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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # MACHINE LEARNING FOR DATA SCIENCE - **Company:** University of Surrey - **Location:** UK - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Data Analysis, Big Data, Collaborative Learning, Computer Programming, Data Presentation, Python (Programming Language), Machine Learning, Data Analytics, Real Time Data, Data Management, Programming Languages - **Published:** October 9, 2026 - **Apply:** https://catalogue.surrey.ac.uk/2026-7/programme/PCL61-2 ## About the Role 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 financial data science problems, discussing the limitations of the techniques employed. KCT MSc Demonstrate the ability to comprehend the concept of arbitrage pricing in financial markets and apply this in practice using models KCT MSc Critically evaluate technical literature, academic research, and industry reports to inform data science methodologies and decision-making processes. CT PGCert, PGDip, MSc Demonstrate the ability to price basic financial derivatives such as an option or a credit default swap, within the framework of the information-based asset pricing theory. KCPT PGDip, MSc Evaluate the societal, ethical, and environmental impact of data science solutions, ensuring responsible and secure use of data and AI technologies. KCT 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 PGCert, PGDip, MSc Demonstrate proficiency in using industry-standard programming languages, data analysis tools to extract, transform, analyse, and visualize data KCP PGDip, MSc Enhance employability skills through independent learning, team-work, and research driven dissertation projects that showcase expertise in specialized areas of data science. KCT MSc, MATM079 MATHEMATICS OF DATA SCIENCE WITH ADVANCED TOPICS Compulsory 15 2 Module Selection for Year 1 (full-time) - FHEQ Levels 6 and 7 Semester 1. Note on 'Essential Mathematics for Data Science' and 'Programming for Data Science'. Students with significant mathematical/ computing backgrounds (as determined in discussion with the programme leader) will take one or two of the modules COMM071, ECO3062 and MAT3052., MATM079 MATHEMATICS OF DATA SCIENCE WITH ADVANCED TOPICS Compulsory 15 2 Module Selection for Year 1 (part-time) - FHEQ Levels 6 and 7 Semester 1. Note on 'Essential Mathematics for Data Science' and 'Programming for Data Science'. Students with significant mathematical/ computing backgrounds (as determined in discussion with the programme leader) will take one or two of the modules COMM071, ECO3062 and MAT3052. Only two L6 optional module can be selected over the duration of the programme., MATM079 MATHEMATICS OF DATA SCIENCE WITH ADVANCED TOPICS Compulsory 15 2 Module Selection for Year 2 (part-time) - FHEQ Levels 6 and 7 Semester 1. Note on 'Essential Mathematics for Data Science' and 'Programming for Data Science'. Students with significant mathematical/ computing backgrounds (as determined in discussion with the programme leader) will take one or two of the modules COMM071, ECO3062 and MAT3052. ## Description Some of these cookies are necessary and are used to help make our site work. With your consent, we will also use cookies to improve your experience, analyse site usage and assist in our marketing efforts. By clicking 'Accept all', you consent to our use of cookies., * 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 encourage innovation and curiosity in exploring emerging trends in finance, data science and AI, preparing students to adapt to a rapidly evolving technological landscape * 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 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. * lobally competent, culturally aware, and resilient graduates, who can work effectively in multidisciplinary, crosscultural, and rapidly evolving professional environments, while recognising the societal impact of data-driven technologies and promoting sustainable and ethical practices. * To equip graduates, including those from non-mathematical and 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, finance, analytics and AI. * 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., 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), This programme allows students to develop knowledge, skills, and capabilities in the following areas: Digital capabilities: To enhance 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. Employability: The evolving needs of the data-driven finance industry are met by developing graduates who are technically skilled and strategically aware. The curriculum blends practical applications with critical reflection, covering areas such as data science, AI, and machine learning as well as the underpinning financial theory. Through the MSc dissertation, students will have the opportunity to demonstrates their ability to apply data science to solve complex, high impact problems in financial data science. Global and cultural capabilities: As a globally relevant discipline, this programme fosters a diverse, inclusive, and collaborative learning environment. Students from various backgrounds will engage in cross-cultural discussions, collaborative projects, and networking opportunities, enriching their understanding of data science challenges from multiple perspectives. Resourcefulness and Resilience: With emphasis on mathematical problem-solving and working with large datasets students will develop the resourcefulness needed to succeed in a dynamic and fast-evolving field. The 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 in finance. 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 Quality assurance The Regulations and Codes of Practice for taught programmes can be found at: https://www.surrey.ac.uk/quality-enhancement-standards