. The team provides opportunities to network with employers and develop your skills through careers fairs and workshops, and supports you in gaining relevant work experience, internships and placements. You can also explore entrepreneurial ambitions with the Enterprise Team, enhance your profile through the Cardiff Award, and access tailored support for international and underrepresented students.

What skills will I practise and develop?

The Learning Outcomes for this Programme describe what you will achieve by the end of your programme at Cardiff University and identify the knowledge and skills that you will develop. They will also help you to understand what is expected of you. 

On successful completion of your Programme you will be able to:

Knowledge & Understanding:

• Critically evaluate current and emerging data science and analytics approaches, synthesising research and practice to justify solutions to complex, real-world problems 
• Explain the theoretical foundations and key models underpinning data science and analytics, identifying their assumptions and limitations 
• Assess the suitability of alternative data science and analytics methods for solving diverse problems, considering performance and interpretability.
• Apply advanced data science and analytics concepts to independently investigate a complex, real-world problem, demonstrating originality and methodological rigour.

Intellectual Skills:

• Analyse complex problems, designing innovative solutions using appropriate data science and analytics techniques 
• Evaluate the robustness of decision-making, applying advanced statistical and operational research reasoning and sensitivity analysis 
• Exercise independent critical judgement in selecting, adapting, and evaluating data science and analytics methodologies for novel or ambiguous problems, integrating evidence to defend methodological choices and conclusions 

Professional Practical Skills:

• Demonstrate advanced proficiency in programming, data extraction, and statistical and operational research modelling, applying these skills to authentic, domain-specific challenges 
• Communicate complex technical concepts, results, and implications clearly and accurately to specialist and non-specialist audiences in a range of formats
• Critically appraise and address ethical and societal challenges in applying data science and analytics methods across industry and public sector contexts 
• Plan, manage, and deliver an extended, research-driven project, demonstrating autonomy, methodological integrity, and effective dissemination of findings to academic and professional audiences 

Transferable/ Key Skills:

• Communicate ideas and evidence effectively through diverse approaches, adapting style for specialist and non-specialist audiences 
• Collaborate effectively in multidisciplinary teams, demonstrating adaptability and professional responsibility 
• Apply logical and analytical reasoning to complex problems.
• Demonstrate autonomy and reflective practice in managing and communicating a substantial independent project 
• Manage data responsibly, ensuring secure storage, ethical use, and critical evaluation of data collection and analysis techniques.
• Locate, critically evaluate, synthesise and correctly acknowledge information from a range of sources.