Data Science with an Industrial Placement - MSc

Program Overview

The MSc Data Science program at Kent is tailored for individuals with limited prior knowledge, providing a solid foundation in data science theory. Led by experts, students learn cutting-edge machine learning and deep learning methods while developing transferable skills essential for success in the field. The program offers an optional industrial placement to enhance employability and prepare graduates for careers in various industries where data is prevalent.

Program Outline

Taught by experts in the field, you'll use real-world data to learn the technical, practical and transferrable skills needed to be a successful data scientist. This conversion course is designed for those with limited previous knowledge of data science, statistics and computing: you don't need a background in the subject as we start with the foundations of data science theory. Artificial intelligence and machine learning innovations have made data processing faster and more efficient. Industry demand has created job positions in organisations around the world within the field of data science. Due to the skillset and expertise required, this looks likely to continue over the coming decades.

• The course was designed in consultation with external organisations; they told us which skills and experience they look for in potential employees.
• You’ll be taught key technical skills that employers expect from their employees, including Python and R programming and the use of machine learning and deep learning methods.
• You’ll develop the transferrable skills you need to be successful in any field.
For example, by the end of the course, we expect that you’ll be much more confident in presenting your findings in various ways to other people.
• The industrial placement allows you to gain valuable work experience and contacts.
• Our teaching staff are research-active and have a wide range of experience.
Our expertise covers the broad subject of data science.

About the School of Mathematics, Statistics and Actuarial Science (SMSAS):

The School has a strong reputation for world-class research and a well-established system of support and training with a high level of flexibility, and frequent contact between staff and research students. Postgraduate students develop analytical, communication and research skills. Developing computational skills and applying them to mathematical problems forms a significant part of the postgraduate training in the School, setting you up for a great career.

Assessment:

Assessments include extensive analyses of complex real-world data. There will be unseen written examinations but most of the credit is based on coursework, including an independent project and report supported by an academic supervisor. Assessments will involve working with messy data where analyses need to be presented to 'clients', group work on coding projects, and development of data visualisation tools that could be used by a particular organisation. There will be exposure to assessments that are relatively low stakes, such as project plans and presentations. The technology to be used to undertake assessment will be standard: any computer with a modern specification will be appropriate.

Teaching:

Our expert teaching staff are leaders in their field who use innovative teaching methods to bring the subject to life. Programme aims and learning outcomes are set to ensure you get the most from your study.

Careers:

You will be a trained data scientist, equipped to work in many fields.

Other:

• Continuation to industrial placement: The placement consists of two modules: Industrial Placement Experience and Industrial Placement Report.
The Industrial Placement lasts 12 months. The Experience module is assessed as pass/fail only and the Report module is graded on a categorical scale. Any student who does not complete both the Industrial Placement Experience module and the Industrial Placement Report module will be transferred to the equivalent non-Year in Industry programme, and the Year in Industry will not be taken into account for the purposes of calculating the award.
• Programme aims: The course aims to:
• Give students the depth of technical knowledge and skills appropriate to masters-level students in Data Science.
• Develop a variety of advanced intellectual and transferable skills, including lifelong learning skills.
• Equip students with a comprehensive and systematic understanding in theoretical and practical Data Science.
• Provide students with a deep understanding of ethical considerations related to the subject.
• Develop students' capacity for rigorous reasoning and precise expression.
• Develop students' capabilities to formulate and solve problems relevant to Data Science.
• Develop students' appreciation of recent developments in Data Science and of the links between theory and practical application.
• Develop in students a logical, systematic approach to solving problems.
• Develop in students an enhanced capacity for independent thought and work.
• Provide students with opportunities to study advanced topics in Data Science, and to engage in research.
• Develop students' communication and personal skills.
• Provide successful students with the depth of knowledge required to enter a career as a professional Data Scientist.
• Enhance the career prospects of graduates seeking employment in the sector.
• Identify, evaluate, and make decisions based on the professional, legal, social, cultural, and ethical issues related to data science
• Identify and apply the concepts and principles behind data science methods for a range of data science paradigms
• Identify, synthesise and apply different statistical concepts and methods
• Evaluate and justify the use of data science in particular subject areas, and the importance of the role of data science in those areas
• Intellectual skills:
On completion of the course students will be able to:
• Apply data science methods systematically and accurately to manipulate data.
• Interpret and develop solutions for complex issues related to data science systematically, logically and creatively
• Act with self-direction and originality in tackling and solving problems
• Develop and apply solutions in the absence of complete data
• Formulate and apply solutions for a substantial research or development-based project and to report the work clearly in the form of a project report
• Subject-specific skills:
On completion of the course students will be able to:
• Undertake practical work that explores techniques covered in the course and to analyse and comment on the findings
• Identify, evaluate and apply advanced data science concepts to formulate solutions to data science problems.
• Select, apply and evaluate modelling and machine learning techniques.
• Extract and synthesise the essentials of problems to facilitate analysis and interpretation.
• Transferable skills: On completion of the course students will be able to:
• Plan, work and study independently and use relevant resources in a manner that reflects good practice
• Manage their time including the ability to manage their own learning and development
• Appreciate the importance of continued professional development as part of lifelong learning
• Work effectively as a member of a team
• Communicate technical issues clearly to specialist and non-specialist audiences
• Present ideas, arguments and results in the form of a well-structured written report
• Act autonomously in planning and implementing tasks at a professional or equivalent level
• Industrial Placement only.
Apply in a working environment the knowledge and skills gained through academic study
• Study support:
• Postgraduate resources:
Kent’s Computing Service central facility runs Windows. Within the School, postgraduate students can use a range of UNIX servers and workstations. Packages available include R, MATLAB, SPSS, STAN and Python.
• Dynamic publishing culture: Staff publish regularly and widely in journals, conference proceedings and books.
Among others, they have recently contributed to: Annals of Statistics; Biometrics; Biometrika; Journal of Royal Society, Series B; Statistics and Computing.
• Global Skills Award: All students registered for a taught Master's programme are eligible to apply for a place on our Global Skills Award Programme.
The programme is designed to broaden your understanding of global issues and current affairs as well as to develop personal skills which will enhance your employability.
• Research:
• Statistical Ecology:
There has been research in the area of statistical ecology at Kent for many years. We are part of the National Centre for Statistical Ecology (NCSE), which was established in 2005.
• Bayesian statistics: The research conducted in this area at Kent is mainly on Bayesian variable selection, Bayesian model fitting, Bayesian nonparametric methods, Markov chain Monte Carlo with applications.
• Biological and health statistics: Research is focused on statistical modelling and inference in biology and genetics with applications in complex disease studies.
Over the past few decades, large amounts of complex data have been produced by high through-put biotechnologies. The grand challenges offered to statisticians include developing scalable statistical methods for extracting useful information from the data, modelling biological systems with the data, and fostering innovation in global health research.
• Machine learning: This theme encompasses both theory and applications.
Theory is involved with supervised and unsupervised learning, matrix factorisation, modelling of high-dimensional time series, differential privacy, deep learning and networks, shape analysis and statistics on manifolds, and neuroimaging. Applications in biology, industry, medicine and psychiatry. Often new computational methods are the key to analysing complex big data problems.
• Nonparametric statistics: In order to describe the data, it is common in statistics to assume a specific probability model.
Nonparametric methods provide statistical tools for addressing inference in these situations.
• Economics and finance: At Kent there is particular interest in the use of nonparametric methods including quantile regression and Bayesian nonparametric approaches.
Application areas include modelling of the business cycle and capacity utilisation, calculating sovereign credit ratings, modelling of stock return data, and predicting inflation.

The 2024/25 annual tuition fees for this course are: Full-time UK £9,800 EU £22,700 International £22,700 For students continuing on this programme fees will increase year on year by no more than RPI + 3% in each academic year of study except where regulated.