Eligibility Requirements for Exeter MSc in Data Science

60/100

Bachelor's GPA Requirement

Relevant degree degree

UG Requirement

8

Maximum Backlogs Accepted

Exeter MSc in Data Science Rankings

WUR

143rd

About The University of Exeter

The University of Exeter is a prestigious research university in the South West of England, with its main campus in Exeter. Established in 1955, the university has a long history dating back to the founding of the Exeter Schools of Art and Science in 1851.

Today, the University of Exeter is a member of the prestigious Russell Group, recognised for its world-class research and excellent teaching. The university has a global reach, with over 25,000 students from 130 countries and over 125,000 alumni in 183 countries.

The university is known for its outstanding facilities, including state-of-the-art laboratories, research centres, and the Exeter Streatham Campus Library, which holds over 1.2 million physical resources.

Exeter MSc in Data Science Program Description

This conversion programme is designed for graduates from non-computing backgrounds who aspire to work with data across various industries. By studying Data Science at Exeter, you will join a rapidly growing department that is already among the top ten in computer science in the UK.

The programme offers the flexibility to pursue data science according to your passions and interests. It is tailored for those interested in learning the theory of data science and methods for implementation and application.

A significant component of the degree involves a research project, which provides one-to-one engagement with a university mentor who is an active data scientist and a leader in their field.

Program URL:https://www.exeter.ac.uk/study/postgraduate/courses/datascience/datascimsc/

Exeter MSc in Data Science Program Curriculum

Program Curriculum URL:https://www.exeter.ac.uk/study/postgraduate/courses/datascience/datascimsc/#course-content

COMM511 Statistical Data Modelling

This module covers statistical techniques for analyzing and interpreting complex data sets, focusing on practical applications in various industries. It equips students with skills to build and validate statistical models, enhancing their ability to draw meaningful conclusions from data.

ECMM409 Nature-Inspired Computation

Explores computational algorithms inspired by natural processes such as evolution, swarm behaviour, and immune systems. Students learn to apply these algorithms to solve complex optimization problems, simulating the efficiency and adaptability of natural systems.

ECMM410 Research Methodology

This module introduces students to essential research methods and techniques used in data science. It covers research projects' design, implementation, and analysis, emphasising ethical considerations and effective communication of findings.

ECMM423 Evolutionary Computation & Optimisation

This course focuses on evolutionary algorithms and their application in optimization problems. Students study the principles of natural evolution and apply these concepts to develop robust computational methods for solving real-world challenges.

ECMM426 Computer Vision

Covers the fundamentals of computer vision, including image processing, feature extraction, and object recognition. Students learn to develop algorithms that enable machines to interpret and understand visual information from the world.

ECMM447 Social Networks and Text Analysis

Examines the analysis of social networks and textual data using computational techniques. This module equips students with tools to study social interactions and extract insights from large volumes of text data.

ECMM450 Stochastic Processes

Introduces the theory and application of stochastic processes, which model systems that evolve with inherent randomness. Students learn to analyze and predict the behaviour of such systems in various contexts.

ECMM461 High Performance Computing

This course focuses on using high-performance computing systems to solve complex computational problems. Students gain practical experience with parallel computing techniques and learn to optimize algorithms for large-scale data processing.

MTHM033 Statistical Modelling in Space and Time

Covers advanced statistical methods for analyzing data that vary across space and time. Students learn to develop models that capture spatial and temporal dependencies, enhancing their ability to make predictions and inform decision-making.

MTHM508 Bayesian Philosophy and Methods in Data Science

This module explores Bayesian methods and their application in data science. It emphasises the philosophical foundations of Bayesian inference and teaches students to apply these techniques to improve decision-making under uncertainty.

SOCM033 Data Governance and Ethics

This course examines the ethical and legal considerations in data science, including data privacy, security, and governance. Students learn to navigate the complex data regulation landscape and develop responsible data management practices.

COMM510 Multi-Objective Optimisation and Decision Making

Introduces techniques for optimising multiple conflicting objectives simultaneously. Students learn to develop and implement algorithms that balance trade-offs, and support informed decision-making in complex scenarios.

COMM109 Programme with Python

Provides a comprehensive introduction to programming with Python, focusing on its applications in data science. Students learn to write efficient code, manipulate data, and develop algorithms using this versatile programming language.

BEMM190 Digital Transformation

Explores the impact of digital technologies on business and society. Students study strategies for leveraging digital tools to drive innovation and improve organizational performance, preparing them for leadership roles in the digital economy.

Electives

COMM511 Statistical Data Modelling

This module covers statistical techniques for analyzing and interpreting complex data sets, focusing on practical applications in various industries. It equips students with skills to build and validate statistical models, enhancing their ability to draw meaningful conclusions from data.

ECMM409 Nature-Inspired Computation

Explores computational algorithms inspired by natural processes such as evolution, swarm behaviour, and immune systems. Students learn to apply these algorithms to solve complex optimization problems, simulating the efficiency and adaptability of natural systems.

ECMM410 Research Methodology

This module introduces students to essential research methods and techniques used in data science. It covers research projects' design, implementation, and analysis, emphasising ethical considerations and effective communication of findings.

ECMM423 Evolutionary Computation & Optimisation

This course focuses on evolutionary algorithms and their application in optimization problems. Students study the principles of natural evolution and apply these concepts to develop robust computational methods for solving real-world challenges.

ECMM426 Computer Vision

Covers the fundamentals of computer vision, including image processing, feature extraction, and object recognition. Students learn to develop algorithms that enable machines to interpret and understand visual information from the world.

ECMM447 Social Networks and Text Analysis

Examines the analysis of social networks and textual data using computational techniques. This module equips students with tools to study social interactions and extract insights from large volumes of text data.

ECMM450 Stochastic Processes

Introduces the theory and application of stochastic processes, which model systems that evolve with inherent randomness. Students learn to analyze and predict the behaviour of such systems in various contexts.

ECMM461 High Performance Computing

This course focuses on using high-performance computing systems to solve complex computational problems. Students gain practical experience with parallel computing techniques and learn to optimize algorithms for large-scale data processing.

MTHM033 Statistical Modelling in Space and Time

Covers advanced statistical methods for analyzing data that vary across space and time. Students learn to develop models that capture spatial and temporal dependencies, enhancing their ability to make predictions and inform decision-making.

MTHM508 Bayesian Philosophy and Methods in Data Science

This module explores Bayesian methods and their application in data science. It emphasises the philosophical foundations of Bayesian inference and teaches students to apply these techniques to improve decision-making under uncertainty.

SOCM033 Data Governance and Ethics

This course examines the ethical and legal considerations in data science, including data privacy, security, and governance. Students learn to navigate the complex data regulation landscape and develop responsible data management practices.

COMM510 Multi-Objective Optimisation and Decision Making

Introduces techniques for optimising multiple conflicting objectives simultaneously. Students learn to develop and implement algorithms that balance trade-offs, and support informed decision-making in complex scenarios.

COMM109 Programme with Python

Provides a comprehensive introduction to programming with Python, focusing on its applications in data science. Students learn to write efficient code, manipulate data, and develop algorithms using this versatile programming language.

BEMM190 Digital Transformation

Explores the impact of digital technologies on business and society. Students study strategies for leveraging digital tools to drive innovation and improve organizational performance, preparing them for leadership roles in the digital economy.

Core Courses

COMM514 Research Project

The research project is a significant component of the program. It allows students to conduct in-depth research on a data science topic of their choice. Under the guidance of a mentor, students apply their knowledge to solve real-world problems and contribute original findings to the field.

ECMM443 Introduction to Data Science

This module provides a comprehensive overview of data science, covering fundamental concepts, tools, and techniques. Students learn the basics of data manipulation, analysis, and visualization, building a solid foundation for advanced study and practice in the field.

ECMM445 Learning from Data

This course focuses on extracting meaningful insights from data through various analytical methods. Students explore regression, classification, and clustering techniques, developing the skills to analyze and interpret complex data sets effectively.

COMM108 Data Systems

Covers the architecture and management of data systems, including databases, data warehousing, and big data technologies. Students learn to design, implement, and maintain robust data systems that support efficient data storage, retrieval, and processing.

ECMM422 Machine Learning

Introduces the principles and practices of machine learning, including supervised and unsupervised learning algorithms. Students gain hands-on experience with model building, evaluation, and optimisation, preparing them to apply machine learning techniques to real-world data science problems.

Specialization

Computer Science

This specialization provides a deep dive into computer science's theoretical and practical aspects. Students explore advanced topics such as algorithms, software development, and computational theory, gaining a strong foundation for innovative problem-solving and technology development in various industries.

Data Science & Big Data

This specialisation focuses on analysing and managing large data sets and covers data mining, big data technologies, and predictive analytics. Students learn to handle, process, and extract valuable insights from vast amounts of data, preparing them for data-driven decision-making and strategic planning roles.

Clubs And Association

Data Science Society: This society focuses on the exploration and application of data science through workshops, guest lectures, and collaborative projects.
Tech and Coding Society: This organization allows students to enhance their coding skills and engage in tech-related projects and hackathons.
Artificial Intelligence Society: This society explores AI technologies and their applications through seminars, coding sessions, and collaborative research projects.
Maths Society: This society encourages interest in mathematics through activities such as guest lectures, problem-solving sessions, and social events.
Women in STEM: This organization supports female students in science, technology, engineering, and mathematics through networking events, workshops, and mentorship programs.
Entrepreneurship Society: This organization fosters the entrepreneurial spirit by organising workshops, networking events, and startup pitch competitions.
Indian Society: This organisation celebrates Indian culture through events like Diwali, Holi, and Bollywood nights, providing a home-away-from-home experience.
South Asian Society: This organisation engages in cultural exchange and social activities, celebrating students' heritage from South Asian backgrounds.

Extra Activites

Raise and Give (RAG)

Raise and Give (RAG) is the student fundraising group at the University of Exeter, known for its significant contributions to various charitable causes. In the 2016/17 academic year, RAG raised an impressive £255,982 through the dedication of over 1,000 student volunteers.

RAG hosts various events annually, from marathons to international fundraising excursions, including treks to iconic locations such as Machu Picchu and Everest Base Camp.

Raise and Donate (RAD)

The RAD Society is actively involved in supporting both local and global charities. This year, they focused on fundraising for Save the Children, completing a 24-hour bikeathon that raised over £1,200.

The society is dynamic, with members selecting new charitable causes to support each year, ensuring their efforts remain relevant and impactful. This approach allows students to engage deeply with philanthropic activities, fostering a strong sense of community and social responsibility.

Exeter MSc in Data Science Acceptance Rate

The University of Exeter's data science programme boasts a competitive acceptance rate of 70%, reflecting a rigorous yet accessible admissions process. This relatively high acceptance rate indicates a welcoming approach to diverse academic backgrounds, making it an attractive option for graduates from non-computing fields eager to transition into the data science industry.

Application Documents for Exeter MSc in Data Science

For the University of Exeter's MSc in Data Science application, applicants must submit several essential supporting documents:

Transcript: A comprehensive academic transcript detailing previous qualifications and grades.

English Language Proficiency Results: Evidence of meeting the university's English language requirements, typically through tests like IELTS or TOEFL.

One Completed Reference: A reference from an academic or professional referee who can assess the applicant's suitability for postgraduate study.

Personal Statement: A personal statement tailored to the MSc application, focusing on linking past experiences with future aspirations, demonstrating relevant skills, and expressing enthusiasm for the program at the University of Exeter.

Mandatory Application Documents

Passport
College Transcript
IELTS/TOEFL/DTE/PTE

Program Application URL:https://srs.exeter.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=EMPTDFC32P&code2=0017

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Exeter MSc in Data Science Employment

Graduates of the MSc Data Science program at the University of Exeter have a variety of career paths available to them. They can work as data analysts, machine learning engineers, data scientists, business intelligence analysts, database developers or administrators, software engineers, and researchers.

The specific career opportunities for each graduate will depend on their individual skills, interests, and experience. Graduates can find work in a wide range of industries, including technology, finance, healthcare, government, and academia, among others.

Companies Recruiting Exeter MSc in Data Science Graduates

Amazon

Google

Facebook

Microsoft

IBM

Deloitte

KPMG

EY

PwC

Accenture

NHS (National Health Service)

Government agencies (e.g., GCHQ, MI5, MI6)

Universities and research institutions.

Employment Overview URL: https://www.exeter.ac.uk/study/postgraduate/courses/datascience/datascimsc/#careers

Letter of Recommendation for Exeter MSc in Data Science

Applicants to the program are required to submit at least one reference from an academic referee capable of assessing their academic performance and suitability for postgraduate study. Alternatively, references from other credible sources demonstrating the applicant's readiness for postgraduate education and alignment with their chosen program, such as an employer or colleague, will be considered if an academic reference is not available.

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Statement of Purpose for Exeter MSc in Data Science

The University of Exeter's MSc in Data Science requires a well-crafted Statement of Purpose (SOP) within a concise 500-word limit. Applicants should align their SOP closely with the program's expectations, demonstrating a clear understanding of the course content and how their academic background, skills, and experiences have prepared them for postgraduate study in data science.

It's crucial to articulate why this program at Exeter is the right fit, emphasizing personal and academic motivations, future career aspirations, and a genuine enthusiasm for the subject. The SOP should include a strong introduction, a detailed main body, and a conclusive summary highlighting their suitability for the program.

Exeter MSc in Data Science Scholarships available for International Students

Global Excellence Scholarships

Global Excellence Scholarships are designed to support students aiming to develop their academic potential and join our vibrant community of students and staff from over 150 countries.

These scholarships acknowledge outstanding academic achievement and help recipients access our exceptional teaching and learning resources. Additionally, they allow scholars to engage with university life, supporting their future ambitions fully.

Green Futures Scholarships

The University of Exeter offers Green Futures Postgraduate Taught Scholarships for selected Master's programmes in 2024/25, including two Jusoor-Exeter Scholarships. These scholarships are aimed at supporting exceptional candidates from low—and lower-middle-income countries in achieving their academic and career objectives.

What makes Exeter MSc in Data Science unique?

The University of Exeter Business School stands out for several reasons:

Triple Crown Accreditation: The school is one of a select few globally to hold triple accreditation from AACSB, AMBA, and EQUIS, demonstrating its commitment to excellence.

International Focus: The school has a highly diverse student body from over 80 countries and maintains partnerships with 60 leading business schools across Europe and Asia. This global perspective is woven throughout the curriculum.

Sustainability Focus: The Exeter MBA program uniquely focuses on sustainability, preparing students to lead organisations in tackling environmental and social challenges.

Innovative Facilities: The school has invested heavily in cutting-edge learning spaces, such as the Creative Quadrant, a versatile area designed for interdisciplinary problem-solving, and the FEELE Lab for experimental economics research.

Employability: Exeter consistently ranks near the top for graduate employability in the UK. The school works closely with employers to ensure programs are relevant and students gain valuable work experience through internships and placements.

Research Excellence: Faculty are at the forefront of research in their fields, informing the curriculum. The school is part of the prestigious Russell Group of research-intensive UK universities.

In summary, Exeter's global outlook, sustainability focus, innovative teaching, strong industry ties, and research excellence make it a unique and attractive option for business education.

Exeter MSc in Data Science Contact Information

Whom should I contact in case of any doubts?

If you have any doubts or need further information about the Data Science programme, you can email outlook.office365.com/mail or contact the university directly by phone at +44 (0) 1392 661000. These contact points will connect you with the admissions team and academic advisors, who can provide detailed assistance and address any queries.

Conclusion: Should you apply to Exeter MSc in Data Science?

The University of Exeter's MSc in Data Science program stands out for its rigorous curriculum and commitment to fostering future leaders in data analytics. With a competitive acceptance rate, the program attracts top-tier students who benefit from a blend of theoretical knowledge and practical skills essential for success in today's data-driven world.

At Exeter, students can access a vibrant academic environment supported by state-of-the-art facilities and experienced faculty. Beyond academics, the program offers many extracurricular activities, clubs, and associations that enrich the student experience and foster personal and professional growth.

Whether you want to advance your career or delve deeper into data-driven decision-making, Exeter's MSc in Data Science promises a transformative educational experience with promising career prospects.

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