How to apply

Overview

The details

Course: Applied Data Science

Start date: January 2025

Study mode: Full-time

Duration: 1 year

Location: Colchester Campus

Based in: Mathematics, Statistics and Actuarial Science (School of)

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 School of Mathematics, Statistics and Actuarial Science, is genuinely innovative and student-focused. Our research groups are working on a broad range of collaborative areas tackling real-world issues. The School of Mathematics, Statistics and Actuarial Science 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 Philosophical, Historical and Interdisciplinary Studies, 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

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 international equivalent, in any discipline.

You may also be considered with a lower-class degree, if you have three year’s relevant work experience (please provide your CV).

Applicants with a strong background in Science, Technology, Engineering & Mathematics (STEM) may be considered for the MSc Data Science and It's Applications. We encourage you to consider this as a choice if you come from a STEM background.

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 contact our Graduate Admissions team at pgquery@essex.ac.uk to request the entry requirements for this country.

English language requirements

If English is not your first language, we require IELTS 6.0 overall with a minimum component score of 5.5 in all components.

If you do not meet our IELTS requirements then you may be able to complete a pre-sessional English pathway that enables you to start your course without retaking IELTS.

Additional Notes

The University uses academic selection criteria to determine an applicant’s ability to successfully complete a course at the University of Essex. Where appropriate, we may ask for specific information relating to previous modules studied or work experience.

Structure

Course structure

We offer a flexible course structure with a mixture of core/compulsory modules, and optional modules chosen from lists.

Our research-led teaching is continually evolving to address the latest challenges and breakthroughs in the field. The course content is therefore reviewed on an annual basis to ensure our courses remain up-to-date so modules listed are subject to change.

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 and in line with your contract with us. However, if we need to make material changes, for example due to significant disruption, 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:

100

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: COMPULSORY WITH OPTIONS

(15 CREDITS)

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

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 04: COMPULSORY

Databases and data processing with SQL

(15 CREDITS)

Relational databases and SQL are fundamental tools for applications in many different disciplines including humanities, life sciences, linguistics, marketing and social science. They are essential in almost all modern organisations for efficient information management in IT systems and commercial applications. The purpose of this module is to provide you with an introduction to the underlying principles of and practical experience in designing and implementing relational databases. It will cover 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 05: 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 06: COMPULSORY

Modelling experimental and observational data

(15 CREDITS)

This module covers the principles of linear modelling for analysing experimental and observational data. You will first study the assumptions of the general linear model, including collinearity, influential data, assessing fitted models and model selection techniques. You will then encounter statistical methods for efficient analysis of experiments with normally distributed data, such as one-way ANOVA, and extend the methodology to logistic regression and analysis of contingency tables with categorical variables of interest. Finally, you will study multivariate methods for the analysis of large, high-dimensional data sets.

View Modelling experimental and observational data on our Module Directory

COMPONENT 07: COMPULSORY

Artificial intelligence and machine learning with applications

(15 CREDITS)

This module introduces Artificial Intelligence (AI), the science of making computers and machines display intelligent behaviour. This multidisciplinary activity draws from computer sciences, mathematics and statistics, and also elements of philosophy, logic and even psychology. Today, AI is ubiquitous in society, from self-driving cars to spam filters and finance trading to video games. The increasing dependence on AI will reshape society and economy. Understanding AI principles, applications, and limitations is important for all students, regardless of their background, and this module assumes no prior knowledge. This module provides both theoretical and practical techniques, covering AI theory and fundamentals of machine learning models, as well as their implementation and applications.

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

COMPONENT 08: COMPULSORY

Foundational Mathematics for Data Science

(15 CREDITS)

In this module you will be introduced to the necessary mathematical foundations and tools for working with data. You will become familiar with standard mathematical notation and concepts, and with common techniques such as differentiation, integration, metrics, vectors and matrices.

View Foundational Mathematics for Data Science on our Module Directory

COMPONENT 09: COMPULSORY

Research Skills and Employability

(0 CREDITS)

What skills do you need to succeed during your studies? What about after university? How will you harness your knowledge and soft skills to realise your career goals? This module helps you take an active role in developing transferrable skills and capitalising on your unique background. As well as broad reflection on your professional development, this module will help you explore different career directions and prepare you for the application process, supported by an advisor from within the department.

View Research Skills and Employability on our Module Directory

COMPONENT 01: CORE WITH OPTIONS

(60 CREDITS)

Teaching

Postgraduate Taught students in the School of Mathematics, Statistics and Actuarial Science typically attend two hours of lectures and one class/lab every week, but this will vary dependent upon the module
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

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

£10,000

International fee

£21,700

Masters fees and funding information

Research (e.g. PhD) fees and funding information

What's next

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.

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.

Applicants with an undergraduate degree from our School of Mathematics, Statistics and Actuarial Science, or who are working towards one, should first contact our admissions staff: maths@essex.ac.uk.

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