MSc DATA SCIENCE (PROFESSIONAL)
DATA SCIENCE AT THE UNIVERSITY OF EXETER
Contact Conrad Gillespie at c.gillespie@exeter.ac.uk or on 01392 723578 Sarah Brooks at s.brooks2@exeter.ac.uk or on 01392 723693
A growth area with excellent career development potential The University of Exeter’s MSc Data Science (Professional) is an innovative taught course targeted at employed professionals. We offer the programme in 2 year or 3 year formats, to give flexibility for students with differing commitments. The taught modules are delivered on campus at the University of Exeter and on site at our industry partner organisations. Intensive oneweek residentials incorporate lectures, practicals, seminars, group work, tools and tech insights and guest lectures. Students are closely supported by academic and employer mentors who support individual self-study, online interaction and work-based projects. Our industry partners have contributed to course design, ensuring that we have a distinctive industry-facing approach relevant to modern business needs. The programme provides commercial and public sector organisations with an opportunity to develop, reward and retain talented data scientists, bringing cutting-edge knowledge and skills into the organisation. We recognise that all students come with their own unique set of skills and knowledge, and this is reflected in the module selection with content covering fundamental mathematical and computational techniques, machine learning and statistical modelling approaches to create insights from complex datasets, as well as the underpinning tools and platforms, wider social context and specific applications of data science in diverse areas. Additionally, assignments are focused around the student’s employer and work commitments to ensure that content is always relevant to, and benefits, their organisation.
www.exeter.ac.uk/datascience
PROGRAMME MODULES Core Modules Students take 5 compulsory modules, 2 compulsory research projects and 3 optional modules.
1. Introduction to Data Science Learn about the broad and fastmoving field of data science and explore the ways in which data science is transforming business and society. Develop fundamental skills in programming, data-handling and visualisation, and learn context and vocabulary to support later, more detailed, study.
2. Fundamentals of Data Science Data science depends on a solid grounding in mathematics and programming. Develop core mathematical, programming and computational skills essential for further study, such as linear algebra, probability and other common computational methods. Learn how to process large datasets using analysis packages and optimisation techniques.
3. From Data to Decisions Use machine learning and statistical modelling methods to effectively use data to make better decisions. Deal with real data to understand the theory and practice of the principal learning paradigms and apply machine learning methods such as classification, regression and unsupervised learning. Use, modify and write software to visualise data to help make better decisions.
4. Data in Business and Society Understand the data science revolution and its impact on industry, business and the public sector. The social context of data science, including ethical issues, privacy, governance, legal frameworks and legislation, is essential background for any data scientist. Learn how to collect and use data effectively to develop strategy and how to approach planning and analysis challenges.
5. Machine Learning and Statistical Modelling Draw on developments in the theory and application of machine learning and statistical modelling to gain a set of fundamental tools to use for modern data analysis, including clustering, classification, pattern recognition, feature extraction and concept acquisition. Use image and speech analysis methods, study applications in medical imaging, bioinformatics, natural science, engineering, government and industry, and learn advanced general approaches to data analysis problems.
Research Project A Apply the knowledge learned in the first year to a significant independent data science project, based in the real-world to provide business relevance. Develop project planning, management and implementation skills as well as those in independent learning, presentation and writing, supported by an academic/ industry supervisory team.
Research Project B Enhance and hone the skills acquired throughout the Masters programme to produce a further advanced data science project focused on the student’s business organisation. Develop a deep understanding of the business requirements and skills needed to produce effective data science projects for genuine applications.
Optional Modules 1. Advanced Machine Learning
4. Machine Vision
Analyse large and complex datasets and create systems that adapt and improve to make data predictions. Learn about the most prominent and effective techniques currently employed in state-of-the-art machine learning systems: artificial neural network and kernel machines. Design predictive systems and apply your models to data of interest within your business.
2. Social Networks and Text Analysis
The Web has created complex, relational datasets which are best understood using a network perspective. Much online data is unstructured text, requiring computational methods for analysis of text at scale. Learn the core principles of network science and text analysis using appropriate tools, then apply them to generate insights from complex networks and large text corpora.
3. Data Governance and Ethics
Consider how complex and powerful technologies such as machine learning, artificial intelligence and big data can be best managed for the benefit of individuals, organisations and society. Understand the conflict of interest between individual privacy and organisational advantage, and the diverse ethical and legal challenges involved.
Learn computer vision and image processing techniques to extract meaningful information from the huge volume of images and video content that is now available, such as medical imaging, satellitebased remote sensing and social media content. Cover the essential challenges and key algorithms for solving a variety of problems related to the automated processing of visual data.
5. Advanced Statistical Modelling
Build on ideas covered in previous modules, looking in greater detail at the concepts and methods of modern statistics. Look at the philosophy and practice of Bayesian inference to acknowledge the inherent uncertainty present in data, model choice, the use of data from multiple sources and the intensive computation required for large datasets.
6. High Performance Computing and Data Architectures
Learn the skills and knowledge to exploit modern computational resources for data-intensive analysis, high-performance computing and how to manipulate large datasets. Study the diverse range of architectures for storing and processing, and cover the core principles underlying the design of software and hardware for handling high demand computation.
7. Information Security
Gain a solid understanding of the vulnerabilities of data collection, storage and communication in modern computer systems, networks and online environments. Explore the foundations of computer security, techniques to secure complex digital systems and gain practical experience in secure management against malicious and criminal exploitation.
Programme Structure Study is delivered in 8 one-week residential blocks and 6 tutorial days, spread over 2 or 3 years. Students will take 8 x 15 credit taught modules and 2 x 30 credit independent research projects, to complete a total of 180 credits to qualify for the MSc.
2 YEAR PROGRAMME
3 YEAR PROGRAMME
Fundamentals of Data Science
Introduction to Data Science
Fundamentals of Data Science
From Data to Decisions
Data in Business and Society
From Data to Decisions
Data in Business and Society
YEAR 1
YEAR 1
Introduction to Data Science
Research Project A Optional Module One
Machine Learning and Statistical Modelling
Optional Module Two
Optional Module Three
Research Project A
Optional Module One
YEAR 2
YEAR 2
Machine Learning and Statistical Modelling
Research Project B Optional Module Two
Optional Module Three
Research Project B
OPTIONAL MODULES (CHOOSE THREE):
Advanced Machine Learning Social Networks and Text Analysis Data Governance and Ethics Machine Vision
Advanced Statistical Modelling High Performance Computing and Data Architectures
Information Security
Entry requirements A good honours degree in a numerate subject from a recognised university. Students are expected to enter this programme with some programming ability. Students with industry experience are actively encouraged to apply irrespective of formal qualifications. We do consider all applications where there is evidence of exceptional performance in modules relevant to the programme of study, significant relevant work experience or professional qualifications.
YEAR 3
Every effort has been made to ensure that this information is correct. The University reserves the right to make variations to programme content and delivery.
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