Program Overview

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Our MSc Applied Data Science is a conversion course specifically designed for students without prior experience of university-level mathematics or statistics, who want to be part of our fast-growing digital economy (students with some mathematical experience may consider our MSc Data Science and its Applications ). The course will build upon your undergraduate degree in the humanities, social or life sciences, or business studies, giving you postgraduate-level skills in essential data science methods with various applications, covering case studies and applications of AI and data using a balance of methods and practical application. The course introduces you to programming with the R language and as well as text analytics. Relational databases and SQL are developed and used for relevant applications from humanities, life sciences, linguistics, marketing and social science. The course encourages statistical thinking by data visualisations and guides you to develop your creativity within a scientific framework. You cover topics such as:

• Using R for statistical modelling and decision making
• Linear and generalised linear models are used for experimental and observational data
• Artificial intelligence
• Deep and statistical learning
• Applied statistics
• Information retrieval
• Digital economy
• Survey sampling

The leading department on this course, our Department of Mathematical Sciences , is genuinely innovative and student-focused. Our research groups are working on a broad range of collaborative areas tackling real-world issues. The Department of Mathematical Sciences and our School of Computer Science and Electronic Engineering are working together with other departments across the University to deliver optional modules and summer projects with Essex Business School, the Department of Language and Linguistics, the School of Life Sciences, the School of Philosophy and Art History, and the Department of Psychology. Our course also benefits from many Knowledge Transfer Partnerships which support students through placements and an interdisciplinary outreach culture. The University of Essex is committed to transformational education and inclusion, focused on learning opportunities for every student, responsive to our students’ needs and aspirations. Our MSc Applied Data Science reflects this by supporting every student, from every background, and removing the barriers to their education. This course is developed in collaboration with industry partners and public sector organisations, which include BT, Profusion, Essex County Council, Essex Police, and Suffolk County Council. Our active links with industry can broaden your employment potential and offer placement opportunities. Suitably-qualified applicants may be eligible for a scholarship as a stipend or towards their tuition fees. This course is available on a full- and part-time basis, starting in October. You can also start this course in January, but this option is only available to those who wish to study full-time. Why we're great.

• We are international leaders in data science education for the digital industry.
• We offer you access to specialist research facilities such as the UK Data Archive and our Institute for Social and Economic Research (ISER), both located on campus.
• We have active links with industry to broaden your employment potential and placement opportunities.

Our expert staff

Today’s data scientists are creative people who are focused and committed, yet restless and experimental. We are home to many of the world’s top scientists, and our staff are driven by creativity and imagination as well as technical excellence. We conduct world-leading research in areas such as artificial intelligence, explorative data analysis, machine learning, classification and clustering, evolutionary computation, data visualisation and financial forecasting. Specialist staff at Essex working on data science across our departments include:

• Dr Yanchun Bao – longitudinal and survival analysis, causal methods, instrumental methods (Mendelian Randomization), covariance modelling, mediation analysis
• Professor Luca Citi – machine learning, learning from biological signals and data (EEG, etc)
• Professor Edward Codling - animal movement and dispersal, random walks and diffusion, path analysis of movement data, behaviour of animal groups, human crowd behaviour
• Dr Stella Hadjiantoni – estimation of large-scale multivariate linear models and applications, numerical methods for the development of recursive regularisation and machine learning algorithms, numerical linear algebra in statistical computing and data science, numerical methods for handling high-dimensional data sets
• Dr Andrew Harrison – bioinformatics, big data science
• Professor Berthold Lausen – biostatistics, classification and clustering, data science education, event time data, machine learning, predictive modelling
• Dr Osama Mahmoud – biostatistics, data science, machine learning, Mendelian Randomization
• Dr Yassir Rabhi – mathematical statistics, mathematical foundations of data science
• Professor Abdel Salhi – optimisation mathematical programming and heuristics (evolutionary computing, nature-inspired algorithms, the Strawberry Algorithm), numerical analysis data mining (big data) bioinformatics
• Dr Dmitry Savostyanov – high-dimensional problems, tensor product decompositions
• Dr Alexei Vernitski – machine learning in mathematics; reinforcement learning applied to knot theory; mathematical education, and in particular, increasing motivation of learners of mathematics
• Dr Spyros Vrontos – actuarial mathematics and actuarial modelling
• Dr Jackie Wong Siaw Tze – Bayesian estimation, MCMC methods
• Dr Xinan Yang – approximate dynamic programming, Markov decision process

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Specialist facilities

• All computers run either Windows 10 or are dual boot with Linux
• Software includes R, Python, SQL, Hadoop and Sparc
• We also have specialist facilities for research into areas including non-invasive brain-computer interfaces, intelligent environments, robotics, optoelectronics, video, RF and MW, printed circuit milling, and semiconductors
• Collaborate with the Essex Institute of Data Analytics and Data Science (IADS) and the ESRC Business and Local Government (BLoG) Data Research Centre of the University of Essex
• The UK Data Archive and the Institute for Social and Economic Research (ISER) at Essex contribute to our internationally outstanding data science environment

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Your future

With a predicted shortage of data scientists, now is the time to future-proof your career. Applied data scientists with undergraduate skills in the humanities, social or life sciences are required for the designing and carrying out of statistical analysis or mining data, so our course opens the door to almost any industry, from health, to government, to publishing. Our graduates are highly sought after by a range of employers and find employment in financial services, scientific computation, decision making support and government, risk assessment, statistics, education and other areas. Our recent graduates have gone onto work as data scientists and data analysts in both the private and public sectors. We also offer supervision for PhD, MPhil and MSc by Dissertation. We additionally work with our Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.