MSc Applied Data Science

Our MSc Applied Data Science is a conversion course specifically designed for students with a background in humanities, social sciences, life sciences and business studies who want to be part of our fast-growing digital economy. The course will build upon your undergraduate degree in the humanities, social or life sciences, 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 conversion course is the perfect gateway for your career in data science and has been developed with our industrial partners (who include BT, Profusion, Essex County Council, Essex Police and Suffolk County Council) with employability in mind.

Why we're great

• We are international leaders in data science education for the digital industry
• We are Top 30 for mathematics (The Times and Sunday Times Good University Guide 2020)
• 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 Hongsheng Dai – Bayesian computational statistics, perfect Monte Carlo sampling, mixture models, graphical models, diffusion models, queuing models, distributed deep learning
• Professor Maria Fasli – machine learning, adaptation, semantic information extraction, ontologies, data exploration, recommendation technologies
• Dr Mario Gutierrez-Roig – complex systems, behavioural economics, computational archaeology
• 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 Fanlin Meng – machine learning, game theory, optimisation, distributed learning, privacy-preserving learning, and much more
• 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

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

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.