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The MSc Data Science at Nottingham is a 12-month full-time interdisciplinary programme combining computer science and mathematical sciences, teaching you statistical modelling, machine learning, advanced algorithms, and an individual research project. It’s suited both to those with prior programming or quantitative backgrounds and those who need a conversion or catch-up pathway, aiming for data science, analytics, AI, or research roles.
Curriculum Structure
Because this is a one‐year programme, it is organised into taught modules (core + optional) plus a 60-credit individual project. In the taught portion, core modules include Machine Learning, where you learn supervised & unsupervised methods, neural networks, data mining; Data Science with Machine Learning, emphasising practical data preprocessing, visualisation, forecasting and classification; and Advanced Algorithms and Data Structures, focusing on efficient data structures, algorithmic complexity, and classic graph & optimization problems. Students without a computing or strong maths background can start with modules such as Programming, Applied Statistics and Probability, or Statistical Modelling to build foundation skills. The year culminates with the Research Project (60 credits), in which students carry out an empirical or theoretical data-science research or development project, often with industry or real data.
Focus areas: “Machine Learning; Statistical Modelling; Advanced Algorithms & Data Structures; Data Visualisation; Forecasting & Time Series; Programming & Data Engineering”
Learning outcomes: “Gain ability to build, train, evaluate ML models; handle large datasets; apply statistical inference and forecasting; use appropriate data structures and algorithms; conduct independent research / project work using real-world data; communicate findings effectively.”
Professional alignment (accreditation): Delivered by Nottingham’s Schools of Computer Science and Mathematical Sciences; supports both computing and mathematics backgrounds; recognised for its strong research profile (REF); no separate professional body accreditation explicitly listed.
Reputation (employability rankings): Nottingham is top-20 in the UK for Computer Science (Complete University Guide 2026), with “96.4% of postgraduates from the School of Computer Science securing work or further study within six months of graduation.”
This programme is designed to transform theoretical knowledge into practical expertise through hands-on analysis of complex, real-world datasets. You will develop these in-demand skills using the University's high-performance computing resources and industry-standard software, preparing you for the technical challenges of a professional data science career. This applied learning philosophy is central to the programme's delivery, which includes:
High-Performance Computing (HPC): You will gain practical experience using the University's High-Performance Computing facilities, which are essential for running complex data-intensive computations, large-scale simulations, and training sophisticated machine learning models.
Industry-Standard Software and Tools: The curriculum is built around practical work with core data science programming languages like Python and R, and you will use major libraries and platforms such as TensorFlow, Keras, and Spark for machine learning and big data processing.
Substantial Individual Project: A core component is a significant summer research project where you will apply the full data science lifecycle to a substantial problem, often defined in collaboration with industry partners or academic research groups.
Practical and Collaborative Modules: Many modules, such as those in data modelling and big data, are centred on coursework that involves analysing real datasets and include group projects to simulate a professional team environment.
Research-Led Teaching: The programme is delivered by academics from the University's Nottingham Internet of Things (IoT) Research Group, the Computer Vision Lab, and the Intelligent Modelling and Analysis Group, ensuring exposure to cutting-edge applications and methodologies.
Graduates of the MSc Data Science at the University of Nottingham benefit from strong employment outcomes, with many taking up roles such as Data Scientist, Data Engineer, Machine Learning Engineer, and Business Analyst in diverse industries. The program provides a strong foundation in advanced data science techniques, practical machine learning applications, and data system administration, preparing students for dynamic careers in data-driven environments:
The University Careers Service offers one-to-one guidance, sector-specific workshops, employer events, and access to the MyCareerHub platform for internships and job placements tailored to data science roles.
Nottingham holds a top position in the UK for graduate employability, with an average starting salary around £34,000 and a graduate employment/full-time study rate exceeding 90% within 15 months.
Strong industry partnerships exist with companies such as Barclays, Bloomberg, Deloitte, IBM, and Goldman Sachs, providing opportunities for internships, projects, and networking.
The program benefits from long-term recognition through its affiliation with the School of Computer Science and Mathematical Sciences, alongside strong research-led teaching.
Graduates have successfully secured roles across finance, tech, consultancy, research, and government sectors globally, supported by the university’s robust alumni network and employer relationships.
Further Academic Progression:
Graduates may pursue doctoral research (PhD) in data science, machine learning, or applied statistics, or work toward professional certifications and advanced specialization programs in emerging areas such as AI, big data, and computational analytics to deepen expertise and expand career options.
2:1 (or international equivalent) with an affinity for programming and/or advanced statistics evidenced through prior study or practical experience detailed in the application. India- India- Bachelor's degree with minimum 60%. IELTS 6.5 with at least 6.0 in each elementTOEFL: 90 (minimum 19 in writing and listening, 20 in reading and 22 in speaking)USA- USA- Bachelor degree from a recognised institution with GPA 3.2 or above.Data ScientistMachine Learning EngineerData AnalystData EngineerBusiness Intelligence AnalystQuantitative Analyst
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University of Nottingham
