MSc Behavioural Economics and Data Science

Key details

MSC BEHAVIOURAL ECONOMICS AND DATA SCIENCE

Start Year

2023

Attendance

Full Time

Award

Degree of Master of Science

Duration (years)

In the coming years, we will see major changes in the world of work. One of these changes will be a huge increase in demand for data scientists. This MSc will prepare you for a career as a data scientist so that you can help meet this change in demand.

In this MSc, there is a focus on particular types of application: data science techniques are combined with the area of Behavioural Economics, in which the School of Economics at UEA has a world-class reputation.

Having completed this MSc, your Employability prospects will be strong. Policy-makers in government are increasingly turning to behavioural economics for insights into decision-making behaviour, insights that inform our understanding of how individuals, groups and markets operate. Private Sector organisations specialising in Behavioural Economics research are also a growing phenomenon.

You could also take your skills into banking and finance, healthcare, regulation, human resources or executive management. You could also progress to PhD study.

About

Our MSc Behavioural Economics and Data Science is part of our Applied Training Programme – designed to provide key skills in economics. It is ideal if your undergraduate training was in a subject other than economics, and you now wish to develop analytical skills as an economist. This degree may be particularly useful for those with a maths or science background who wish to make their skills more marketable in the rapidly changing jobs market.

The degree course consists of core Economics and Econometrics modules taught within the School of Economics, and well as specialised modules in programming and behavioural economics. Some of the programming modules are taught in the School of Computing. You will also write a dissertation towards the end of the academic year.

In many ways, Behavioural Economics is an ideal framework in which to carry out big data research. The focus of the MSc is on the methodology for testing predictions of behavioural economics using big data sources. For example: you’ll learn how to create data sets using data scraping techniques, and you will be made aware of the relevant ethical issues; you’ll learn how to process and manipulate data; you will learn how to make economic forecasts using web search data; you will learn how to work with multiple data sources (e.g. how to merge dietary and health data on individuals); you will learn how to test economic theories using online data sources (e.g. testing auction theory using data from online auctions).

The dissertation module gives you the freedom to choose your own topic from within the area of behavioural economics and data science. Throughout your dissertation, you’ll be supported and supervised by a member of the School of Economics.

We offer specialised structural support to help you negotiate your masters course. This includes compulsory, intensive pre-sessional training before the start of teaching on your course modules. This training incorporates the techniques of calculus and matrix algebra and an introduction to the specialist econometric software that you'll use in your MSc programme.

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Important Information

Whilst the University will make every effort to offer the courses listed, changes may sometimes be made arising from the regular review of course programmes. Where this activity leads to significant (but not minor) changes to programmes, there will normally be prior consultation of students and others. Changes 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 inform students.

Assessment for Year 1

Your coursework will be in a variety of forms – including take-home assignments, seminar presentations, written tests, computer tests and your dissertation (which you'll submit at the end of August).

Throughout your course, you'll be given guidance on your work and constructive feedback to help you improve. You’ll receive written feedback for all pieces of coursework and further guidance will be available from your module’s organisers.

If you have additional needs due to disabilities such as sensory impairment, or learning difficulties such as dyslexia, please talk to our Student Support Service about how we can help.

Structure

The MSc Behavioural Economics and Data Science is a one-year course. In each semester, you'll take three taught modules, followed by writing your dissertation in the period between June and August.

In the autumn semester, you’ll take Economic Concepts, Econometric Methods, and Programming and Analytics for Behavioural Economists. In spring, you'll take Data Mining (CMP module), Behavioural Consumer Analytics, and an option. The possible options here are Financial Mathematics, Risk Management and Trading, Environmental and Natural Resource Economics, and Visualisation.

Although you'll write your dissertation between June and August, you’ll begin your dissertation module with a sequence of dissertation training lectures and workshops in spring. Here you'll discover how to choose a topic, how to access data and search literature, how to reference, and how to analyse quantitative data.

You will be able to take advantage of further dissertation training workshops in late June and early July.

Teaching and Learning

UEA’s School of Economics is lively, friendly, research-orientated and committed to excellence in teaching. We have an international reputation in many key areas, including theoretical and applied economics. Our research interests include behavioural and experimental economics, competition economics, environmental policy, conflict, contests and corporate behaviour, finance and financial markets, and Applied Econometrics.

The teaching of your modules will be spread over two semesters. In a typical module, you'll have lectures and seminars (or lab sessions) per week. Your seminars are more interactive than lectures and provide you with an opportunity to raise questions arising from lectures.

In your programming and econometrics modules, where you'll learn how to program and conduct econometric analysis using specialist econometric software.

You will also become practiced in independent study, spending time working on coursework assignments, preparing for seminars, and doing your own wider reading.

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Assessment for Year 1

If you have additional needs due to disabilities such as sensory impairment, or learning difficulties such as dyslexia, please talk to our Student Support Service about how we can help.

Compulsory Modules

ECONOMIC CONCEPTS

Code ECO-7011A

Credits 20

Introducing micro and macro economic analysis, this module will familiarise you with a wide range of economic tools which can be applied to issues relating to your Applied Training Programme. It covers, in particular, consumer and producer choice, market equilibrium, market structure, externalities and public goods, the macroeconomics of inflation, unemployment and growth and macroeconomic policy.

ECONOMETRIC METHODS

Code ECO-7000A

Credits 20

Are you interested in working with data? Would you like to be able to make economics forecasts or predictions, or test an economic theory? If so, this module will introduce you the empirical techniques you need to satisfy these interests. This module will introduce you to econometrics, which is a combination of economic theory, mathematical economics, economic statistics and mathematical statistics. You’ll learn some basic econometric techniques that will enable you to obtain numerical estimates of, for example, the relationship between expenditure on a good and income, or the relationship between years of education and pay. In addition, as your seminars will take place in the computer labs, you’ll gain skills in analysing data using the econometric computer package STATA. In the first part of the module, you’ll be shown how to estimate a simple regression model, interpret your findings and test the relationships between variables. As you progress, you’ll learn how to identify problems with your model and how to address these problems. In lectures you’ll learn about the techniques; in seminars you’ll apply these techniques to a variety of economic problems and estimate models with the help of STATA. In the later part of the module you’ll consider a more advanced style of modelling, which is appropriate for dealing with data on the choices made by individuals. For example, owning your own home rather than renting, or going to university instead of entering the labour market. By the end of the module you’ll be able to estimate appropriate regression models, identify problems which may arise and know how to address them. In addition, you’ll be able use STATA and understand some of the methods, models and techniques that may be useful in the workplace.

DATA MINING

Code CMP-7023B

Credits 20

This module is designed for postgraduate students studying on MSc courses. You will explore the methodology of Knowledge Discovery and Data Mining (KDD). You will cover each stage of the KDD process, including preliminary data exploration and visualisation, data cleansing, pre-processing, various data analysis tasks, model evaluation and model exploitation. In terms of data analysis, we will focus on clustering, classification, association rule induction and text mining. You will explore a number of algorithms for each tasks and also cover ensembles. Through this module, you will gain knowledge of algorithms and methods for data analysis, as well as practical experience programming in Python using popular libraries such as Pandas, Scikit-learn and Seaborne.

ECONOMICS DISSERTATION (60 CREDITS)

Code ECO-7023X

Credits 60

Economics Dissertation is the “icing on the cake” of an Economics MSc. It is a module that brings together the skills that you have learnt throughout your degree programme and applies these skills to study a particular topic of your interest. This module gives you an opportunity to spend a period of 2 or 3 months working exclusively on a topic of your own choice, at your own pace, with guidance from a supervisor. The module commences with a sequence of “dissertation training” lectures during the Spring Semester, including such themes as choosing a topic, accessing data, searching the literature, referencing, and qualitative data analysis. There are further dissertation training lectures during late June and early July, held in computer labs, with the objective of providing the types of econometric skills often required in dissertation research. The Econometric software STATA is used, the same as used in taught modules in Econometrics. At the end of the Spring semester, you will submit a research proposal stating the research question you would like to address and proposing a suitable methodology. You can also select to work on a dissertation topic either with a business, charity, or other organization. In all cases, it is your responsibility to come up with a research proposal although you are encouraged to speak with instructors and the PG Course Director for advice and guidance. On the basis of your proposal, a suitable supervisor will be allocated to you. At least three meetings with the supervisor should take place between May - July. The total contact time over these meetings should be at least three hours. At the first meeting, the research proposal is discussed. By the time of the third and final meeting, you should ideally have supplied a first draft of the dissertation for your supervisor to provide feedback on. The deadline for the dissertation is at the end of August and the word limit for dissertations is either 8,000 or 12,000, depending on which MSc you are taking. If you want to work on a dissertation topic with a business, charity, or other organization you must get your dissertation topic pre-approved by the PG Course Director.

PROGRAMMING AND ANALYTICS FOR BEHAVIOURAL ECONOMISTS

Code ECO-7026A

Credits 20

This Autumn Semester module is intended to provide students with a grounding in the programming and data-analytic techniques that will be used in later modules and also in the dissertation. The main part of the module introduces students to programming methods in Python, including use of loops and lists, data cleaning and manipulation, methods of exploratory data analysis, data visualisation, web scraping, and methods for working with multiple data sets. Ethical issues faced when working with individual data will also be covered. The programming methods will then be applied to problems in policy evaluation, including applications of D-in-D and regression discontinuity methodology to naturally occurring data. The module ends with the use of web-search data in Economic Analysis.

BEHAVIOURAL CONSUMER ANALYTICS

Code ECO-7027B

Credits 20

Consumers play a critical role in any economy. But why do they buy what they buy? Can we predict what they will consume? Can we influence what they will choose? This module explores how insights from behavioural economics can help us analyse and understand consumer behaviour. The module commences by covering the standard model of consumer behaviour, and the potential for behavioural economics to enrich the model. An array of topics from Behavioural Economics and Consumer Behaviour are then covered, including: values, attitudes and preferences; discrete choice experiments; experimental design; revealed preference; econometric models for choice between discrete alternatives; estimation of demand parameters using purchaser data; decision-making under risk; mental accounting and information; intertemporal choice; suboptimal consumer choice; choice architecture; and behavioural finance.

Optional A Modules

(20 credits)

INFORMATION VISUALISATION

Code CMP-7022B

Credits 20

This module is an introduction to information visualisation. You will learn techniques for summarising and presenting a wide range of data. There is a strong emphasis on understanding the appropriate context and use of visualisation techniques. You will also learn about problems and techniques for dealing with large data flows and issues of integrating multiple data sources.

FINANCIAL MATHEMATICS

Code ECO-7013B

Credits 20

This technical finance module is suited to students who wish to pursue a career in the financial sector. The focus is on valuation and risk analysis of financial products, and the module is highly analytical, with weekly exercise sessions in computer labs. Topics covered include: continuous-time stochastic processes, stochastic models for security prices, bonds, term structure of interest rates, futures and forwards, options, swaps, hedging and credit risk.

RISK MANAGEMENT AND TRADING

Code ECO-7017B

Credits 20

The first part of the module begins by introducing concepts such as risk in financial markets, and discusses the stylised facts of financial returns. With the aid of the econometric computer package STATA and using real data, we test models in empirical finance. Students will learn how to calculate risk using daily and high-frequency data, and how to calculate realised volatility. The second part of the module introduces algorithmic trading using Excel VBA, as well as the role of behavioural finance in financial markets.

ENVIRONMENTAL AND NATURAL RESOURCE ECONOMICS

Code ECO-7020B

Credits 20

Gain a thorough grounding in the key issues in environmental and resource economics. This module provides an overview of the economic and econometric principles that serve as pivotal tools in ensuring that natural resources are sustainably valued and managed in a global economic environment. The applied nature of the module is complemented by technical papers that form the subject of the discussions in seminar sessions. Open to postgraduate students enrolled on both ATP and APP programmes, this module will enhance the econometric principles taught in the Autumn Semester by applying analytical and statistical methods to the field of environmental economics so as to emphasise the empirical relevance of these theories.

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Open Days

Important Information

Whilst the University will make every effort to offer the modules listed, changes may sometimes be made arising from the annual monitoring and review of modules. Where this activity leads to significant change to a programme and modules, the University will endeavour to consult with affected students. The University may not be able to offer a module for reasons outside of its control, such as the illness of a member of staff. Availability of optional modules may be restricted owing to timetabling, lack of demand, or limited places. Where this is the case, you will be asked to make alternative module choices and you will be supported during this process.

Entry Requirements

Degree classification

Bachelors degree - 2.2 or equivalent

Degree subject

Any subject area

Additional entry requirements

INTO University of East Anglia

If you do not meet the academic requirements for this course, you may be able to study one of the International Graduate Diploma programmes offered by our partner INTO UEA. These programmes guarantee progression to selected master's degrees if students achieve the appropriate grade. For more details please click here:

International Graduate Diploma in Economics

Students for whom english is a foreign language

We welcome applications from students whose first language is not English. To ensure such students benefit from postgraduate study, we require evidence of proficiency in English. Our usual entry requirements are as follows:

IELTS: 6.0 (minimum 5.5 in only two components with 6.0 in the others)
PTE (Pearson): 64 (minimum 59 in two components only with 64 in the other two)

Test dates should be within two years of the course start date.

Other tests, including Cambridge English exams and the Trinity Integrated Skills in English are also accepted by the university. The full list of accepted tests can be found here: Accepted English Language Tests

INTO University of East Anglia

If you do not meet the English requirements for this course, our partner INTO UEA run pre-sessional English courses which can be taken prior to the start of your course. For further information and to see if you qualify please contact intopre-sessional@uea.ac.uk