MSc Data Science
Please note: This page is for 2027 entry. Click here for 2026 entry.
| UCAS code | 1234 |
|---|---|
| Duration | 1 year full time 2 years part time (September 2026 entry only) |
| Entry year | 2026 + January 2027 start |
| Campus | Streatham Campus |
| Typical offer | Applicants are required to have either a 2:2 in a non-related science undergraduate degree, OR a 2:2 in any other degree subject and A Level Mathematics at Grade A, or equivalent. |
|---|---|
Why study MSc Data Science at Exeter?
- MSc Data Science is the programme for you if you want to explore the role of data science in a range of sectors.
- Nearly every sector requires data science expertise – from healthcare, wellbeing and retail to environment, banking, finance and government agencies.
- This conversion programme teaches graduates from a non-computing background* the necessary skills to understand the theory of data science and apply it to their area of interest.
- By choosing optional modules and a research project that match your interests, you can curate a masters in data science that meets your career goals.
- Your research project, which is supervised by an expert data scientist, brings together your learning and enables you to apply it to a practical or theoretical problem.
- You will have access to our latest computer facilities which are world-class spacious teaching labs allowing comfortable, collaborative working in a sensory-friendly environment.
*If you have a computing background, we very strongly advise that you apply for one of our advanced programmes, please see MSc Advanced Computer Science or MSc Advanced Computer Science with Business.
Fast Track (current Exeter students)
Data Science at the University of Exeter equips students with technical skills to understand, manage and store large data sets, together with the flexibility to apply powerful analytics to data across industry, business and research.
Top 10 in the UK for graduate prospects
Joint 9th for graduate prospects for Computer Science in the Complete University Guide 2027
The University has invested £50 million in the development of its Data Science and Artificial Intelligence capabilities
Excellent facilities spanning a wide range of machine types and software ecosystems alongside world-class computer science labs
Long-established partnership with the Alan Turing Institute and home to the Institute of Data Science and Artificial Intelligence
Entry requirements
Applicants are required to have at least a 2:2 degree in a non-related science undergraduate degree, or a 2:2 in any other degree subject and A Level Mathematics at Grade A or equivalent.
Please note, this is a conversion course designed to allow students from a wide range of backgrounds to pursue a career in data science. If you have studied Computer Science, Mathematics, Physics, Engineering or other related degree, we very strongly advise that you apply for one of our advanced programmes, please see MSc Advanced Computer Science or MSc Advanced Computer Science with Business. The MSc Data Science programme is a conversion course intended for students who have a non-related degree background therefore the course content has been designed so that it is appropriate for that entry level.
We may consider applications with non-standard qualifications where there is evidence of exceptional performance in modules relevant to the programme of study, significant relevant work experience, or relevant professional qualifications.
Please also see our guidance on essential documentation required for an initial decision on taught programme applications.
Entry requirements for international students
Please visit our entry requirements section for equivalencies from your country and further information on English language requirements.
Please also see our guidance on essential documentation required for an initial decision on taught programme applications.
Entry requirements for international students
English language requirements
International students need to show they have the required level of English language to study this course.
The required IELTS test scores for this course fall under Profile B1.
Please visit our English language requirements page to view the required test scores and equivalencies from your country.
I did my BSc in Politics and International Relations here at Exeter and graduated with proficiency in Spanish. What I love about the MSc Data Science course is that it’s a conversion programme, perfect for someone like me who didn't have a strong technical background when starting the programme.
The lecturers really understand that people come from different academic backgrounds with varying levels of experience, and they make a real effort to meet everyone where they are. They break down complex concepts in a way that’s easy to grasp, which has made the transition into a technical field smoother for me.
For the past year and a half, I’ve been working as a Data Assistant with the Students’ Guild, analysing survey data to help make the Guild more student-led. It’s been so rewarding to see how my work directly shapes and improves services for fellow students.
In January 2025 I joined the Chartered Management Institute (CMI) as an Events Research Intern. It will be a great way to apply the skills I’m learning in my MSc, boosting my confidence in tackling real-world problems.
Ekaterina
MSc Data Science
Course content
The MSc Data Science programme is designed for individuals from diverse academic backgrounds to learn the underpinning theory of data science together with methods for implementation and application. The programme has compulsory modules to ensure you learn a comprehensive set of data science skills alongside a choice of optional modules, enabling you to flex the programme to meet your interests and aspirations.
You have the option to study this programme full-time over one year or part-time over two years (September 2026 entry only).
Part time programme structure
- Year 1: You will complete at least 4 modules (60 credits) which must include ECMM443 Introduction to Data Science and COMM109 Programming with Python.
- Year 2: You must complete at least 4 modules (60 credits) and the compulsory COMM514 Research Project (60 credits).
All students (full or part-time) may choose up to 30 credits of NQF Level 7 modules which are not listed in the optional modules list below, either from within or outside the Faculty of Environment, Science and Economy, subject to approval, timetabling and satisfaction of prerequisites.
Not all modules will be available every year, and new modules may be made available from time to time.
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.
150 credits of compulsory modules, 30 credits of optional modules
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Data Systems | 15 | |
| Research Project | 60 | |
| Machine Learning | 15 | |
| Introduction to Data Science | 15 | |
| Learning from Data | 15 | |
| Data Governance and Ethics | 15 | |
COMM108: Data Systems
This module will introduce you to the ways in which data is stored within a computer system. You will learn about a variety of types of database, including those based on the structured query language (SQL) and those designed to structure data differently (so-called NoSQL databases). You will develop a theoretical understanding about how data should be organised, and will learn how to access and modify the data in a database from an application.
The aim of this module is to instil students with an appreciation of the different ways that data can be stored. By introducing multiple approaches (e.g. SQL-based and NoSQL) students will learn how to select the most appropriate storage for a given application, taking into account the complexities around accessing and writing data. Students will also learn how to construct software to connect an application to a database securely.
The module will also cover concurrency control, backup and recovery, user management, and web development with databases, The module will also introduce modern concepts concerning data systems, which might include big data, the cloud, data warehousing, blockchain, decentralised systems, and governance.
COMM514: Research Project
In this module, you will work on a research problem in an area relating to your programme of study, applying the tools and techniques that you have learned throughout the modules of the programme. This is an independent project, supervised by an expert from the relevant area, and culminates in writing a dissertation in the form of a research paper, describing your research and its results.
Research topics can be selected from across the breadth of computer science, data science and related topics. The project may include theoretical analysis, as well as practical software implementation.
This module aims to give you in-depth experience of research in an area relating to your programme of study. It will help you prepare for projects both in an industry or commercial setting, as well as in further postgraduate research work, such as a PhD. The module builds on the knowledge and skills you have acquired in the taught modules of the programme to allow you to investigate an area of particular interest to you. It aims to give you experience of many aspects of research work, including problem formulation, literature review, planning, tool development, experimentation, analysis and presentation of results.
ECMM422: Machine Learning
Machine learning has emerged mainly from computer science and artificial intelligence, and draws on methods from a variety of related subjects including statistics, applied mathematics and more specialized fields, such as pattern recognition and neural computation. Applications are, for example, image and speech analysis, medical imaging, bioinformatics and exploratory data analysis in natural science and engineering. This module will provide you with a thorough grounding in the theory and application of machine learning, pattern recognition, classification, categorisation, and concept acquisition. Hence, it is particularly suitable for Computer Science, Mathematics and Engineering students and any students with some experience in probability and programming.
In this data-driven era, modern technologies are generating massive and high-dimensional datasets. This module aims to give you an understanding of computational methods used in modern data analysis.
ECMM443: Introduction to Data Science
In this module, you will learn about the broad and fast-moving field of data science. You will be introduced to the core competencies and application areas associated with data science, including data handling and visualisation, statistical modelling, network and text data analysis. You will also explore the ways in which data science is transforming business and society, and learn about ethical and governance aspects of data science. Practical exercises and individual study will consolidate your learning and provide the foundations for later study.
This module will cover the breadth of data science to equip students with the context and vocabulary to support more detailed study in future modules. Topics will evolve to reflect current issues in data science, providing students with the tools to formulate data science problems and construct pipelines to begin to solve them technically.
Lectures will be accompanied by data analysis exercises. A series of guided practical exercises will develop skills in programming (in Python), data handling and visualisation.
ECMM445: Learning from Data
Artificially intelligent machines and software must assimilate data from their environment and make decisions based upon it. Likewise, we live in a data-rich society and must be able to make sense of complex datasets. This module will introduce you to machine learning methods for learning from data. You will learn about the principal learning paradigms from a theoretical point of view and gain practical experience through a series of workshops. Throughout the module, there will be an emphasis on dealing with real data, and you will use, modify and write software to implement learning algorithms. It is often useful to be able to visualise data and you will gain experience of methods of reducing the dimension of large datasets to facilitate visualisation and understanding.
The module will also cover some recent neural network architectures and related learning algorithms.
This module aims to equip you with the fundamentals of machine learning and at the same time discuss technical aspects of some well-known machine learning models and related learning algorithms. It will provide a thorough grounding in the theory and application of machine learning and statistical techniques for classification, regression and unsupervised methods (clustering and dimension reduction). The module will cover kernel methods and neural networks (feed-forward architectures only).
SOCM033: Data Governance and Ethics
Data science, machine learning, artificial intelligence and 'big data' have become central to every aspect of social life. How can these complex and powerful technologies best be managed and governed for the benefit of society now and in the future? In this module you will: (1) identify some of the main risks and ethical/legal challenges involved in the widespread automation and digitalisation of services characterising 21st century life (for example, the clash between individual desire for privacy, frameworks for data ownership and the institutional commodification of personal data); (2) examine whether and how such concerns can be handled; and (3) discuss the responsibilities of data scientists and other producers of technologies for data analysis towards their proper use.
Optional modules
| Code | Module | Credits |
|---|---|---|
| Optional 1 | ||
| Digital Transformation | 15 | |
| Strategic and HR Analytics | 15 | |
| Supply Chain Analytics | 15 | |
| Introduction to Computer Vision | 15 | |
| Foundations of Human-Centred AI | 15 | |
| Design Methods for Human-Centred AI | 15 | |
| Social Networks and Text Analysis | 15 | |
| Stochastic Processes | 15 | |
| Bayesian Philosophy and Methods in Data Science | 15 | |
BEMM190: Digital Transformation
This module will introduce you to the fundamentals of digital transformation through study of a range of practical examples. Organisations must position themselves for success in the digital era to be sustainable. For new ventures, it means creating structures and working practices appropriate to the dynamic environment. For established organisations, it means transforming existing structures and ways of working to meet current and future needs while continuing to meet the expectations of existing clients, employees and other stakeholders. Consequently, digital transformation activities are becoming increasingly strategic across public, private and third sectors.
You will also assess the implications for career development within these disruptive environments. This means building the digital skills required for success such as effective workplace communications across hybrid locations, the use of collaborative online tools, and the importance of ethical behaviour and wellbeing.
This module will help you to:
BEMM464: Strategic and HR Analytics
In this module you will deepen your understanding of how to identify and analyse data and information essential to making strategic and HR decisions. In addition to introducing various concepts, theories and frameworks from strategic management and human resources management that can be used to help identify and evaluate important sources of data and information relevant to key strategic and HR decisions and leverage points, the module will develop understanding about how unstructured text can be converted to structured data and then analysed using various dimension reduction techniques. Key concepts taught include pattern recognition, classification, categorisation, and concept acquisition. Additionally, the module will also cover recent developments in the field such as machine learning, deep learning and neural networks and their role in the analytics toolkit.
BEMM783: Supply Chain Analytics
The dynamic and economic performance of supply chains is driven by their lead-times, forecasting, and production activities. Poorly managed supply chains result in an expensive dynamic effect called the bullwhip effect. The bullwhip effect causes inefficiencies in terms of increased inventory holding and costs, poor customer service levels, and the inefficient use of production capacity. We will cover a wide range of topics, such as: Dynamic value stream mapping and time series analysis; Understanding your supply chain in relation to inventory, service levels, and capacity costs; Forecasting for production and distribution in supply chains; Setting the cadence of your production pacemaker; Detailed scheduling at the shop floor; Communicating replenishment orders with suppliers.
This module is suitable for non-specialist and interdisciplinary students. Although this module is based on real world applications of research, no industrial experience or high-level maths is required (although some basic mathematical and computer skills, mostly Excel-based, are required).
Those who are able to successfully apply this knowledge in practice will allow companies to reduce over-time working, reduce inventory investments and improve customer service levels. They will be able to create a stable working environment in supply chains so that companies have the time to undertake maintenance activities, creating a virtuous cycle of improvement.
COMM042: Introduction to Computer Vision
How do we recognise objects and people? How can we catch a ball? How do we navigate our way from our desk to the coffee machine, without bumping into each other? These seemingly simple tasks have represented a challenge for AI scientists for decades. Recent developments in computer vision have seen significant improvement in important applications (face detection in cameras, body tracking, and autonomous cars).
This module will provide you with the fundamentals of computer vision, covering the essential challenges and key algorithms for solving a variety of vision problems. The course will provide both theoretical grounding in the relevant theories and a blend of classical and state-of-the-art approaches to computer vision problems. The course will focus on practical applications of computer vision and cover a broad range of problems, from low-level image processing to object recognition, tracking and 3D vision.
COMM111: Foundations of Human-Centred AI
You will study foundational concepts in how to design Artificial Intelligence (AI) systems that interact with humans. This will involve learning about human psychology including computational theories of how people represent and process knowledge, learn and work together. You will learn about topics including, how people make decisions, how they perform perceptual/manual tasks, how human vision works. You will use these theories to build and critically evaluate Artificial Intelligence systems that work with people.
You should take this module if you are interested in going on to a masters/research degree and/or in the rapidly expanding number of career pathways that involve designing AI to work with people. For these careers learning about human psychology is vital to designing systems that, for example, people find useful but not controlling and people find engaging but not addictive. For example, answers to the following questions require an understanding of the psychology of the user. How can AI be fine-tuned to human preferences and emotions? How can an AI system learn about an individual person’s goals and preferences? How can it learn about their emotions and feelings about others? Answers to these questions can help improve AI systems that work with people in the workplace and the home.
COMM112: Design Methods for Human-Centred AI
Learn the skills needed to practice Human-centred design of Artificially Intelligent systems. You will learn how to use computational design thinking to empathise with people, ideate, prototype and evaluate AI systems. You will learn how to abstract AI problems by engaging with people, communities and contexts. You will apply methods from Human-Computer Interaction to engage with users through participatory design practices.
Having used these methods to abstract Human Centred AI problems, you will learn how to investigate prototype solutions and critically analyse their strengths and weaknesses, both from a computational perspective and a human perspective.
You will attend a weekly class in which an expert in Human-centred AI will lead discussion of an aspect of Human Centred AI design and its implications for how relevant Artificial Intelligence technologies are likely to impact people.
This course is a hands-on, practice-oriented approach to learning Human-centred AI (HCAI) design, with a strong emphasis on the evaluation of AI systems from both technical and human perspectives. Methods covered will include design thinking, participatory design, A/B testing, think-aloud protocols, diary studies, eye tracking studies etc. These tools provide students with the skills required to work with people to understand their needs and desires, understand how and why they perform tasks as they do and design AI systems that work with and for them.
ECMM447: Social Networks and Text Analysis
The rise of the Web has created huge datasets relating to the interaction of users and online content. Much of this content is relational and is best understood using a network perspective (for example, hyperlinked web pages; users linking to content; users linking to users on social platforms). Much of this content consists of unstructured text (for example, webpages, blogs, social media posts) that requires computational methods for analysis at scale. In this module you will learn the core principles of social network analysis and computational text analysis, enabling you to gain insight from the rich data available on the Web.
The aim of this module is to equip you with a range of knowledge and skills needed to make effective use of data from the Web. This module will cover various topics in social network analysis and text analysis, which together allow relational and unstructured text data to be analysed at scale. The module will be taught using the Python language and various open-source packages.
The module will be taught in weekly lectures and associated practical work, together with individual self-study and labs. Lectures will introduce the topics of social network analysis and text analysis, accompanied by practical exercises based on lecture material.
ECMM450: Stochastic Processes
A stochastic process is one that involves random variables. A large number of practical systems within industry, commerce, finance, biology, nuclear physics and epidemiology can be described as stochastic and analysed using the techniques developed in this module. The systems considered may exist in any one of a finite, or possibly countably infinite, number of states. The state of a system may be examined continuously through time or at fixed and regular intervals of time.
You will study processes whose changes of state through time are governed by probabilistic laws, and you will learn how models of such processes can be applied in practice.
Pre-Requisite Modules:
MTHM508: Bayesian Philosophy and Methods in Data Science
Since the 1980s, computational advances and novel algorithms have seen Bayesian methods explode in popularity, today underpinning modern techniques in data science and machine learning with applications across science, social science, the humanities and finance.
This module will cover the Bayesian approach to modelling, data analysis and statistical inference. The module describes the underpinning philosophies behind the Bayesian approach, looking at subjective probability theory, the notion and handling of prior knowledge, posterior inference, and how this approach differs to classical approaches to statistics. It will explore simulation-based inference in Bayesian analyses and develop important algorithms for Bayesian simulation by Markov Chain Monte Carlo (MCMC) such as the Gibbs sampler and the Metropolis-Hastings algorithm. Finally, we’ll apply the techniques and tools developed through the module to fit a wide range of models using modern Bayesian inference software, enabling you to apply techniques discussed in the course to a variety of real datasets, ensuring the student is equipped to use Bayesian methods in future jobs.
This module is appropriate for MSc students who have not completed a mathematics undergraduate degree, with the focus of the assessment on the understanding and application of techniques. Experience of a programming language such as R or Python, and some basic statistics/probability, is beneficial but not required.
Please note that the module information displayed here is subject to change.
165 credits of compulsory modules, 15 credits of optional modules
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Data Systems | 15 | |
| Programming with Python | 15 | |
| Research Project | 60 | |
| Machine Learning | 15 | |
| Introduction to Data Science | 15 | |
| Social Networks and Text Analysis | 15 | |
| Applications of Data Science and Statistics | 15 | |
| Data Governance and Ethics | 15 | |
COMM109: Programming with Python
This module will introduce students to the fundamentals of constructing software using the Python programming language. You will learn how to decompose problems into components that can be implemented to provide a software solution, as well as how to control program flow and represent data within software. Having learned the fundamentals of Python coding you will be introduced to exception handling, Python classes, and be introduced to principles of software development and testing.
MTHM503: Applications of Data Science and Statistics
This module will enable you to learn new Data Science and Statistical methods, and to use the techniques learnt in other modules, by working on analyses of real data examples. There will be a strong emphasis throughout on understanding the practical application of statistical and machine learning methods including clustering, data reduction, methods for handling missing data, study design and introductory methods for time series data. Theory and ideas will be developed to allow the implementation of methods in examples drawn from industry, medicine, finance, public health and environmental challenges, including climate change and air pollution.
Pre-requisites: None
Optional modules
| Code | Module | Credits |
|---|---|---|
| Optional 1 | ||
| Introduction to Computer Vision | 15 | |
| Algorithms and Architectures | 15 | |
COMM107: Algorithms and Architectures
This module will introduce you to algorithms, the fundamentals of their design, and their importance within computer science. You will learn how to design and implement efficient algorithms, as well as evaluating algorithm complexity. You will also learn about key concepts concerning computer architectures, operating systems and networks – including memory management, storage concepts, process control and software execution.
The aims of this module are to introduce you to the skills needed to design, construct and manage the operation of software running on modern computing systems. You will understand the importance of designing efficient algorithms and will be able to analyse their runtime complexity.
I chose the MSc Data Science course at Exeter because I really enjoyed coding in my undergraduate degree and wanted to continue building my coding skills, specifically using machine learning algorithms.
One module I really enjoyed was Bayesian Statistics, as it was quite fun to challenge the traditional frequentist way of thinking, learn about randomness and sampling algorithms. The module leader was a great teacher and made all of the lectures really interactive and actually surprisingly quite entertaining.
The most enjoyable part of the course is definitely the research project. You get to spend around four months working on a topic that you find really interesting. My research focussed on using Bayesian machine learning to predict wake patterns of turbulent air within wind farms, and I had the opportunity to publish my work into a research journal once completing the project. I found it a really valuable way to learn more about the Energy sector, which I now work in, using stochastic programming to model global gas and European power markets for the valuation of flexible assets. I am using and applying a lot of the algorithms and models I learned about on this course in my day-to-day work, which feels really rewarding.
Arthur
MSc Data Science graduate
Fees
2026/27 entry (including January 2027 entry)
UK fees per year:
£14,300 full-time; £7,150 part-time
International fees per year:
£30,300 full-time; £15,150 part-time
Scholarships
The University of Exeter offers a wide range of scholarships to support your education, with £7 million available for international students applying to study with us in the 2026/27 academic year, including our prestigious Exeter Excellence Scholarships. We also provide awards for sport, music and other achievements, as well as regional and partner scholarships with organisations such as Chevening, The Beacon Trust and the British Council. For more information on scholarships and other financial support, please visit our scholarships and bursaries page.
University of Exeter Alumni Scholarship
We are pleased to offer the University of Exeter Alumni Scholarship, a scholarship for University of Exeter alumni beginning a standalone postgraduate programme in 2026/27 with us a scholarship worth 20% of the cost of your first year tuition fees.
Terms and conditions, including deadlines, apply.
Academic partners
Our long-established partnership with the Alan Turing Institute, the UK’s national institute for data science and artificial intelligence, means that we have strong connections to the UK AI research community. Currently we host 11 Turing Fellows and one Turing AI Fellow at the University. Turing Fellows are established scholars with proven research excellence in data science, AI, or a related field. Lectures, conferences and seminars organised by Turing and the Turing University Network are usually open to our students to attend either in person or online.
One of the key reasons I chose Exeter was the MSc Data Science programme’s balance between theory and practical application. Modules like Machine Learning, Learning from Data, and Social Networks and Analysis were my absolute favourites. They allowed me to explore cutting-edge techniques and apply them in real-world scenarios. One project I particularly enjoyed focused on predictive maintenance. This project not only challenged me but also aligned directly with my professional aspirations to work in analytics and process optimisation.
Kokila
MSc Data Science graduate
Teaching and research
All our courses are taught by active researchers who work closely with industrial partners. Our module leads are renowned in their field with prestigious fellowships and awards, many also consulting with major companies. Teaching is mainly delivered by lectures, workshops and online materials. You will have access to comprehensive online learning materials through the University’s virtual learning environment, including recorded lectures. Each module references core and supplementary texts, or material recommended by module deliverers, which provide in depth coverage of the subject and go beyond the lectures.
Internationally recognised research
We believe every student benefits from being taught by experts active in research and practice. All our academic staff are active in internationally-recognised scientific research across a wide range of topics. You will discuss the very latest ideas, research discoveries and new technologies, becoming actively involved in a research project yourself. Find out about our data science researchers by discovering our hub for data-intensive science and artificial intelligence at the Institute for Data Science and Artificial Intelligence (IDSAI). You can also read more about our current computer science research.
Investment in data science and artificial intelligence
The University has invested £50 million in the development of its Data Science and Artificial Intelligence capabilities. The Accelerating Data Science and Artificial Intelligence (ADA) project has been running since 2023 and has invested in teaching, research and infrastructure which this programme benefits from.
Supportive environment
We aim to provide a supportive environment where students and staff work together in an informal and friendly atmosphere. We operate an open door policy, so it is easy to consult individual members of staff or to fix appointments with them via email. As a friendly group of staff, you will get to know us well during your time here.
Assessments
The assessment strategy for each module is explicitly stated in the full module descriptions given to students. Group and team skills are addressed within modules dealing with specialist and advanced skills. Assessment methods include essays, closed book tests, exercises in problem solving, use of the Web for tool-based analysis and investigation, mini-projects, extended essays on specialised topics, and individual and group presentations.
Careers
Data Science is changing the way people do business. Mountains of previously uncollectable data, generated by huge growth in online activity and appliance connectivity, is becoming available to businesses in every sector. The opportunities for businesses and individuals who can manage, manipulate and extract insights from these enormous data sets are limitless. A direct result of this is the dramatic increase in demand for individuals with the skills to turn this information into insight is outstripping supply.
Graduate destinations
Whether you’re looking to take your career in a new direction or for an MSc that will sit alongside your undergraduate degree to land you an exhilarating graduate job, you’re unlikely to find a better choice than Data Science. Examples of graduate roles include data scientist, machine learning scientist, Python programmer and software engineer.
Careers support
You will receive support from our Career Zone team, who provide excellent career guidance at all stages of career planning. The Career Zone provides one-on-one support and is home to a wealth of business and industry contacts. Additionally, they host useful training events, workshops and lectures which are designed to further support you in developing your enterprise acumen. Please visit the Career Zone for additional information on their services.
The University’s career portal has been useful for improving my CV and getting advice on how to make it stand out. Thanks to the University's excellent infrastructure, including cutting-edge labs and a vast library, I've been able to delve deeper into data analysis and predictive modelling, which is crucial for my future career as a Data Scientist/Analyst.
Sandeep
MSc Data Science
