Undergraduate Course

Integrated Master in Science: Actuarial Science and Data Science

How to apply

Overview

The details

Course: Actuarial Science and Data Science

UCAS code: N399

Start date: October 2021

Study mode: Full-time

Duration: 4 years

Location: Colchester Campus

Based in: Mathematical Sciences

Risk is opportunity – being an actuary is a shrewd career choice. Our MSci Actuarial Science and Data Science is an Integrated Masters course that will teach you the art of turning risk into opportunity with the understanding of visualising data in actuarial applications and beyond. Such a skillset is crucial for the growing market for experts in data science with the collection and analysis of data being crucial to understanding how to improve, create and guide products and services across the globe.

Our attractive blend of actuarial science and data science will equip you with an understanding of real-world financial issues, efficient use of experimental design to provide fast and less expensive solutions, and computing skills essential for entering the actuarial and data science profession.

You’ll be taught theory and methods used by professional actuaries; on how to apply mathematical and statistical skills to minimise financial risk when the stakes are high, in areas such as commerce, government, insurance and finance. You’ll be provided with the crucial basis of core skills in data base programming and management, in developing data processing pipelines and in organising and analysing large and massive data sets. And you’ll be introduced to and use programming language Python and R for statistical analysis and data visualisation. In your third and fourth year, analysing data and methods in group projects will be essential capstone modules for the learning outcomes of the course.

Topics include:

Mathematical skills and finance
Computer science and programming
Statistics and operations research
Artificial intelligence, databases and information retrieval
Ethical issues around the use and processing of data
Specialist skills in the areas of big data, data analytics and data science

Our Integrated Masters will give you the opportunity to fast-track your degree and complete your final year in nine months compared with a regular MSc which usually takes twelve months. Combining your undergraduate and postgraduate study in one degree will give you a strong theoretical background as well as specialist expertise through independent research. This combination makes graduates from our course attractive candidates for many employers.

Why we're great.

You have access to our ultramodern facilities at our new STEM Centre that provide real-world experience.
You join a community of scholars leading the way in technological research and development.
We have active links with industry to broaden your employment potential and placement opportunities.

THE Awards 2018 - Winner University of the Year

Our expert staff

As well as being world-class academics, our staff are award-winning teachers. Many of our academics have won national or regional awards for lecturing, and many of them are qualified and accredited teachers – something which is very rare at a university.

Teaching on our course are expert academics and practising actuaries. The key departmental staff for our BSc Actuarial Science are Keith Bannister, Dr Haslifah Hashim, and Dr Spyridon Vrontos.

Keith Bannister is a qualified actuary, and a Fellow of the Institute and Faculty of Actuaries; he lectures part-time in actuarial science.

Dr Hashim specialises in life and general insurance, pensions, financial mathematics, enterprise risk management, forensic economics, and Islamic finance and takafu.

Dr Vrontos specialises in actuarial mathematics and modelling, asset-liability management and performance measurement for pension funds, hedge funds and mutual funds, risk management and solvency, and risk theory.

Specialist facilities

In addition to teaching, we have a Maths Support Centre, which offers help to students 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 a dedicated social and study space for Maths students in the department, which is situated in the new £18m STEM Centre
We host regular 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

Your future

Demand for skilled graduates in the areas of mathematics, big data, data science and actuarial science is growing rapidly in both the public and private sector, and there is a predicted shortage of data scientists with the skills to understand and make commercial decisions based on the analysis of big data. It is predicted by the US Department of Labor that the employment of actuaries is expected to grow faster than any other occupation, making it a great prospect for a graduate job.

Aside from a rewarding career as an actuary, clear thinkers are required in every profession, so the successful mathematician has an extensive choice of potential careers. An incredible 89% of our undergraduate maths students are in employment or further study (Graduate Outcomes 2020).

We also work with our University's Student Development Team to help you find out about further work experience, internships, placements, and voluntary opportunities.

Entry requirements

UK entry requirements

A-levels: ABB including Mathematics or Further Mathematics

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.

Structure

Example structure

We offer a flexible course structure with a mixture of compulsory and optional modules chosen from lists. The first three undergraduate years listed below are an example structure from the current academic year. Your course structure could differ from this if modules change from year-to-year. The final Masters year shows you all of the modules currently available (compulsory and optional) so you can see the breadth of what is on offer.

Our research-led teaching is continually evolving to address the latest challenges and breakthroughs in the field, therefore all modules listed are subject to change. To view the compulsory modules and full list of optional modules currently on offer, please view the programme specification via the link below.

Teaching and learning disclaimer

Following the impact of the pandemic, we made changes to our teaching and assessment to ensure our current students could continue with their studies uninterrupted and safely. These changes included courses being taught through blended delivery, normally including some face-to-face teaching, online provision, or a combination of both across the year.

The teaching and assessment methods listed show what is currently planned for 2021 entry; changes may be necessary if, by the beginning of this course, we need to adapt the way we’re delivering them due to the external environment, and to allow you to continue to receive the best education possible safely and seamlessly.

See full list of modules

Calculus

This module will allow you to build your knowledge of differentiation and integration, how you can solve first and second order differential equations, Taylor Series and more.

View Calculus on our Module Directory

Statistics I

How do you apply the addition rule of probability? Or construct appropriate diagrams to illustrate data sets? Learn the basics of probability (combinatorial analysis and axioms of probability), conditional probability and independence, and probability distributions. Understand how to handle data using descriptive statistics and gain experience of R software.

View Statistics I on our Module Directory

Matrices and Complex Numbers

Can you perform simple operations on complex numbers? How do you solve systems of linear equations using row operations? Can you calculate the determinant and inverse of a matrix? Understand the basics of linear algebra, with an emphasis on vectors and matrices.

View Matrices and Complex Numbers on our Module Directory

Mathematical and Computational Modelling

This module introduces you to programming skills in the context of a range of mathematical modelling topics. Mathematical modelling skills will be an important focus alongside learning how to structure and implement codes in both Matlab and R. A key part of the module will be investigative open-ended computational modelling studies at both the group and individual level.

View Mathematical and Computational Modelling on our Module Directory

Mathematics Careers and Employability

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 Mathematics Careers and Employability on our Module Directory

Introduction to Finance

This module introduces you to finance as a subject area. You gain an overview of the financial system, instruments and markets and explore important financial concepts and problems. Topics include investment companies, return and risk and behavioural finance.

View Introduction to Finance on our Module Directory

Introduction to Economics

How do consumers make decisions? Or firms conduct different market strategies? What impact does government policy have on inflation? Or unemployment? Develop your knowledge of economics in relation to a range of contemporary issues. Learn how to apply both micro and macroeconomic principles to the analysis of such problems.

View Introduction to Economics on our Module Directory

Mathematics Careers and Employability

View Mathematics Careers and Employability on our Module Directory

Ordinary Differential Equations

The subject of ordinary differential equations is a very important branch of Applied Mathematics. Many phenomena from Physics, Biology, Engineering, Chemistry, Finance, among others, may be described using ordinary differential equations. To understand the underlying processes, we have to find and interpret the solutions to these equations. The last part of the module is devoted to the study of nonlinear differential equations and stability. This module provides an overview of standard methods for solving single ordinary differential equations and systems of ordinary differential equations, with an introduction to the underlying theory.

View Ordinary Differential Equations on our Module Directory

Statistics II

This module combines a brief period of revision where you look at events and their probabilities with a close look at the principal continuous distributions. You will also have the opportunity to learn how to determine confidence intervals and carry out hypothesis tests.

View Statistics II on our Module Directory

Finance and Financial Reporting

What instruments are used by companies to raise finance and manage financial risk? What is the role of financial institutions operating in financial markets? What are the techniques of financial accounting? How do you use spreadsheets in financial analysis? Examine and develop the concepts and elements of corporate finance.

View Finance and Financial Reporting on our Module Directory

Contingencies I

How do you define simple assurance contracts? What practical methods are required to evaluate expected values from a contract? How can you calculate gross premiums and reserves of assurance and reserves? Understand the mathematical techniques that can calculate, model and value cashflows dependent on death, survival or other uncertain risks.

View Contingencies I on our Module Directory

Survival Analysis

What are the principles of actuarial modelling? And what are survival models? Examine how calculations in clinical trials, pensions, and life and health insurance require reliable estimates of transition intensities/survival rates. Learn how to estimate these intensities. Build your understanding of estimation procedures for lifetime distributions.

View Survival Analysis on our Module Directory

Corporate Finance

Explore how financial theory can aid financial decision-making in corporations. You discover how to apply the value maximization principle to corporate decisions, such as investment decision, borrowing decisions, and dividend decisions.

View Corporate Finance on our Module Directory

Macroeconomics (Intermediate)

What tools can you use for macroeconomic analysis? And how can these then be applied to macro-policy issues? Learn how to build alternative macroeconomic models and apply analytical reasoning. Examine real-life macroeconomic questions, on topics such as government budgets or wage-price flexibility, and critically evaluate macroeconomic policies.

View Macroeconomics (Intermediate) on our Module Directory

Mathematics Careers and Employability

View Mathematics Careers and Employability on our Module Directory

Mathematics of Portfolios

How do you formulate financial decision problems mathematically? And how do you identify an appropriate method of solution? Understand the basic models and mathematical methods underlying modern portfolio management. Assess the limitations of these models and learn to correctly interpret your results from calculations.

View Mathematics of Portfolios on our Module Directory

Contingencies II

What methods are available to model cashflows that are contingent on competing risk? What techniques for discounted emerging costs can be used in pricing, reserving and assessing profitability? Study the methods and techniques used in pricing and evaluating insurance and pension products, and insurance companies.

View Contingencies II on our Module Directory

Statistical Methods

This module will enable you to expand your knowledge on multiple statistical methods. You will learn the concepts of decision theory and how to apply them, have the chance to explore “Monte Carlo” simulation, and develop an understanding of Bayesian inference, and the basic concepts of a generalised linear model.

View Statistical Methods on our Module Directory

Stochastic Processes

Ever considered becoming an Actuary? This module covers the required material for the Institute and Faculty of Actuaries CT4 and CT6 syllabus. It explores the stochastic process and principles of actuarial modelling alongside time series models and analysis.

View Stochastic Processes on our Module Directory

Financial Derivatives

Why are arbitrage arguments important in modern finance? How can a binomial model evaluate derivatives? What are the main models for interest rates? Understand the mathematical techniques underlying the modelling of derivative pricing. Acquire skills in the development of pricing and risk management. Explore stochastic methods and credit risk.

View Financial Derivatives on our Module Directory

Capstone Project: Mathematics

This module will allow you to step out of the classroom and gain real experience in your selected branch of Mathematics that you could not gain from a lecture. You will be able to develop your ability to work independently on research and produce a project report on your topic of interest.

View Capstone Project: Mathematics on our Module Directory

Microeconomics (Intermediate)

How do consumers behave in a competitive market? And what about producers? How do various imperfections affect the outcome of decentralised markets? Study the fundamental concepts and methods in microeconomics. Understand the tools and methods of analysis for economic reasoning, and develop your critical approach to economic issues and policies.

View Microeconomics (Intermediate) on our Module Directory

Modelling Experimental Data

Can you calculate confidence intervals for parameters and prediction intervals for future observations? Represent a linear model in matrix form? Or adapt a model to fit growth curves? Learn to apply linear models to analyse data. Discuss underlying assumptions and standard approaches. Understand methods to design and analyse experiments.

View Modelling Experimental Data on our Module Directory

Research Methods

Looking to build your research capabilities? This module will equip you with the principal research tools for your postgraduate course in Mathematical Sciences, including practice in the mathematical word-processing language LaTeX.

View Research Methods on our Module Directory

Advanced Capstone Project: Actuarial Science, Data Science or Mathematics

Our Advanced Capstone Projects are opportunities for students to study independently a topic in mathematics, statistics and related areas (such as mathematical physics, data science, modelling and so on) and develop skills such as writing reports and giving presentations. You will be monitored by a supervisor, who will periodically set tasks and discuss the progress of the work. The key purpose of Advanced Capstone Projects is that you should be given the opportunity to show your strengths and be allowed a certain amount of freedom and leeway in how you complete the project. It will also provide opportunities for you to develop transferable communication, time- and task-management skills, through researching the topic and organising and producing the written and oral reports.

View Advanced Capstone Project: Actuarial Science, Data Science or Mathematics on our Module Directory

Machine Learning

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

Survival Analysis (optional)

View Survival Analysis (optional) on our Module Directory

Data Visualisation (optional)

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 (optional) on our Module Directory

Stochastic Processes (optional)

View Stochastic Processes (optional) on our Module Directory

Applied Statistics (optional)

How do you apply multivariate methods? Or demographical and epidemiological methods? And how do you apply sampling methods? Study three application areas of statistics – multivariate methods, demography and epidemiology, and sampling. Understand how to apply and assess these methods in a variety of situations.

View Applied Statistics (optional) on our Module Directory

Introduction to Programming in Python (optional)

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 (optional) on our Module Directory

Information Retrieval (optional)

Search engines have become the first entry point into a world of knowledge and they form an essential part of many modern computer applications. While much of the underlying principles have been developed over decades, the landscape of search engine technology has changed dramatically in recent years to deal with data sources magnitudes larger than ever before (the rise of 'big data'). As a result of this, new paradigms for storing, indexing and accessing information have emerged. This module will provide the essential foundation of information retrieval and equip students with solid, applicable knowledge of state-of-the-art search technology.

View Information Retrieval (optional) on our Module Directory

Text Analytics (optional)

We live in an era in which the amount of information available in textual form - whether of scientific or commercial interest - greatly exceeds the capability of any man to read or even skim. Text analytics is the area of artificial intelligence concerned with making such vast amounts of textual information manageable - by classifying documents as relevant or not, by extracting relevant information from document collections, and/or by summarizing the content of multiple documents. In this module we cover all three types of techniques.

View Text Analytics (optional) on our Module Directory

Natural Language Engineering (optional)

As humans we are adept in understanding the meaning of texts and conversations. We can also perform tasks such as summarize a set of documents to focus on key information, answer questions based on a text, and when bilingual, translate a text from one language into fluent text in another language. Natural Language Engineering (NLE) aims to create computer programs that perform language tasks with similar proficiency. This course provides a strong foundation to understand the fundamental problems in NLE and also equips students with the practical skills to build small-scale NLE systems. Students are introduced to three core ideas of NLE: a) gaining an understanding the core elements of language--- the structure and grammar of words, sentences and full documents, and how NLE problems are related to defining and learning such structures, b) identify the computational complexity that naturally exists in language tasks and the unique problems that humans easily solve but are incredibly hard for computers to do, and c) gain expertise in developing intelligent computing techniques which can overcome these challenges.

View Natural Language Engineering (optional) on our Module Directory

Data Science and Decision Making (optional)

The aim of this module is to familiarise students with the whole pipeline of processing, analysing, presenting and making decision using data. This module blends data analysis, decision making and visualisation with practical python programming. Students will need a reasonable programming background as they will be expected to develop a complete end-to-end data science application.

View Data Science and Decision Making (optional) on our Module Directory

Neural Networks and Deep Learning (optional)

The aim of this module is to provide students with an understanding of the role of artificial neural networks (ANNs) in computer science and artificial intelligence. This will allow the student to build computers and intelligent machines which are able to have an artificial brain which will allow them to learn and adapt in a human like fashion.

View Neural Networks and Deep Learning (optional) on our Module Directory

Big-Data for Computational Finance (optional)

This module is a mix of theory and practice with big data cases in finance. Algorithmic and data science theories will be introduced and followed by a thorough introduction of data-driven algorithms for structures and unstructured data. Modern machine learning and data mining algorithms will be introduced with particular case studies on financial industry.

View Big-Data for Computational Finance (optional) on our Module Directory

Teaching

Courses are taught by a combination of lectures, laboratory work, assignments, and individual and group project activities

Group work

A significant amount of practical lab work will need to be undertaken for written assignments and as part of your learning

Assessment

You are assessed through a combination of written examinations and coursework
All our modules include a significant coursework element
You receive regular feedback on your progress through in-term tests
Courses are assessed on the results of your written examinations, together with continual assessments of your practical work and coursework

Fees and funding

Home/UK fee

£9,250

International fee

£16,850

EU students commencing their course in the 2021-22 academic year will be liable for the International fee.

Fees will increase for each academic year of study.

Home/UK fee information

International fee information

What's next

Our events are a great way to find out more about studying at Essex. We run a number of Open Days throughout the year which enable you to discover what our campus has to offer. You have 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

Check out our Visit Us pages to find out more information about booking onto one of our events. And if the dates aren’t suitable for you, feel free to book a campus tour here.

Applications for our full-time undergraduate courses should be made through the Universities and Colleges Admissions Service (UCAS). Applications are online at: www.ucas.com. Full details on this process can be obtained from the UCAS website in the how to apply section.

Our UK students, and some of our EU and international students, who are still at school or college, can apply through their school. Your school will be able to check and then submit your completed application to UCAS. Our other international applicants (EU or worldwide) or independent applicants in the UK can also apply online through UCAS Apply.

The UCAS code for our University of Essex is ESSEX E70. The individual campus codes for our Loughton and Southend Campuses are 'L' and 'S' respectively.

You can find further information on how to apply, including information on transferring from another university, applying if you are not currently at a school or college, and applying for readmission on our How to apply and entry requirements page.

Applicant Days and interviews

Resident in the UK? If your application is successful, we will invite you to attend one of our applicant days. These run from January to April and give you the chance to explore the campus, meet our students and really get a feel for life as an Essex student.

Some of our courses also hold interviews and if you're invited to one, this will take place during your applicant day. Don't panic, they're nothing to worry about and it's a great way for us to find out more about you and for you to find out more about the course. Some of our interviews are one-to-one with an academic, others are group activities, but we'll send you all the information you need beforehand.

If you're outside the UK and are planning a trip, feel free to email applicantdays@essex.ac.uk so we can help you plan a visit to the University.

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.

The Campus is set within 200 acres of beautiful parkland, located two miles from the historic town centre of Colchester – England's oldest recorded town. Our Colchester Campus is also easily reached from London and Stansted Airport in under one hour.

How to get to Colchester Campus

Find out more

If you live too far away to come to Essex (or have a busy lifestyle), no problem. Our 360 degree virtual tours allows you to explore our University from the comfort of your home. Check out our Colchester virtual tour and Southend virtual tour to see accommodation options, facilities and social spaces.

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 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.