How to apply

Overview

The details

Course: Data Science with Professional Placement

Start date: January 2025

Study mode: Full-time

Duration: 2 years

Location: Colchester Campus

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

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. Technology is growing and evolving at an incredible speed, and both the rate of growth of data we generate and the devices we use to process it can only increase.

Data science is a growing and important field of study with a fast-growing number of jobs and opportunities within the private and public sector. The application of theory and methods to real-world problems and applications is at the core of data science, which aims especially to use and to exploit big data.

If you are interested in solving real-world problems, you like to develop skills to use smart devices efficiently, you want to use and to foster your understanding of mathematics, and you are interested and keen to use statistical techniques and methods to interpret data, MSc Data Science at Essex is for you. You study a balance of solid theory and practical application including:

Computer science
Programming
Statistics
Data analysis
Probability

A successful career in data science requires you to possess truly interdisciplinary knowledge, so we ensure that you graduate with a wide-ranging yet specialised set of skills in this area. You are taught mainly within our School of Mathematics, Statistics and Actuarial Science and our School of Computer Science and Electronic Engineering, but also benefit from input from our Essex Business School, and our Essex Pathways Department. Data scientists are required in every sector, carrying out statistical analysis or mining data on social media, so our course can open the door to almost any industry, from health, to government, to publishing.

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. Here are a few examples:

Our data scientists carefully consider how not to lie, and how not to get lied to with data. Interpreting data correctly is especially important because much of our data science research is applied directly or indirectly to social policies, including health, care and education.
We do practical research with financial data (for example, assessing the risk of collapse of the UK's banking system) as well as theoretical research in financial instruments such as insurance policies or asset portfolios.
We also research how physical processes develop in time and space. Applications of this range from modelling epilepsy to modelling electronic cables.
Our optimisation experts work out how to do the same job with less resource, or how to do more with the same resource.
Our pure maths group are currently working on two new funded projects entitled ‘Machine learning for recognising tangled 3D objects' and ‘Searching for gems in the landscape of cyclically presented groups'.
We also do research into mathematical education and use exciting technologies such as electroencephalography or eye tracking to measure exactly what a learner is feeling. Our research aims to encourage the implementation of ‘the four Cs' of modern education, which are critical thinking, communication, collaboration, and creativity.

This course is available to study starting in either October or January.

Why we're great.

We are committed to developing the data scientists of the future.
Our interdisciplinary Institute for Data Analytics (IADS) researches data issues from the scientific and technological, to the sociological and legal.
We have active links with industry to broaden your employment potential and placement opportunities.

Placement year

MSc Data Science with Professional Placement offers a unique opportunity for you to gain relevant work experience within an external business or organisation, giving you a competitive edge in the job market and providing you with key contacts within the industry. The placement is undertaken between the taught part of the course and the individual project. Its aim is to allow you to acquire industry experience and, especially, develop an appreciation of how the skills acquired in the taught part of the course can be applied to real world problems.

You'll be responsible for securing your own work placement, but if you change your mind and decide not to do your placement, or if you are not able to secure a placement, you can start your dissertation earlier and complete your Masters in the first year.

Our expert staff

Today's computer 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 explorative data analysis, classification and clustering, evolutionary computation, data visualisation and financial forecasting. Specialist staff working on data science and analytics include:

Dr Luca Citi – machine learning, learning from biological signals and data (EEG, etc)
Professor Berthold Lausen – biostatistics, classification and clustering, data science education, event time data, machine learning, predictive modelling
Professor Abdel Salhi – data mining, numerical analysis, optimisation
Professor Spyros Vrontos – actuarial mathematics and actuarial modelling
Professor Xinan Yang – approximate dynamic programming, Markov decision process

Specialist facilities

All computers run either Windows 10 or are dual boot with Linux
Software includes Java, Prolog, C++, Perl, Mysql, Matlab, DB2, Microsoft Office, Visual Studio, and Project
You have access to CAD tools and simulators for chip design (Xilinx) and computer networks (OPNET)
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. Data scientists are required in every sector, carrying out statistical analysis or mining data on social media, 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 in one of the following subjects:

Applied Mathematics
Biostatistics
Computer Science
Economic Statistics
Economics
Mathematics
Operational Research
Pure Mathematics
Statistics

Any other 2.2 degree in any subject which includes three modules from the below lists:

One module, from:

Advanced Maths (I/II/III)
Calculus (AKA Mathematical Analysis)
Engineering Maths (I/II/III)
Maths (I/II/III)

And

One module, from:

Advanced Maths (I/II/III)
Engineering Maths (I/II/III)
Maths (I/II/III)
Statistics or Probability

And

One additional relevant module, from:

Advanced Maths (I/II/III)
Algebra
Analysis
Complex Numbers
Differential Equations
Engineering Maths (I/II/III)
Maths (I/II/III)
Numerical Methods
Optimisation (Linear Programming)
Programming Language (R or Matlab or Python)
Regression
Another module in Probability or Statistics
Stochastic Process

Applicants with a degree below 2:2 or equivalent will be considered dependent on any relevant professional or voluntary experience and previous modules studied.

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

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.

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

Applied Regression and Experimental Data Analysis

(15 CREDITS)

This module is concerned with the application of regression models to the analysis of data. The underlying assumptions will be discussed and general results are obtained using matrices. You will be introduced to the standard approach to the analysis of normally distributed data using ANOVA, as well as the methods for the design and analysis of efficient experiments. The general methodology is extended to nonlinear regression, generalised regression and the analysis of multidimensional contingency tables.

View Applied Regression and Experimental Data Analysis on our Module Directory

COMPONENT 02: COMPULSORY

Applied Statistics

(15 CREDITS)

In this module, you will study three application areas of statistics - multivariate methods, demography and epidemiology, and sampling, and how to apply and assess these methods in a variety of situations.

View Applied Statistics on our Module Directory

COMPONENT 03: COMPULSORY

Machine Learning

(15 CREDITS)

Humans can often perform a task extremely well (e.g., telling cats from dogs) but are unable to understand and describe the decision process followed. Without this explicit knowledge, we cannot write computer programs that can be used by machines to perform the same task. “Machine learning” is the study and application of methods to learn such algorithms automatically from sets of examples, just like babies can learn to tell cats from dogs simply by being shown examples of dogs and cats by their parents. Machine learning has proven particularly suited to cases such as optical character recognition, dictation software, language translators, fraud detection in financial transactions, and many others.

View Machine Learning on our Module Directory

COMPONENT 04: 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 05: OPTIONAL

(30 CREDITS)

COMPONENT 06: OPTIONAL

(30 CREDITS)

COMPONENT 07: 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

Professional Placement

(120 CREDITS)

This module enables you to undertake a placement with an external Placement Provider. You will acquire effective work-based skills specific to your chosen field, and gain a detailed understanding of work processes. It’s an opportunity to put taught skills into practice and develop a network of industry professionals. Your placement is a sought-after contribution to your employability, giving you the tools employers look for in skilled graduates.

View Professional Placement on our Module Directory

COMPONENT 01: CORE

Dissertation

(60 CREDITS)

This is a dissertation module for MSc students. Students will be provided with a list of dissertation titles or may suggest their own, provided this is agreed with the dissertation supervisor.

View Dissertation on our Module Directory

Placement

You will complete a professional placement between the taught part of the course and the individual project. This professional placement allows you to gain work experience during your postgraduate studies.

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 per year

International fee

£21,700 per year

Fees will increase for each academic year of study.

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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