Postgraduate Course

MSc Data Science and its Applications

MSc Data Science and its Applications

Overview

The details
Data Science and its Applications
October 2022
Full-time
1 year
Colchester Campus

Our MSc Data Science and its Applications is a conversion course specifically designed for students wanting to be part of our fast-growing digital economy but do not have the sufficient programming and statistics skills. This course you will place you at the core of data science with the application of theory and methods to real-world problems, specifically to use and exploit big data.

At Essex, the techniques we use to model and manipulate data guide the political, financial and social decisions that shape our modern society and are the basis of growth of the economy and success of businesses.

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:

  • Modelling experimental data
  • Machine learning and decision making
  • Applied statistics
  • Combinatorial optimisation
  • Statistical methods
  • Stochastic processes

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 Data Science and its Applications course 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.

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 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. A successful career in data science requires you to possess truly interdisciplinary knowledge and our staff ensure that you graduate with a wide-ranging, yet specialised, set of skills in this area.

Data scientists are required in every sector, carrying out statistical analysis or mining data on social media. Our graduates are highly sought after by a range of businesses and organisations 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

Course structure

Our research-led teaching is continually evolving to address the latest challenges and breakthroughs in the field. The following modules are based on the current course structure and may change in response to new curriculum developments and innovation.

The 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.

We understand that deciding where and what to study is a very important decision for you. We’ll make all reasonable efforts to provide you with the courses, services and facilities as described on our website. However, if we need to make material changes, for example due to significant disruption, or in response to COVID-19, we’ll let our applicants and students know as soon as possible.

Components and modules explained

Components

Components are the blocks of study that make up your course. A component may have a set module which you must study, or a number of modules from which you can choose.

Each component has a status and carries a certain number of credits towards your qualification.

Status What this means
Core
You must take the set module for this component and you must pass. No failure can be permitted.
Core with Options
You can choose which module to study from the available options for this component but you must pass. No failure can be permitted.
Compulsory
You must take the set module for this component. There may be limited opportunities to continue on the course/be eligible for the qualification if you fail.
Compulsory with Options
You can choose which module to study from the available options for this component. There may be limited opportunities to continue on the course/be eligible for the qualification if you fail.
Optional
You can choose which module to study from the available options for this component. There may be limited opportunities to continue on the course/be eligible for the qualification if you fail.

The modules that are available for you to choose for each component will depend on several factors, including which modules you have chosen for other components, which modules you have completed in previous years of your course, and which term the module is taught in.

Modules

Modules are the individual units of study for your course. Each module has its own set of learning outcomes and assessment criteria and also carries a certain number of credits.

In most cases you will study one module per component, but in some cases you may need to study more than one module. For example, a 30-credit component may comprise of either one 30-credit module, or two 15-credit modules, depending on the options available.

Modules may be taught at different times of the year and by a different department or school to the one your course is primarily based in. You can find this information from the module code. For example, the module code HR100-4-FY means:

HR 100  4  FY

The department or school the module will be taught by.

In this example, the module would be taught by the Department of History.

The module number. 

The UK academic level of the module.

A standard undergraduate course will comprise of level 4, 5 and 6 modules - increasing as you progress through the course.

A standard postgraduate taught course will comprise of level 7 modules.

A postgraduate research degree is a level 8 qualification.

The term the module will be taught in.

  • AU: Autumn term
  • SP: Spring term
  • SU: Summer term
  • FY: Full year 
  • AP: Autumn and Spring terms
  • PS: Spring and Summer terms
  • AS: Autumn and Summer terms

COMPONENT 01: CORE

Dissertation
(60 CREDITS)

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

COMPONENT 02: COMPULSORY

Data Visualisation
(15 CREDITS)

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 Data Visualisation on our Module Directory

COMPONENT 03: COMPULSORY

Data analysis and statistics with R
(15 CREDITS)

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

COMPONENT 04: COMPULSORY

Programming and Text Analytics with R
(15 CREDITS)

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 string processing, text analytics, and sentiment analysis. 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

COMPONENT 05: COMPULSORY

Databases and data processing with SQL
(15 CREDITS)

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

COMPONENT 06: COMPULSORY

Modelling experimental and observational data
(15 CREDITS)

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

COMPONENT 07: OPTIONAL

Option from list
(15 CREDITS)

COMPONENT 08: COMPULSORY WITH OPTIONS

CE156-7-AU or MA214-7-SP
(15 CREDITS)

COMPONENT 09: COMPULSORY WITH OPTIONS

MA336-7-SP or CE802-7-SP
(15 CREDITS)

COMPONENT 10: COMPULSORY

Research Skills and Employability
(0 CREDITS)

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 Research Skills and Employability 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

Assessment

  • On this course you are assessed mostly by coursework and projects, but this does vary from module to module
  • Some modules may also incorporate written examinations

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

£9,200

International fee

£19,740

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.

2022 Open Days (Colchester Campus)

  • Saturday, September 17, 2022
  • Saturday, October 22, 2022

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.

Set within the 200-acre award-winning beautiful parkland - Wivenhoe Park and located two miles from the historic city centre of Colchester – England's oldest recorded development. Our Colchester Campus is also easily reached from London and Stansted Airport in under one hour.

Whether you are planning to visit us at one of our Open Days, or coming to an Applicant day. Our campus conveniently located and easy to reach by car, train or bus.

View from Square 2 outside the Rab Butler Building looking towards Square 3

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 University would inform and engage with you if your course was to be discontinued, and would provide you with options, where appropriate, in line with our Compensation and Refund Policy.

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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