MSc — 2026 entry Data Science (Conversion)
Unlock your potential with our new MSc Data Science (Conversion) programme, created especially for students from non-STEM backgrounds. Build in-demand skills in big data analytics, AI, cloud computing, data ethics and security, all while learning from leading academics and gaining exposure to real industry challenges. Whether you’re aiming for a career change or looking to boost your prospects, this programme gives you the tools to shape tomorrow’s technology landscape.
4,138+ people have created a bespoke digital prospectus
Why choose
this course?
- Develop industry-relevant data science skills through a programme co-designed with industry and informed by leading research.
- Upskill or retrain with our Data Science (Conversion) MSc if you come from a varied academic or professional background.
- Transition into the data science workforce through a clear pathway designed for career changers, working professionals and international learners.
- Study flexibly through full-time or part-time options designed to fit around work and other commitments. The two modes aim to cater for various professional backgrounds, with guidance available on the mode best suited to you.
The demand for data science professionals continues to grow across industries such as finance, banking, politics and healthcare due to the increasing reliance on AI and data-driven decision-making.
Statistics
Excellent global ranking
7th in the UK and Top 75 globally for computer science and engineering (Shanghai Global Ranking of Academic Subjects 2025)
What you will study
Start your journey into data science by developing the essential mathematics and programming skills that underpin the core modules of the programme. Gain experience working with real-world datasets across the entire data analysis pipeline while building strong technical knowledge in modern machine learning and statistical methods and their application in business contexts.
The course covers key practical topics, such as business analytics and data visualisation, data security, building databases and working with cloud-based technologies. You will also have the opportunity to dive deeper into specialist areas such as natural language processing, deep learning, and other advanced AI methods, allowing you to shape your expertise in this dynamic and transformative field.
You will complete a dissertation where you can further explore an area of personal interest under the guidance of an academic supervisor. Dissertation projects focus on topical, real-world problems and are often linked to cutting-edge research. Through this process, you will develop research skills, learn how to communicate effectively, and engage with the ethical and security considerations central to data science.
If you're studying this course full-time, you'll study eight modules across the year — four in each semester. You will work on your project full-time during the summer period for approximately two-and-a-half months.
If you are studying part-time, you will study four modules across the year, two in each semester. You will work on your project during the summer period over the two years.
The structure of our programmes follows clear educational aims that are tailored to each programme. These are all outlined in the programme specifications which include further details such as the learning outcomes:
Modules
Modules listed are indicative, reflecting the information available at the time of publication. Modules are subject to teaching availability, student demand and/or class size caps.
The University operates a credit framework for all taught programmes based on a 15-credit tariff, meaning all modules are comprised of multiples of 15 credits.
Course options
Year 1
Semester 1
Compulsory
The module provides for coverage of a variety of statistical methods, including descriptive statistics and validating formulated hypotheses, as well as predictive analytics. The computational foundations and methods of importance to data science are also covered, along with consideration for relevant supporting software and tools.
View full module detailsMathematics is a key tool in Data Science. This module is designed to introduce students to the foundational mathematical techniques that are required to support future data science modules.
View full module detailsThis module provides a solid foundation in Python programming relevant for data science. It introduces students to core programming concepts, essential Python libraries, and practical coding techniques widely used in data analysis and machine learning. By the end of the module, students will be confident in writing Python programs for solving real-world data problems, handling data, performing analysis, and creating visualisations.
View full module detailsOptional
The need for computational power and data storage continues to drive demand for more highly capable systems. Highly data intensive applications demand fast access to terabytes, petabytes, even exabytes of storage; processor intensive applications demand access to various types of processors in various configurations. Such applications are increasingly being developed in both scientific and industrial contexts and need to be variously scalable and supportable for large numbers of geographically distributed users. This module will provide insights into how Cloud Computing attempts to meet the varying needs of such applications.
View full module detailsA key aspect of business operations today, across sectors almost, has to do with gathering the right type of data and storing it in a way that it can be readily available to the right person at the right time. This course looks into the techniques that allow us nowadays to define and operate on large volumes of data as and when it is created. This paves the way for making more intelligent uses of data, whether this has to do with correctness (reliability and consistency) or informing more strategic decisions of the business so it can better prepare itself for the future.
View full module detailsSemester 2
Compulsory
Machine Learning for Data Science incorporates a wide range of machine learning algorithms and data mining techniques, which can be applied to real-world problems and datasets with various characteristics to generate new insights and understanding. Through treatment of the principles and fundamental requirements for machine learning, example applications, and related exercises, this module will offer coverage of a range of contemporarily important and emergent machine learning algorithms. The module will provide for the means to critically evaluate, extend, and apply, appropriate techniques to datasets exemplifying specific characteristics in order to derive suitable and defensible results.
View full module detailsIn today¿s world where companies can amass more and more fine-grained data, it is crucial for a business to understand how this data can be used to effectively drive the business forward. Business Analytics is a set of methods and tools that can transform data into useful insights for decision-making. For example machine learning algorithms can be used to discover interesting patterns in the current market data or to predict customer behaviour (e.g. customer churn) from past data.
View full module detailsThis module introduces students to the research skills required to engage in data science projects in both industry and academia, whilst also covering the relevant ethical and security considerations when designing and implementing data driven projects. Areas of specific concern for ethics and security in machine learning and statistical analysis are highlighted.
View full module detailsOptional
This module will demonstrate fundamental concepts from the field of Natural Language Processing (NLP) and Computational Linguistics. It will also discuss some of the latest advances in NLP and Generative Artificial Intelligence with a focus on Language Models like BERT, T5, and GPT, and get student up to speed with current research. It will provide the necessary skills to enable students to build computational models for solving a range of problems, such as text classification, sequence classification, machine translation and building conversation agents. The students will learn how to build NLP pipelines for preparing training data and choosing appropriate algorithms and techniques to build such models. The module also focuses on aspects of ethical and trustworthy artificial intelligence with discussion on rigorous model evaluation and ethical considerations for computational modeling. Although traditional linguistic approaches will be mentioned, majority emphasis will be put on the state-of-the-art Deep Learning algorithms and Transfer Learning methods for building efficient and trustworthy NLP solutions.
View full module detailsMachine/Deep learning has emerged from computer science and artificial intelligence. It draws on methods from a variety of related subjects including statistics, applied mathematics and more specialized fields, such as pattern recognition and neural network computation. This module offers the theory and related applications of advanced deep/machine learning topics and an overview their applications to other fields, such as natural language processing, medical imaging, health, audio, and fintech etc. The deep learning algorithms which will be studied are used widely in industry by AI start-ups to AI tech giants, like, Google, Meta, Microsoft, Amazon, Tesla etc. It provides a background and related theory of deep/machine learning to manipulate data from various domains like image, video, text, audio etc. This is done by various machine learning algorithms that are discussed, implemented, and demonstrated within the module.
View full module detailsAcross academic years
Compulsory
The dissertation consists of a substantial written report. This report is based on a major piece of work that involves applying material encountered in the taught component of the degree, and extending that knowledge with the student's contribution, under the guidance of a supervisor. The dissertation usually involves a substantial literature survey on a specific topic, followed by the identification of a problem to tackle, and thereafter the development of a technical solution, and experimental or theoretical evaluation of the achievement.
View full module detailsYear 1
Semester 1
Compulsory
Mathematics is a key tool in Data Science. This module is designed to introduce students to the foundational mathematical techniques that are required to support future data science modules.
View full module detailsThis module provides a solid foundation in Python programming relevant for data science. It introduces students to core programming concepts, essential Python libraries, and practical coding techniques widely used in data analysis and machine learning. By the end of the module, students will be confident in writing Python programs for solving real-world data problems, handling data, performing analysis, and creating visualisations.
View full module detailsSemester 2
Compulsory
Machine Learning for Data Science incorporates a wide range of machine learning algorithms and data mining techniques, which can be applied to real-world problems and datasets with various characteristics to generate new insights and understanding. Through treatment of the principles and fundamental requirements for machine learning, example applications, and related exercises, this module will offer coverage of a range of contemporarily important and emergent machine learning algorithms. The module will provide for the means to critically evaluate, extend, and apply, appropriate techniques to datasets exemplifying specific characteristics in order to derive suitable and defensible results.
View full module detailsThis module introduces students to the research skills required to engage in data science projects in both industry and academia, whilst also covering the relevant ethical and security considerations when designing and implementing data driven projects. Areas of specific concern for ethics and security in machine learning and statistical analysis are highlighted.
View full module detailsAcross academic years
Compulsory
The dissertation consists of a substantial written report. This report is based on a major piece of work that involves applying material encountered in the taught component of the degree, and extending that knowledge with the student's contribution, under the guidance of a supervisor. The dissertation usually involves a substantial literature survey on a specific topic, followed by the identification of a problem to tackle, and thereafter the development of a technical solution, and experimental or theoretical evaluation of the achievement.
View full module detailsYear 2
Semester 1
Compulsory
The module provides for coverage of a variety of statistical methods, including descriptive statistics and validating formulated hypotheses, as well as predictive analytics. The computational foundations and methods of importance to data science are also covered, along with consideration for relevant supporting software and tools.
View full module detailsOptional
The need for computational power and data storage continues to drive demand for more highly capable systems. Highly data intensive applications demand fast access to terabytes, petabytes, even exabytes of storage; processor intensive applications demand access to various types of processors in various configurations. Such applications are increasingly being developed in both scientific and industrial contexts and need to be variously scalable and supportable for large numbers of geographically distributed users. This module will provide insights into how Cloud Computing attempts to meet the varying needs of such applications.
View full module detailsA key aspect of business operations today, across sectors almost, has to do with gathering the right type of data and storing it in a way that it can be readily available to the right person at the right time. This course looks into the techniques that allow us nowadays to define and operate on large volumes of data as and when it is created. This paves the way for making more intelligent uses of data, whether this has to do with correctness (reliability and consistency) or informing more strategic decisions of the business so it can better prepare itself for the future.
View full module detailsSemester 2
Compulsory
In today¿s world where companies can amass more and more fine-grained data, it is crucial for a business to understand how this data can be used to effectively drive the business forward. Business Analytics is a set of methods and tools that can transform data into useful insights for decision-making. For example machine learning algorithms can be used to discover interesting patterns in the current market data or to predict customer behaviour (e.g. customer churn) from past data.
View full module detailsOptional
This module will demonstrate fundamental concepts from the field of Natural Language Processing (NLP) and Computational Linguistics. It will also discuss some of the latest advances in NLP and Generative Artificial Intelligence with a focus on Language Models like BERT, T5, and GPT, and get student up to speed with current research. It will provide the necessary skills to enable students to build computational models for solving a range of problems, such as text classification, sequence classification, machine translation and building conversation agents. The students will learn how to build NLP pipelines for preparing training data and choosing appropriate algorithms and techniques to build such models. The module also focuses on aspects of ethical and trustworthy artificial intelligence with discussion on rigorous model evaluation and ethical considerations for computational modeling. Although traditional linguistic approaches will be mentioned, majority emphasis will be put on the state-of-the-art Deep Learning algorithms and Transfer Learning methods for building efficient and trustworthy NLP solutions.
View full module detailsMachine/Deep learning has emerged from computer science and artificial intelligence. It draws on methods from a variety of related subjects including statistics, applied mathematics and more specialized fields, such as pattern recognition and neural network computation. This module offers the theory and related applications of advanced deep/machine learning topics and an overview their applications to other fields, such as natural language processing, medical imaging, health, audio, and fintech etc. The deep learning algorithms which will be studied are used widely in industry by AI start-ups to AI tech giants, like, Google, Meta, Microsoft, Amazon, Tesla etc. It provides a background and related theory of deep/machine learning to manipulate data from various domains like image, video, text, audio etc. This is done by various machine learning algorithms that are discussed, implemented, and demonstrated within the module.
View full module detailsAcross academic years
Compulsory
The dissertation consists of a substantial written report. This report is based on a major piece of work that involves applying material encountered in the taught component of the degree, and extending that knowledge with the student's contribution, under the guidance of a supervisor. The dissertation usually involves a substantial literature survey on a specific topic, followed by the identification of a problem to tackle, and thereafter the development of a technical solution, and experimental or theoretical evaluation of the achievement.
View full module detailsTeaching and learning
Our teaching is influenced by current research in data science and artificial intelligence (AI) and driven by the tools and techniques that both employers and academic researchers are using daily.
You will be taught by lecturers who are experts in their fields and are members of the Surrey Institute for People-Centred AI and/or the NICE Research group.
You will also benefit from guest lectures, industry talks and seminars.
- Group work
- Independent study
- Laboratory work
- Lectures
- Project work
- Research work
- Seminars
- Tutorials
- Workshops
Assessment
We use a variety of methods to assess you, including:
- Coursework
- Examinations
- Presentations
- Projects (individual and group)
- Reports
- Class tests.
General course information
Contact hours
Contact hours can vary across our modules. Full details of the contact hours for each module are available from the University of Surrey's module catalogue. See the modules section for more information.
Timetable
New students will receive their personalised timetable during Welcome Week. In later semesters, at least one week before the start of the semester.
Scheduled teaching can take place on any day of the week (Monday – Friday), with part-time classes normally scheduled for one or two days. Wednesday afternoons tend to be for sports and cultural activities.
View our code of practice for the scheduling of teaching and assessment (PDF) for more information.
Location
This course is based at Stag Hill campus. Stag Hill is the University's main campus and where the majority of our courses are taught.
We offer careers information, advice and guidance to all students whilst studying with us, which is extended to our alumni for three years after leaving the University.
Our data science masters graduates are in high demand across many sectors, and go on to roles such as data scientist, data analyst, data engineer, data architect or business analyst.
Surrey graduates have joined organisations such as:
- Allianz Partners
- Bank of America
- Fluro
- Healthera
- IBM
- KPMG
- NBCUniversal
- Rolls-Royce
- S&P Global.
- A multi-purpose Computer Science Laboratory
- Six open access PC labs and four dedicated specialist labs
- Specialist desktop solutions, including development software, research packages and dedicated printing
- On premise cloud facilities, OpenNebula provides large-scale support for deployment and security experimentation
- State-of the art machines are grouped into an AI cluster to support performing high level computer experimentation.
Computer science lab tour
Computer science lab tour
UK qualifications
A minimum of a 2:2 UK honours degree in any subject, with prior study of modules in mathematics, statistics, or programming.
We may be able to take relevant work experience into consideration if you don't meet these requirements. If this is the case, please provide full details of your role and responsibilities in your personal statement and CV.
English language requirements
IELTS Academic: 6.5 overall including 6.0 in writing and 5.5 in each other component.
These are the English language qualifications and levels that we can accept.
If you do not currently meet the level required for your programme, we offer intensive pre-sessional English language courses, designed to take you to the level of English ability and skill required for your studies here.
International Pre-Masters
Prepare for postgraduate study and boost your career prospects. This is an intensive programme of academic subjects, study skills and English language preparation designed to help you succeed.
Credit Transfer and Recognition of Prior Learning
We recognise that many students enter their course with valuable knowledge and skills developed through a range of ways.
If this applies to you, the recognition of prior learning process may mean you can join a course without the formal entry requirements, or at a point appropriate to your previous learning and experience.
There are restrictions for some courses and fees may be payable for certain claims. Please contact the Admissions team with any queries.
Scholarships and bursaries
Discover what scholarships and bursaries are available to support your studies.
Fees per year
Explore UKCISA’s website for more information if you are unsure whether you are a UK or overseas student. View the list of fees for all postgraduate courses.
September 2026 - Full-time - 1 year
- UK
- £12,900
- Overseas
- £25,900
September 2026 - Part-time - 2 years
- UK
- £6,500
- Overseas
- £13,000
- These fees apply to the academic year 2026-27 only. Fees are reviewed annually, and tuition fees may increase for courses running over more than one year.
Payment schedule
- Students with Tuition Fee Loan: the Student Loans Company pay fees in line with their schedule (students on an unstructured self-paced part-time course are not eligible for a Tuition Fee Loan).
- Students without a Tuition Fee Loan: pay their fees either in full at the beginning of the programme or in two instalments as follows:
- 50% payable 10 days after the invoice date (expected to be October/November of each academic year)
- 50% in January of the same academic year.
- Students on part-time programmes where fees are paid on a modular basis: cannot pay fees by instalment.
- Sponsored students: must provide us with valid sponsorship information that covers the period of study.
The exact date(s) will be on invoices.
Funding
You may be able to borrow money to help pay your tuition fees and support you with your living costs. Find out more about postgraduate student finance.
Apply online
To apply online first select the course you'd like to apply for then log in.
Select your course
Choose the course option you wish to apply for.
Sign in
Create an account and sign into our application portal.
Please note that we may have to close applications before the stated deadline if we receive a high volume of suitable applications. We advise you to submit your application as soon as it is ready.
ApplyPlease note that we may have to close applications before the stated deadline if we receive a high volume of suitable applications. We advise you to submit your application as soon as it is ready.
ApplyAdmissions information
Once you apply, you can expect to hear back from us within 14 days. This might be with a decision on your application or with a request for further information.
Our code of practice for postgraduate taught admissions explains how the Admissions team considers applications and admits students. Read our postgraduate applicant guidance for more information on applying.
About the University of Surrey
Need more information?
Contact our Admissions team or talk to a current University of Surrey student online.
Terms and conditions
When you accept an offer to study at the University of Surrey, you are agreeing to follow our policies and procedures, student regulations, and terms and conditions.
We provide these terms and conditions at offer stage and are shown again at registration. You will be asked to accept these terms and conditions when you accept the offer made to you.
View our generic registration terms and conditions (PDF) for the 2025/26 academic year, as a guide on what to expect.
Disclaimer
This online prospectus has been published in advance of the academic year to which it applies.
Whilst we have done everything possible to ensure this information is accurate, some changes may happen between publishing and the start of the course.
It is important to check this website for any updates before you apply for a course with us. Read our full disclaimer.