Postgraduate Diploma Optimisation and Data Analytics

Program Overview

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Our Postgraduate Diploma Optimisation and Data Analytics is aimed at those with a first degree in which the major subject was mathematics, and we expect you to have prior knowledge of statistics – for example significance testing or basic statistical distributions – and operational research such as linear programming. Postgraduate Diplomas last for six to nine months (full-time) and include the modules and assessed work of a Masters, without a dissertation. This allows you to proceed to a Masters in mathematics if your undergraduate degree was in a different subject. Businesses, organisations, and individuals all strive to work as effectively as possible. Operational research uses advanced statistical and analytical methods to help improve the complex decision-making processes to deliver a product or service. Working in this field, you might be identifying future needs for a business, evaluating the time-life value of a customer, or carrying out computer simulations for airlines. You specialise in areas including:

• Continuous and discrete optimisation
• Time series econometrics
• Heuristic computation
• Experimental design
• Machine learning
• Linear models

Our interdisciplinary research recognises that mathematics, including what can be very abstract mathematics, is an essential part of research in many other disciplines. Our Department of Mathematical Sciences has an international reputation in many areas including semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology. We are 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.

Why we're great.

• This course will appeal to you if your first degree included mathematics as the major subject, and you have prior knowledge of statistics.
• You specialise in areas including continuous and discrete optimisation, time series econometrics, and experimental design.
• We have an international reputation for our work on optimisation, probability and applied statistics.

Our expert staff

Our department is committed to providing you with the academic support you need to succeed. Our flexible policy means some staff are always available, whilst others maintain regular drop-in times. Staff are always happy to arrange appointments for longer discussions, and no issue is too big or too small. Our staff have published several well-regarded text books and are world leaders in their individual specialisms, with their papers appearing in learned journals like Communications in Algebra, Studia Logica, International Journal of Algebra and Computation, SIAM Journal in Optimization, IEEE Evolutionary Computation, Computers and Operations Research, Ecology, Journal of Mathematical Biology , and Journal of Statistical Applications in Genetics and Molecular Biology.

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Specialist facilities

• Unique to Essex is our renowned Maths Support Centre , which offers help to students, staff and local businesses on a range of mathematical problems. Throughout term-time, we can chat through mathematical problems either on a one-to-one or small group basis
• We have our own computer labs for the exclusive use of students in the Department of Mathematical Sciences – in addition to your core maths modules, you gain computing knowledge of software including Matlab and Maple
• We host events and seminars throughout the year
• Our students run a lively Mathematics Society, an active and social group where you can explore your interest in your subject with other students

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Your future

Our Postgraduate Diploma Optimisation and Data Analytics will equip you with employability skills like problem solving, analytical reasoning, data analysis, and mathematical modelling, as well as training you in independent work, presentation and writing skills. Your exposure to current active research areas, such as decomposition algorithms, prepares you for further study at doctoral level. Graduates of this course now hold key positions in government, business and academia, and work for global companies such as Ocado Technology and Dunnhumby, as well as for HM Revenue & Customs, as data and business intelligence analysts. We also offer supervision for PhD, MPhil and MSc by Dissertation. We have an international reputation in many areas such as semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology, and our staff are strongly committed to research and to the promotion of graduate activities. We additionally work with our Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Program Outline

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.

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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.

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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

Year 1 How do you apply an algorithm or numerical method to a problem? What are the advantages? And the limitations? Understand the theory and application of nonlinear programming. Learn the principles of good modelling and know how to design algorithms and numerical methods. Critically assess issues regarding computational algorithms. View Nonlinear Programming on our Module Directory In this module you will learn what underpins the algorithms used where variables are integer and apply these algorithms to solve integer and mixed integer problems with cutting-plane algorithms. View Combinatorial Optimisation on our Module Directory The aim of this module is to provide an introduction to computer programming for students with little or no previous experience. The Python language is used in the Linux environment, and students are given a comprehensive introduction to both during the module. The emphasis is on developing the practical skills necessary to write effective programs, with examples taken principally from the realm of data processing and analysis. You will learn how to manipulate and analyse data, graph them and fit models to them. Teaching takes place in workshop-style sessions in a software laboratory, so you can try things out as soon as you learn about them. View Introduction to Programming in Python on our Module Directory Are you interested in understanding how AlphaGo was able to beat a top Go player? In this module, you will learn about the models behind successful stories of Reinforcement Learning, where a machine (agent) makes sequential decisions to reach an optimal goal. The lectures will be complemented with Lab sessions where we will take advantage of the Open AI Gym environments, allowing us to train our agents to perform tasks such as playing videogames (Atari) and more. View Dynamic programming and reinforcement learning on our Module Directory 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 06: COMPULSORY WITH OPTIONS Option(s) from list (55 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

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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

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Assessment

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