2020 applicants
Postgraduate Course

MSc Applied Data Science

MSc Applied Data Science

Overview

The details
Applied Data Science
January 2021
Full-time
1 year
Colchester Campus

This course is available to study starting in January 2021 or October 2021; part-time study is only available as part of the October-start option.

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.

Funding for this course available

We are offering a brand new scholarship, funded by the Office for Students. Each scholarship is valued at £10,000, which can be used to pay tuition fees. These scholarships are aimed at groups of people who under-represented in the areas of Data Science and Artificial Intelligence in the UK. To apply for a scholarship, you will need to be holding an offer to study our MSc Applied Data Science. Full details about this scholarship and information on how to apply can be found on our website here.

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.
THE Awards 2018 - Winner University of the Year

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, game-theoretic modelling in real-world applications, data-driven optimisation, distributed and large-scale optimisation, smart energy and smart grids, demand flexibility management, microgrids, energy systems modelling, energy markets, local energy trading, demand response / renewable energy integration, energy markets integration, intelligent transportation systems, game-theoretic modelling, autonomous driving, EVs modelling, sustainability, smart buildings, thermal comfort, digital infrastructure, cyber Physical Systems
  • 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.

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.

Entry requirements

UK entry requirements

A 2:2 degree, or equivalent, in any discipline.

International & EU entry requirements

We accept a wide range of qualifications from applicants studying in the EU and other countries. Get in touch with any questions you may have about the qualifications we accept. Remember to tell us about the qualifications you have already completed or are currently taking.

Sorry, the entry requirements for the country that you have selected are not available here. Please select your country page where you'll find this information.

English language requirements

IELTS overall score of 6.0 with a minimum of 5.5 in all components.

Structure

Example structure

Most of our courses combine compulsory and optional modules, giving you freedom to pursue your own interests. All of the modules listed below provide an example of what is on offer from the current academic year. Our Programme Specification provides further details of the course structure for the current academic year.

Our research-led teaching is continually evolving to address the latest challenges and breakthroughs in the field, therefore to ensure your course is as relevant and up-to-date as possible your core module structure may be subject to change.

The example structure below is representative of this course if taken full-time. If you choose to study part-time, the modules will be split across two years.

Please note that if you are studying full-time (either starting in October or January) there is no second year; you will develop your dissertation throughout the course of your single year.

Mathematics Careers and Employability

What skills do you need to succeed during your studies? And what about after university? How will you realise your career goals? Develop your transferable skills and experiences to create your personal profile. Reflect on and plan your ongoing personal development, with guidance from your personal advisor within the department.

View Mathematics Careers and Employability on our Module Directory

Exploratory Data Analysis and Data Visualisation

In a world increasingly driven by data, the need for analysis and visualisation is more important than ever. In this module you will look at data through the eyes of a numerical detective. You will work on the lost art of exploratory data analysis, reviewing appropriate methods for data summaries with the aim to summarise, understand, extract hidden patterns and identify relationships. You will then work on graphical data analysis, using simple graphs to understand the data, but also advanced complex methods to scrutinise data and interactive plots to communicate data information to a wider audience. For data analysis and visualisations you will use R-studio, and a combination of R-shiny applications and google visualisations for interactive plotting.

View Exploratory Data Analysis and Data Visualisation on our Module Directory

Databases and data processing with SQL

Relational databases and SQL are developed and used as a fundamental tool for relevant applications from different disciplines including humanities, life sciences, linguistics, marketing and social science. They are essential to the efficient information management for IT systems and commercial applications in almost all modern organisations. The purpose of this module is to provide you with an introduction to the underlying principles and practical experience of the design and implementation of relational databases. It will cover the data modelling and SQL, database analysis, design and management, and advanced topics including big data, security and privacy issues of modern databases.

View Databases and data processing with SQL on our Module Directory

Data analysis and statistics with R

The module will introduce you to concepts from data analysis and statistics and show how they can be applied effectively via the R language. It will cover a wide introduction to statistics and provide practical experience of real-world examples of how statistics is used to gain insights. Throughout these examples, and many more, we will teach programming techniques that will enable you to apply statistical approaches to real-world applications. This module assumes no previous exposure to statistics.

View Data analysis and statistics with R on our Module Directory

Modelling experimental and observational data

This module will introduce you to the principles for the application of linear modelling methodologies for the analysis of experimental and observational data. The first strand of the module will study the assumptions of the general linear model. Collinearity, influential data, assessing the fitted model and model selection techniques will be discussed. The second strand will introduce statistical methods for the efficient analysis of experiments when the data are normally distributed, for example one-way ANOVA. The methodology will be extended to logistic regression and the analysis of contingency tables when the variable of interest is categorical. The third strand of the module will study various multivariate methods for the analysis of large and high-dimensional data sets.

View Modelling experimental and observational data on our Module Directory

Artificial intelligence and machine learning with applications

Artificial Intelligence is the science of making computers and machines to produce results and behave in a way that resembles human intelligence. This multidisciplinary activity involves the knowledge of different disciplines such as computer science, Mathematics and statistics, but also includes important elements from philosophy, logic and even psychology. Nowadays, AI is well embedded in our society from self-driving cars to spam filters, and from finance trading to video games. All predictions state that more and more of our society will depend on this technology with the consequent transformation of our society and economy. The impact of AI affects any discipline and therefore it is important for everyone to understand its principles, applications and limitations. This module is suitable for any student regardless of their background. This module will provide you with a broad overview of AI, as well as more detailed understanding of core concepts and models. We will follow an approach both theoretical and practical, describing the theory and fundamentals of machine learning models, as well as showing how to implement them and their applications.

View Artificial intelligence and machine learning with applications on our Module Directory

Research Methods

Looking to build your research capabilities? This module will equip you with the principal research tools for your postgraduate course in Mathematical Sciences, including practice in the mathematical word-processing language LaTeX.

View Research Methods on our Module Directory

Dissertation

This is a dissertation module for MSc students. Student will be provided with a list of dissertation titles or your own, provided a member of staff agrees it is of suitable standard and is able to supervise it.

View Dissertation on our Module Directory

Programming and Text Analytics with R

This module will introduce you to the underlying principles and basic concepts of programming with the R language. It will cover a wide range of analytics, provide practical experience of powerful R tools, and present real-world examples of how data and analytics are used to gain insights and to improve a business or industry. These examples include text analytics, Twitter, and IBM Watson. Throughout these examples, and many more, we will teach programming techniques that will enable you to apply advanced data science approaches to real-world applications. This module assumes no prior programming skills.

View Programming and Text Analytics with R on our Module Directory

Applied Statistics (optional)

How do you apply multivariate methods? Or demographical and epidemiological methods? And how do you apply sampling methods? Study three application areas of statistics – multivariate methods, demography and epidemiology, and sampling. Understand how to apply and assess these methods in a variety of situations.

View Applied Statistics (optional) on our Module Directory

Information Retrieval (optional)

Search engines have become the first entry point into a world of knowledge and they form an essential part of many modern computer applications. While much of the underlying principles have been developed over decades, the landscape of search engine technology has changed dramatically in recent years to deal with data sources magnitudes larger than ever before (the rise of 'big data'). As a result of this, new paradigms for storing, indexing and accessing information have emerged. This module will provide the essential foundation of information retrieval and equip students with solid, applicable knowledge of state-of-the-art search technology.

View Information Retrieval (optional) on our Module Directory

Dissertation

This is a dissertation module for MSc students. Student will be provided with a list of dissertation titles or your own, provided a member of staff agrees it is of suitable standard and is able to supervise it.

View Dissertation on our Module Directory

Teaching

  • Core components can be combined with optional modules, to enable you to gain either in-depth specialisation or a breadth of understanding
  • Learn to use LATEX to produce a document as close as possible to what professional mathematicians produce in terms of organisation, layout and type-setting
  • Our postgraduates are encouraged to attend conferences and seminars on a Thursday afternoon

Assessment

  • Courses are assessed on the results of your written examinations, together with continual assessments of your practical work and coursework

Dissertation

  • You will be provided with a list of dissertation titles or topics proposed by staff and it may be possible to propose a project of your own
  • Most dissertations are between 10,000 and 30,000 words in length. However, these are guidelines, not mandatory word counts
  • Close supervision by academic staff

Fees and funding

Home/UK fee

£8,340

EU students commencing their course in the 2020-21 academic year will be liable for the Home/UK fee.

International fee

£17,900

Fees will increase for each academic year of study.

What's next

Open Days

We hold Open Days for all our applicants throughout the year. Our Colchester Campus events are a great way to find out more about studying at Essex, and give you the chance to:

  • tour our campus and accommodation
  • find out answers to your questions about our courses, student finance, graduate employability, student support and more
  • meet our students and staff

If the dates of our organised events aren’t suitable for you, feel free to get in touch by emailing tours@essex.ac.uk and we’ll arrange an individual campus tour for you.

Applying

You can apply for this postgraduate course online. Before you apply, please check our information about necessary documents that we’ll ask you to provide as part of your application.

We aim to respond to applications within two weeks. If we are able to offer you a place, you will be contacted via email.

For information on our deadline to apply for this course, please see our ‘how to apply’ information.

Colchester Campus

Visit Colchester Campus

Home to 15,000 students from more than 130 countries, our Colchester Campus is the largest of our three sites, making us one of the most internationally diverse campuses on the planet - we like to think of ourselves as the world in one place.

The Campus is set within 200 acres of beautiful parkland, located two miles from the historic town centre of Colchester – England's oldest recorded town. Our Colchester Campus is also easily reached from London and Stansted Airport in under one hour.

 

Virtual tours

If you live too far away to come to Essex (or have a busy lifestyle), no problem. Our 360 degree virtual tour allows you to explore the Colchester Campus from the comfort of your home. Check out our accommodation options, facilities and social spaces.

Exhibitions

Our staff travel the world to speak to people about the courses on offer at Essex. Take a look at our list of exhibition dates to see if we’ll be near you in the future.

At Essex we pride ourselves on being a welcoming and inclusive student community. We offer a wide range of support to individuals and groups of student members who may have specific requirements, interests or responsibilities.


Find out more

The University makes every effort to ensure that this information on its programme specification is accurate and up-to-date. Exceptionally it can be necessary to make changes, for example to courses, facilities or fees. Examples of such reasons might include, but are not limited to: strikes, other industrial action, staff illness, severe weather, fire, civil commotion, riot, invasion, terrorist attack or threat of terrorist attack (whether declared or not), natural disaster, restrictions imposed by government or public authorities, epidemic or pandemic disease, failure of public utilities or transport systems or the withdrawal/reduction of funding. Changes to courses may for example consist of variations to the content and method of delivery of programmes, courses and other services, to discontinue programmes, courses and other services and to merge or combine programmes or courses. The University will endeavour to keep such changes to a minimum, and will also keep students informed appropriately by updating our programme specifications.

The full Procedures, Rules and Regulations of the University governing how it operates are set out in the Charter, Statutes and Ordinances and in the University Regulations, Policy and Procedures.

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