MSc Data Science with Professional Placement

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 Department of Mathematical Sciences 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 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. 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.

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 Udo Kruschwitz – natural language processing, analysis textual/unstructured data, information retrieval
• Professor Berthold Lausen – biostatistics, classification and clustering, data science education, event time data, machine learning, predictive modelling
• Dr Aris Perperoglou – data analysis and data visualisation, statistical modelling and smoothing, survival analysis, clinical trials
• Professor Abdel Salhi – data mining, numerical analysis, optimisation
• Professor Edward Tsang – applied AI, constraint satisfaction, computational finance and economics, agent-based simulations
• Dr Spyros Vrontos – actuarial mathematics and actuarial modelling
• 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 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.

UK entry requirements

We will consider applicants with a 2:1 degree in one of the following subjects:

• Mathematics,
• Statistics
• Operational research
• Computer Science
• Applied Mathematics
• Pure Mathematics
• Biostatistics
• Economic Statistics
• Statistics
• Economics

OR

A 2.1 degree in any subject which includes:

• Calculus
• Maths
• Engineering Maths
• Advanced Maths

• Statistics or Probability
• Maths
• Engineering Maths
• Advanced Maths

• Algebra
• Analysis
• Programming language (R, Matlab or Python)
• A second module in Probability or Statistics
• Numerical Methods
• Complex Numbers
• Differential Equations
• Optimisation (Linear Programming)
• Regression
• Stochastic Process
• Maths
• Engineering Maths
• Advanced Maths

Applicants with a degree below 2:1 or equivalent will be considered dependent on any relevant professional or voluntary experience and previous modules studied.

English language requirements

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.

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. 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.

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

• 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