Data Science (Conversion) MSc - 2026/7
Awarding body
University of Surrey
Teaching institute
University of Surrey
Framework
FHEQ Level 7
Final award and programme/pathway title
MSc Data Science (Conversion)
Subsidiary award(s)
| Award | Title |
|---|---|
| PGDip | Data Science (Conversion) |
| PGCert | Data Science (Conversion) |
Modes of study
| Route code | Credits and ECTS Credits | |
| Full-time | PCL61006 | 180 credits and 90 ECTS credits |
| Part-time | PCL61007 | 180 credits and 90 ECTS credits |
QAA Subject benchmark statement (if applicable)
Other internal and / or external reference points
N/A
Faculty and Department / School
Faculty of Engineering and Physical Sciences - Computer Science and Electronic Eng
Programme Leader
CIROVIC Mariam (CS & EE)
Date of production/revision of spec
02/03/2026
Educational aims of the programme
- To equip graduates from non-computing backgrounds with the core knowledge and practical skills in data science, including programming, statistical analysis, data management, and machine learning, to prepare them for a range of careers in data science, analytics and AI.
- To develop students' ability to critically evaluate, select, and apply appropriate tools, technologies, and methods for data analysis and interpretation from diverse data sources, including structured, unstructured, and real-time data and alongside scientific literature and domain knowledge.
- To create an intellectually stimulating and practically relevant learning environment, where students can develop a broad, systematic, and critical understanding of data science theory and applications, and apply this knowledge to address complex, real-world problems across multiple domains.
- To provide access to comprehensive digital resources, professional development opportunities, and academic support, enabling students to develop key digital capabilities, employability skills, and a range of transferable competencies including critical thinking, collaboration and teamwork, and communication with diverse stakeholders.
- To stimulate the development of advanced technical, investigative, and research skills while embedding considerations of ethics, fairness, bias, privacy, security, and legal frameworks throughout the project and data lifecycle.
- To foster globally competent, culturally aware, and resilient graduates, who can work effectively in multidisciplinary, cross-cultural, and rapidly evolving professional environments, while recognising the societal impact of data-driven technologies and promoting sustainable and ethical practices.
- To encourage innovation and curiosity in exploring emerging trends in data science and AI, preparing students to adapt to a rapidly evolving technological landscape.
Programme learning outcomes
| Attributes Developed | Awards | Ref. | |
| Apply a comprehensive knowledge of mathematics, statistics, programming, and research methods to solve complex problems in data science. | KC | PGCert, PGDip, MSc | |
| Formulate and analyse complex data science problems, identifying patterns, trends, and relationships within structured, unstructured and real time datasets to reach substantiated conclusions. | KC | PGDip, MSc | |
| Select and apply appropriate computational, statistical, and machine learning techniques to model and solve real-world data science problems, discussing the limitations of the techniques employed. | KCT | PGDip, MSc | |
| Design and implement scalable data-driven solutions that integrate cloud computing, database systems, and business intelligence tools, ensuring efficiency and reliability in data processing and storage. | CP | PGDip, MSc | |
| Critically evaluate technical literature, academic research, and industry reports to inform data science methodologies and decision-making processes. | CT | PGDip, MSc | |
| Develop and deploy machine learning models, including deep learning architectures, and assess their performance in diverse applications such as natural language processing, predictive analytics, and data visualization. | KCP | PGDip, MSc | |
| Evaluate the societal, ethical, and environmental impact of data science solutions, ensuring responsible and secure use of data and AI technologies. | KCT | PGDip, MSc | |
| Communicate effectively complex data science concepts, results, and recommendations to both technical and non-technical audiences using data storytelling, visualization, and reporting techniques. | PT | MSc | |
| Demonstrate proficiency in using industry-standard programming languages, data analysis tools, and cloud-based platforms to extract, transform, analyse, and visualize data. | KCP | PGCert, MSc | |
| Develop innovative solutions that address real-world challenges across various domains, including business, healthcare, finance, and technology, while considering societal and industry needs. | KCP | PGCert, MSc | |
| Enhance employability skills through independent learning, team-work, and research-driven dissertation projects that showcase expertise in specialized areas of data science. | KCT | PGCert, MSc |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Programme structure
Full-time
This Master's Degree programme is studied full-time over one academic year, consisting of 180 credits at FHEQ level 7. All modules are semester based and worth 15 credits with the exception of project, practice based and dissertation modules.
Possible exit awards include:
- Postgraduate Diploma (120 credits)
- Postgraduate Certificate (60 credits)
Part-time
This Master's Degree programme is studied part-time over two academic years, consisting of 180 credits at FHEQ level 7. All modules are semester based and worth 15 credits with the exception of project, practice based and dissertation modules.
Possible exit awards include:
- Postgraduate Diploma (120 credits)
- Postgraduate Certificate (60 credits)
Programme Adjustments (if applicable)
N/A
Modules
Year 1 (full-time) - FHEQ Level 7
Year 1 (part-time) - FHEQ Level 7
| Module code | Module title | Status | Credits | Semester |
|---|---|---|---|---|
| COMM075 | MACHINE LEARNING FOR DATA SCIENCE | Compulsory | 15 | 2 |
| COMM072 | RESEARCH, ETHICS, AND SECURITY IN DATA SCIENCE | Compulsory | 15 | 2 |
| COMM070 | MSC DATA SCIENCE DISSERTATION | Compulsory | 60 | Cross Year |
| MATM074 | ESSENTIAL MATHEMATICS FOR DATA SCIENCE | Compulsory | 15 | 1 |
| COMM080 | PROGRAMMING FOR DATA SCIENCE | Compulsory | 15 | 1 |
Year 2 (part-time) - FHEQ Level 7
Opportunities for placements / work related learning / collaborative activity
| Associate Tutor(s) / Guest Speakers / Visiting Academics | Y | |
| Professional Training Year (PTY) | N | |
| Placement(s) (study or work that are not part of PTY) | N | |
| Clinical Placement(s) (that are not part of the PTY scheme) | N | |
| Study exchange (Level 5) | N | |
| Dual degree | N |
Other information
The school/department of [CSEE] / [MSc Data Science (Conversion)] is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This programme is designed to allow students to develop knowledge, skills, and capabilities in the following areas:
Digital capabilities: [ Digital skills are key for many industry jobs and this programme aims to develop both the foundational underpinning as well as industry ready digital skills. This programme enhances students' digital capabilities by providing hands-on experience with cutting-edge data science tools and technologies. Students will gain proficiency in industry-standard programming languages such as Python and R, work with cloud-based data platforms, and develop expertise in database management, machine learning, and data visualization. ]
Employability: [The MSc in Data Science (Conversion) is designed to meet the evolving needs of data-driven industries by developing graduates who are both technically skilled and strategically aware. The curriculum blends practical applications with critical reflection, covering areas such as data science, AI, cloud computing, business intelligence, and machine learning. Through real-world projects, industry-informed case studies, and an MSc dissertation, students will have the opportunity to build a professional portfolio that demonstrates their ability to apply data science to solve complex, high-impact problems. The programme explicitly prepares students for roles where data insights drive strategic decision-making whether in finance, healthcare, retail, government, or technology by encouraging alignment of technical solutions with business objectives, organisational priorities, and societal needs]
Global and cultural capabilities: [ Data science is a globally relevant discipline, and this programme fosters a diverse, inclusive, and collaborative learning environment. Students from various backgrounds will engage in cross-cultural discussions, collaborative projects, and virtual networking opportunities, enriching their understanding of data science challenges and applications from multiple perspectives. Through case studies and real-world datasets from international sources, students will gain insights into how data science can be applied across different industries, regions, and cultural contexts.]
Resourcefulness and Resilience: [ The emphasis on critical thinking, algorithmic problem-solving, and working with large, unstructured datasets will help students develop the resourcefulness needed to succeed in a dynamic and fast-evolving field. The MSc Dissertation Project will further enhance students' ability to manage complex research tasks, reinforcing their capacity for independent inquiry and problem-solving.]
Sustainability: [ The programme integrates sustainability principles by exploring the role of data science in addressing global challenges such as climate change, public health, and responsible AI. Through modules like Professionalism, Ethics and Security for Data Science, students will critically evaluate the ethical and societal implications of data science, including issues of data privacy, algorithmic bias, and responsible AI development. Additionally, topics such as energy-efficient computing, ethical AI, and data-driven sustainability initiatives that address the UN sustainability goals will be explored in coursework and research projects, ensuring that graduates can contribute to sustainable and socially responsible data science practices.]
Quality assurance
The Regulations and Codes of Practice for taught programmes can be found at:
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2026/7 academic year.