BSc Data Science (at ZJUT, China)
| UCAS code | N/A |
|---|---|
| Duration | 3 years |
| Entry year | 2027 |
| Campus |
| Typical offer | Gaokao: At least 80%. English subject exam: At least 80%. |
|---|---|
Why study BSc Data Science (at ZJUT, China) at Exeter?
Join a unique programme that brings together the academic strengths of both the UK and China. Through our partnership with the Zhejiang University of Technology (ZJUT), you can study for a University of Exeter degree without leaving China - combining the global perspective of a top UK university with the local expertise and reputation of one of China’s leading institutions.
BSc Data Science is a joint programme that offers high-quality teaching, a carefully designed curriculum, and strong student support, all shaped by Exeter’s academic standards.
You’ll gain more than just a degree - you’ll develop a way of thinking that’s valued worldwide, giving you an edge whether you pursue a career in China or internationally.
Course content
You may notice changes to some of our modules over the coming months. This is because we are making space for the following:
- Minors: Future Skills Pathways - Alongside your main degree you may be eligible (depending on your course) to choose modules from another subject to broaden your skills and interests.
- Skills to Thrive built into every degree - Essential skills for your future, including communication, problem-solving, teamwork and digital confidence.
- Increased innovation and wellbeing - More room for creative learning, real-world projects and a healthier study rhythm.
The modules below provide examples of what you can expect to learn on this degree course based on recent academic teaching. The precise modules available to you in future years may vary depending on staff availability and research interests, new topics of study, timetabling and student demand.
Please note that the module information displayed here is subject to change.
45 credits of compulsory modules, 60 credits of compulsory ZJUT modules not required for UoE award.
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Programming | 15 | |
| Calculus I | 15 | |
| Discrete Mathematics and Probability | 15 | |
| ESE UGT Stage 1 Compulsory ZJUT Modules (not required for UoE award) | ||
| Academic English Reading | 15 | |
| Academic English for Science and Engineering | 15 | |
| Academic English Writing | 15 | |
| Academic Oral English | 15 | |
COM1020ZUT: Programming
We use computers in almost all aspects of our daily lives and throughout science, so it is easy to take them for granted. However, so that we can use computers to solve new problems and create new things, we have to be able to program them. This module introduces you to programming and problem-solving with a computer. You will learn how to formulate an algorithm to solve a problem, and you will acquire the skills to write, test and debug programs. The module will also introduce you to object-oriented problem-solving methods and provide you with object-oriented (OO) techniques for the analysis, design and implementation of solutions.
This module is an introductory course in computer programming and will introduce you to the fundamental concepts of computer algorithms and programming, with a strong emphasis on practical implementation. You will also learn how to apply analytical and problem-solving skills to the design and implementation of small applications. Finally, the module will introduce you to the Object-Oriented programming paradigm and its basic design concepts.
MTH1005ZUT: Calculus I
During your degree, you will be solving problems and proving theories in several branches of mathematics, such as pure mathematics, applications to science and engineering, and statistics. Inevitably, you need to be able to calculate. That is what gives mathematics its great power. This module covers developed bodies of useful techniques as a toolkit of common knowledge. It brings emphasis on the techniques rather than the applications of the techniques. Such techniques will enable you to deepen your familiarity with, and generalise, methods that you have seen at the school level of mathematics. This module will study topics that include the geometry of conic sections, functions and their properties, continuity and differentiability, including the Mean Value Theorem and Rolles’ Theorem, differentiation from first principles, Leibniz’ rule for differentiation, limits and convergence of sequences and series, including Power Series and Taylor Series.
MTH1006ZUT: Discrete Mathematics and Probability
Our ability to collect and analyse data is increasingly driving our world. Statistics is concerned with both the practice of analysing data to learn about the world, and the theory that underpins the methods and models used for data collection and analysis. This theory is itself based on probability, the mathematics of chance and uncertainty. In this module, you will learn about the fundamentals of combinatorics and probability and the mathematical properties of random variables and probability distributions, always with a view to how these can be used as building blocks for formulating data models.
The aim of this module is to introduce basic concepts in discrete mathematics and probability. It provides the foundation for learning other topics in statistics and probability, including statistical modelling and inference. The module aims to develop an understanding of how to describe and analyse formal probability concepts, including sample spaces, events, conditional probability, and probability distributions. The emphasis will be on building rigorous problem-solving and on illustrating how discrete methods are applied.
INT0087ZUT: Academic English Reading
This module develops your academic reading skills and ability to locate, evaluate, and use sources effectively for university study. You will learn strategies for reading academic texts efficiently, taking effective notes, and building your academic vocabulary. Through regular reading practice and source evaluation exercises, you will develop confidence in finding relevant sources, understanding complex texts, and synthesizing information from multiple sources. These skills are essential for success in research-based assignments in science and engineering programmes.
INT0088ZUT: Academic English for Science and Engineering
This module provides you with the foundational academic language skills needed to study science and engineering at university level. You will develop essential grammar and vocabulary knowledge alongside practical skills in reading, writing, listening, and speaking. Through engaging with authentic academic materials and collaborative activities, you will build confidence in using English accurately in academic contexts. The module emphasizes systematic vocabulary development and grammatical accuracy, preparing you for the integrated demands of university-level study in science and engineering disciplines.
INT0089ZUT: Academic English Writing
This module develops your academic writing skills through a process-based approach, emphasizing planning, drafting, revising, and editing. Building on the grammar, vocabulary, and research skills developed alongside this module, you will learn to write clear, well-organised academic essays that integrate sources effectively. Through peer review and tutor feedback, you will develop your ability to revise and improve your writing. The module prepares you for the writing demands of university-level assignments in science and engineering programmes.
INT0090ZUT: Academic Oral English
This module develops your spoken English and listening skills for academic success in higher education. Through listening comprehension activities, collaborative group presentations and academic discussions you will build confidence in communicating effectively in English. You will learn to take effective notes from academic lectures, deliver clear presentations and participate actively in seminars. The module emphasizes group work and peer learning, helping you develop the interpersonal skills essential for study on science and engineering programmes.
Please note that the module information displayed here is subject to change.
75 credits of compulsory modules.
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Computers and the Internet | 15 | |
| Data Structures and Algorithms | 15 | |
| Social and Professional Issues of Information Age | 15 | |
| Calculus II | 15 | |
| Probability, Statistics and Data | 15 | |
COM1022ZUT: Computers and the Internet
This module is designed to equip you with the foundational information you need to understand and work in business and technical fields requiring the use of computers and networking technologies. Computing technology has a diversity of applications, so this module is suitable both for computer science students and for those pursuing other study disciplines. On this module, you will acquire useful knowledge of computer systems, computer networks and information systems analysis and design.
By the end of the module, you should be well placed to make use of an extensive range of hardware and software. In addition, you will have gained the knowledge and skills to enable you to analyse existing computer-based information systems and to design and develop web-based applications from informal specifications.
COM1023ZUT: Data Structures and Algorithms
According to an old formula, Algorithms + Data Structures = Programs. This remains as true today as when it was originally formulated by Niklaus Wirth in 1976, and encapsulates the truism that all computation consists of the manipulation of data by means of systematic procedures. But data comes in many different forms (e.g., numerical, alphabetical, graphical) and only by knowing how it is structured can we specify the procedures – algorithms – for manipulating it to produce desired outcomes. Thus, the study of data structures and algorithms constitutes an integrated topic, which forms the subject matter of this module. You will be introduced to some of the key concepts in the area, with plenty of examples to illustrate them, and you will be given a chance to demonstrate your understanding by undertaking exercises.
COM1024ZUT: Social and Professional Issues of Information Age
The module aims to provide you with the tools to reflect upon your role in the interface between digital technologies and society and on the moral and ethical use of information and information systems. By taking this module, you will become aware of your legal responsibilities and rights as an IT professional and as a user of digital technologies. The module will cover ethical theories, computer law and professional codes of conduct, and will address the ways in which broader areas of law (e.g. defamation, contracts, privacy and freedom of information legislation) impact upon technology users and IT professionals.
This module will introduce you to the law regulating the use of information and digital technology. It will enhance your awareness and critical thinking skills regarding the social impact of information technology, and help you relate professional codes of conduct to ethical theories.
MTH1008ZUT: Calculus II
This module builds on Calculus I by extending fundamental techniques to functions of several variables. Students will explore the concept of multivariable functions through topics such as partial derivatives and the chain rule, and will learn to apply Taylor approximations to estimate multivariate functions. In addition, the module introduces constrained optimisation using Lagrange multipliers. The course further develops integral calculus skills by covering techniques such as substitution and integration by parts, and by teaching the evaluation of multiple integrals with applications to problems like computing volumes and areas in higher dimensions. An introduction to differential equations is provided, beginning with linear and separable ordinary differential equations before progressing to basic partial differential equations. Throughout the module, there is a strong emphasis on both theoretical understanding and practical problem-solving.
MTH1009ZUT: Probability, Statistics and Data
In this module, you will learn about the mathematics for how probability is used to quantify uncertainty. You will also gain experience in employing these ideas to analyse data using statistical software such as the R programming environment. You will learn about the fundamental way with which mathematical entities such as random variables and probability distributions are used to formulate statistical models to learn from data. This includes classical statistical inference and uncertainty quantification of estimation uncertainty.
The aims of the module are to develop the ability to analyse, interpret, and draw conclusions from real data sets using sound statistical principles. Students will learn the theory and practical use of estimation, hypothesis testing, regression, and model fitting, as well as how to evaluate model assumptions, goodness-of-fit, and prediction accuracy. The emphasis will be on applying statistical techniques to real-world problems, integrating the use of R, and critical thinking about date, variability, and uncertainty.
Please note that the module information displayed here is subject to change.
120 credits of compulsory modules.
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Communication and Climate Crisis | 15 | |
| Fundamentals of Machine Learning | 15 | |
| Group Software Engineering Project | 15 | |
| Machine Learning and Data Science | 15 | |
| Software Development | 15 | |
| Statistical Modelling and Inference 1 | 15 | |
| Statistical Modelling and Inference 2 | 15 | |
| Database Theory and Design | 15 | |
CMM2019ZUT: Communication and Climate Crisis
The climate crisis is the defining issue of our era, threatening the future viability of human life on planet Earth. This module critically explores how the climate crisis is communicated. We will think about the role that the mass media, from the news to disaster movies, play in framing the parameters of climate change debates. Digital communications technologies, from the internet to the smartphone, play a key role in circulating information and images about the climate catastrophe and are often promoted as environmentally friendly solutions. However, they come with their own carbon costs that are frequently overlooked. We will develop critical approaches for thinking about the climate crisis through a range of topics, including media representations of the natural world, the carbon footprint of digital technologies, and fictional and non-fictional stories about environmentalism and celebrity activism.
COM2029ZUT: Fundamentals of Machine Learning
Unlike traditional software, artificial intelligence software can improve performance upon ingesting increasing quantities of data. This module will introduce you to the core concepts that are needed to understand the field of Artificial Intelligence and Machine Learning. You will learn about the principal paradigms from a theoretical point of view and gain practical experience through a series of workshops. In this module, we will emphasise the notion and importance of data, and you will learn how machines can deal with different types of data sources, ranging from images and text to networks and user preferences.
This module aims to equip you with the fundamental notions to understand and identify the compromises and trade-offs that must be made when using a machine learning approach. It will provide the foundations to understand the principal flavours of machine learning techniques. Emphasis will be placed on how to work effectively with different information sources.
COM2030ZUT: Group Software Engineering Project
This module will introduce you to those aspects of software engineering that relate to the requirements analysis and production of large systems by the application of programming techniques to a large and complex project in a team-working environment.
The main objectives of this module are to introduce key aspects of software engineering to you in a practical way. Aspects of software engineering that will be covered in this module include application of programming techniques to large-scale software system development, requirements analysis, human computer interface (HCI) design, software system design and development, software system testing and software system integration and deployment. They also include project documentation, project management and teamworking.
The aim of this module is to equip you with the necessary practical and theoretical skills to enable you to develop and implement software systems in a systematic manner. You will be introduced to the main principles underpinning software engineering through lectures and workshops. Early in the module, you will be presented with a realistic software engineering problem. Working as part of a team, you will apply the skills being presented in the course to develop and implement a software solution to the specified problem.
COM2031ZUT: Machine Learning and Data Science
This module will improve your knowledge and skills in machine learning and data science. You will gain theoretical and practical understanding of some of the core techniques in machine learning (including supervised/unsupervised methods, feature extraction, binary classification, elementary text and image analysis, amongst others). You will also understand how machine learning and other techniques are combined in effective data science workflows. Finally, you will learn about alongside some of the practical challenges faced in real-world data science, such as handling missing or erroneous data, linking different datasets, and data visualisation and how to address them using modern data engineering techniques.
This module aims to equip you with the fundamentals of machine learning and data analysis. It will provide a thorough grounding in the theory and application of machine learning and statistical techniques for classification, regression and unsupervised methods. We will pay particular attention to methods for visualising and communicating complex datasets.
COM2032ZUT: Software Development
The module will introduce you to software design and development concepts and methods, alongside intermediate and advanced constructs and concepts in the Java programming language, and the programming paradigms these relate to. This includes generic programming (and Java generics), concurrent programming (via Java threads), design patterns, networked programs and nested inner classes. We will also cover widespread tools in software development, including version control and unit testing.
This module will introduce you to methods for the rigorous testing and assessment of software, and prepare you for complex programming tasks in a specific object-oriented programming language, including advanced concepts and syntax, and the use of multiple programs in parallel.
MTH2012ZUT: Statistical Modelling and Inference 1
Statistical modelling lies at the heart of modern data analysis, helping us to describe and predict the real world. Statistical inference is the way that we use data and other information to learn about and apply statistical models. In this module, you will learn the theory underpinning modern statistical methods such as fitting normal linear models, evaluating how well they fit the data and taking inferences from it. You will apply the theory using statistical software such as R to analyse and draw conclusions from a range of real-world data sets. Topics covered in the module range from estimators, confidence intervals and hypothesis testing to statistical modelling, regression and comparison of models.
This module aims to develop understanding and competence in statistical modelling by introducing you to the Normal linear model from a modern perspective. It will provide you with the ability to formulate and apply these models in a range of practical settings, to carry out associated inference appreciating how this relates to the general likelihood inferential framework, and to perform appropriate model selection and model checking procedures. Use will be made of a suitable statistical computer language for practical work.
MTH2013ZUT: Statistical Modelling and Inference 2
In this module, you will learn the fundamental of experimental designs and non-parametric statistics. Key elements of statistical (regression) modelling such as interactions between quantitative and categorical explanatory variables will be taught. We will also discuss key statistical concepts such as sample size, missing data and robust or resistant statistical methods. Moreover the module will cover key non-parametric statistical concepts and methods such as kernel density estimation non-parametric statistical tests and permutation/randomisation tests.
ZUT2001: Database Theory and Design
This module provides a comprehensive exploration of the theoretical foundations and advanced technical issues underpinning modern database management systems. It aims to bridge the gap between classical database theory and contemporary industrial practice.
The module is designed to equip you with the critical knowledge and practical skills necessary to architect, implement, and manage robust database systems. Through a blend of theoretical study and hands-on practice with a current, commercially relevant database management system, you will explore the full lifecycle of database development.
Upon successful completion of this module, you should be able to: Articulate database queries using both SQL and relational algebra; Explain the core architectural principles and internal mechanisms of modern relational database management systems (RDBMSs); Design robust database schemas by applying the principles of functional dependencies and normalization; Analyse the structure and function of a disk-oriented storage manager, including the role of table heaps and indexes; Evaluate fundamental concurrency control and database recovery algorithms, and justify their importance in ensuring data integrity; Critically assess emerging trends and technologies in the database landscape; Apply industry-standard methodologies to design, develop, and manage database systems and applications.
Entry requirements for 2027 entry
Students are selected based their scores from the National College Entrance Examination (Gaokao) in China.
The typical offer would be a final average grade of at least 600 out of a total 750, i.e. 80%, and at least 80% for the English subject exam.
Learning and teaching
Our BSc Data Science programme is an innovative interdisciplinary course aimed at students wishing to work or research in data science.
It covers the core areas of mathematics and computer science and includes modules which introduce you to applied data science (e.g. machine learning, data structure and algorithm, social networks and text analysis) as well as social context (e.g. governance, ethics, business applications).
Your future
The course will enable you to become an outstanding dynamic problem solver with a strong technical skillset.
The broad-based skills acquired during your degree will also give you an excellent grounding for a wide variety of careers, not only those related to Data Science but also in wider fields.