Economics and Data Science

Core Modules

Year 1

• Principles of Economics

  Principles of Economics is a first-year undergraduate module in how the economy works. The module is suitable for students with or without A-Level economics or equivalent. We will cover the basic theories of macroeconomics (that of the economy as a whole) and microeconomics (the behaviour of individuals, firms and governments and the interactions between them).

  The module adopts the state-of-the-art CORE approach (Curriculum Open-access Resources in Economics) to teaching Principles of Economics. The approach has three pillars which we rely on throughout the module:

  • Formulate a problem that our society is facing now or has faced in the past;
  • Build a theory to explain and solve the problem;
  • Evaluate the usefulness of the theory by using data observations and more novel theories.
• Data Skills for Economists

  Data Skills for Economists is about understanding the data we encounter constantly in everyday life and the data that social science researchers create as they explore and analyse the world around us. We'll endeavour to understand such questions as:

  • Where does data come from and how can we harvest it?
  • What useful information does the data contain?
  • How can we create new data to generate useful insights?

  Computers equipped with statistical software are a big part of the answer to the third question (above) so, accordingly, you'll spend much of your time learning to analyse and display data using the R statistical software package (R is the industry standard).

  We'll develop an ethos of clear communication of numerical information that will be supported by our growing understanding of statistical concepts and our growing proficiency with computers.

  Simultaneously, we'll delve into the seamy underside of the tricksters who try to fool you with falsified data. Understanding their game can provide at least some degree of inoculation against their attacks.

• Applied Economics and Policy

  In this module you will develop an understanding of information surrounding economic institutions, economic history, applied economics and policy & experimental & behavioural economics. In the seminars, you will discuss each topic and learn among other things how to write an essay, how to present, how to collect economic data, how to find relevant economic research, and how to think like an economist.

   

• Mathematics for Economics

  The aims of the module are to cover the basic mathematical and quantitative tools used by economists every day. The module gives an emphasis to the mathematical tools, which are applicable to solving a wide range of economic problems. The first half of the module is devoted to linear algebra, specific functions of one and more variables used in economics, manipulating those functions and finding their minima and maxima. In addition, the first half of this module delivers the rules of integration and differentiation, which prepares the you to apply constrained and unconstrained optimisation techniques in their subsequent 2nd and 3rd year of studies. Constrained and unconstrained optimisation techniques are also discussed. The second half of the module is devoted to optimisation theory which in turn will use the concepts of vectors and matrices, drawn from linear algebra, and require the study of concave functions. The knowledge of matrices will help you solve systems of linear equations, which are used in both microeconomic and macroeconomic planning and forecasting.

• Employability 1

  This module will be composed of an introduction to Employability, library resources, team building, and CV making. Career services will provide a session on self-awareness and decision making and library services will present their relevant resources. Finally, the Economics department will organise some team building exercises.

• Academic Integrity

  This module will describe the key principles of academic integrity, focusing on university assignments. Plagiarism, collusion and commissioning will be described as activities that undermine academic integrity, and the possible consequences of engaging in such activities will be described. Activities, with feedback, will provide you with opportunities to reflect and develop your understanding of academic integrity principles.

   

Year 2

• Microeconomics

  In this module you will develop an understanding of the models of individual optimisation and their applications. You will look at the key determinants of an individual’s behaviour in a variety of circumstances and the behaviour of firms in different market environments, such as perfect competition, monopoly and oligopoly. You will consider how changing circumstances and new information influences the actions of the economic agents concerned, and examine the properties of competitive markets and the need for government intervention to correct market failures.

• Macroeconomics

  In this module you will develop an understanding of macroeconomics and macroeconomic policy-making. You will look at a variety of contemporary and historical macroeconomic events, and the differences between the short, medium and long run. You will consider why some countries are rich and some are poor, why different economies grow at different rates, and what determines economic growth and prosperity. You will examine the role of monetary and fiscal policy, its impact on the economy and its limitations. You will also analyse how taxation, budget deficits, and public debt affect the economy.

• Econometrics 1

  The aim of this module is to provide you with a solid understanding of the essentials of empirical research techniques (i.e. econometrics) used by applied economists. The module will cover core econometric topics that are needed by all wishing to undertake econometric analysis, with a particular focus on topics in both time series and cross section econometrics that can be used by students of industrial, business and finance.

• Econometrics 2

  The aim of this module is to extend your knowledge of the essentials of empirical research techniques (i.e. econometrics) used by applied economists. The module will build on the topics covered in Econometrics 1.

• Introduction to Data Science

  The aim of the module is for students to understand how to use data in order to draw relevant conclusions, and how to present those conclusions. This aim includes both how to abstractly think about data and data analysis, and how to concretely implement techniques using the Python programming language. As the module serves as an introduction to the language, the first part of the module provides students with general programming tools, and, as the module progresses, these tools will become more and more focused on data science applications. Of course, being an Economics module, these applications will take the form of economic and financial investigations.

  As a lab class, students will have significant hands on experience with material and will investigate problems on their own, under the continued guidance of module leaders. By the end of the term, students will have the tools to be able to conduct their own research projects. As a final project, students will be provided with a dataset, asked to draw insights into it, and to present their findings both visually and narratively.

• Economic Modelling and Data Science

  The aim of the module is to advance the quantitative skills of the students and familiarize them with the use of Python and R in empirical work in economics. This includes collection, manipulation and presentation of data on economic phenomena, as well as the use of regression-based econometric techniques to draw causal inference and perform prediction and forecasting.

• Employability 2

  Career services will provide a session on how to be ready to apply for an internship at the end of the second year. Students will prepare for a psychometric test and will undertake a series of a mock interviews in order to improve their interview technique. Finally, students will attend at least one Econ@Work talk to be aware of professional life and challenges.

Year 3

• Employability 3

  This third-year course will deepen the elements covered previously in Employability 1 and 2. Career services will provide a session on how to be ready for employment at the end of the year. Students will prepare for a psychometric test and undertake a series of mock interviews in order to improve their interview technique. Finally, students will attend at least one Econ@Work talk to be aware of professional life and challenges.

• Dissertation in Economics

  The Dissertation provides you with the opportunity to undertake an extended piece of individual research work. You will use the econometric and statistical techniques you have learned about in the quantitative method modules taken earlier in the course.

• Advanced Econometrics I

  In this module you will develop an understanding of the theoretical properties of different econometric estimation and testing procedures under various modelling assumptions. You will learn to formulate, estimate, test and interpret suitable models for the empirical study of economic phenomena. You will consider how to apply regression techniques and evaluate the appropriateness of each econometric estimation method under different data limitations.

• Advanced Econometrics II

  In this module you will develop an understanding of the theoretical properties of different econometric estimation and testing procedures under various modelling assumptions. You will look at regression techniquies and learn how to apply relevant econometric and statistical methods to your own research. You will also evaluate the appropriateness of each of the economic estimation methods and the impact of consider data limirations.

• Financial Econometrics

  In this module you will develop an understanding of the principal techniques used in financial econometrics. You will look at why deviations from standard models are required to handle the peculiarities of financial data and consider how to interpret the theoretical techniques used in finance. You will also learn how to apply the techniques using standard econometric software packages such as STATA.

• Time Series for Macroeconomics with Data Science

  This module provides students on the BSc Economics, BSc Economics and Data Science, BSc Financial and Business Economics, BSc Finance and Mathematics courses with an optional course in macroeconomic time series analysis. The module material will focus on Bayesian time series analysis and will include examples of how to use Python via Jupyter Notebooks to implement the analysis in the course. The content will be useful for any student aiming to begin a MSc in Economics or macroeconomics. Upon completion of the module, students will be able to draw random samples from important distributions, estimate Bayesian regression models (including time-series Bayesian models), produce economic forecasts using Bayesian Time Series Models and communicate the results to others.

• Web-Scraping and Internet-Based Data Collection

  Empirical work requires obtaining the correct data for the question at hand, and nowhere has more data than the internet. Nonetheless, the data created by our modern internet driven economy is not always structured or easily available. The aim of the module is to provide students with the tools to be able to collect the right data to answer important economic questions. These techniques include using APIs, static web scraping, and browser emulation for scraping dynamic webpages, all facilitated by the Python programming language.

• Data Presentation and Visualisation

  Data visualization techniques are central to both for revealing structure and patterns in the data at hand and for conveying these patterns to relevant audiences -- it forms an integral part of the "data journey" from data acquisition, through analysis to product. The aim of the module is to provide students with the tools to be able tackle data of various structures and natures and select and implement appropriate and efficient tools data visualization and presentation in current standard software, including R. The content will introduce the notion of the psychology of data visualisation, outline key development tools and methods, describe how to achieve analytics storytelling for impact, distinguish between univariate- and multivariate analysis, and other forms of data -- e.g geographical and time-series data -- and structures including groups, hierarchies, networks and high-dimensional data. Students will have significant hands-on experience with material and will investigate problems on their own, under the continued guidance of module leaders. By the end of the module, students will have the tools to be able to conduct their own presentation and visualization choices, and create a portfolio of high-quality presentations of complex data structures.

• Machine Learning and Data Mining

  The module is designed to broaden the economist’s statistical toolkit beyond the standard regression analysis and introduces modern techniques in machine learning. Topics covered will include k-Nearest Neighbours, decision trees, Linear Regression, Logistic Regression, Neural Networks, comparing prediction algorithms and Model Selection, Ensemble Methods, Unsupervised Learning including Clustering and Density Estimation. Weekly lab sessions where weekly assignments are covered will complement the theoretical material presented in the lecture.

• Time Series for Macroeconomics with Data Science

  This module provides students on the BSc Economics, BSc Economics and Data Science, BSc Financial and Business Economics, BSc Finance and Mathematics courses with an optional course in macroeconomic time series analysis. The module material will focus on Bayesian time series analysis and will include examples of how to use Python via Jupyter Notebooks to implement the analysis in the course. The content will be useful for any student aiming to begin a MSc in Economics or macroeconomics. Upon completion of the module, students will be able to draw random samples from important distributions, estimate Bayesian regression models (including time-series Bayesian models), produce economic forecasts using Bayesian Time Series Models and communicate the results to others.

Optional Modules

There are a number of optional course modules available during your degree studies. The following is a selection of optional course modules that are likely to be available. Please note that although the College will keep changes to a minimum, new modules may be offered or existing modules may be withdrawn, for example, in response to a change in staff. Applicants will be informed if any significant changes need to be made.

Year 1

• All modules are core

Year 2

• Financial Markets and Institutions

  In this module you will develop an understanding of the economic principles underlying the working of national and international financial institutions. You will look at what a financial system is and does, and the distinct functions of each component. You will consider the key financial instruments and the relationship between assets, agents, and institutions, and learn to solve simple problems using quantitative and graphical tools. You will critically evaluate country differences and analyse the interdependencies and rapid change of the modern financial world.

• Industrial Growth and Competition

  In this module you will develop an understanding of the principal-agent problem, the Coase theorem, theories of the firm, the role of transaction costs, moral hazard, adverse selection, and issues surrounding organisation, investment, governance and expansion of corporations. You will look the role of incentivisation and how conflicts of interests shape economic interactions. You will consider the role of transaction costs in determining the existence, scale and scope of firms, and examine why government regulation may be inferior to market solutions when dealing with externalities. You will also analyse the developments of Anglo-American industrial and Japanese capitalism.

• Introduction to Economic Growth

  In this module you will develop an understanding of the process of economic growth at the world level, and the sources of income and growth differences across countries. You will look at Piketty's work on income distribution and economic growth, Malthus' work on population and economic growth, and Solow's standard economic growth model. You will examine why some countries are rich and some are poor, and consider the differences between countries that explain economic success and failures.

• Game Theory and Behaviour

  In this module you will develop an understanding of the mathematical models used to study and analyse strategic interactions between agents. You will look at the fundamental concepts in game theory as applied to economics in general and microeconomics in particular. You will become familiar with basic equilibrium concepts such as Nash equilibrium and subgame perfect equilibrium, and be able to find equilibrium outcomes of simple games including the use of backward induction.

Year 3

• Behavioural Economics

  In this module you will develop an understanding of the use of experiments to test economic theories. You will look at how individuals make decisions in markets, how individuals decide to spend money today or save it for future spending, the assumption of self-regarding preferences commonly made in standard economic models, and the ability to act rationally in a strategic environment. You will consider the issues raised by experimental design and critically evaluate the advantages and disadvantages of experimental methods.

• Monetary Economics

  In this module you will develop an understanding of the role of money in the economy. You will look at models where inflation shows persistence, the theory of monetary policy, monetary policy operating procedures and the central banking mechanisms. You will consider why inflation is persistent in the data and how the political forces affecting monetary policy-making may affect inflation.

• Advanced Economic Theory

  In this module you will develop an understanding of the mathematics of optimisation and of equilibrium models. You will look at the linkage between markets and Pareto optimality and consider the social outcomes that can be implemented in game-theoretic equilibrium. You will also examine the basic types of auctions and when and why they implement identical outcomes.

   

• Economics of Inequality

  In this module you will develop an understanding of economic inequality. You will look at the factors that determine wage differentials among workers from a theoretical and empirical point of view. You will consider why similar workers are paid differently and examine how labour mobility can improve the allocation of workers to firms, enhance aggregate productivity, and reduce inequality.

• Labour Economics

  This module will analyse the economic issues of behaviour and outcomes in labour markets. It will focus on topics relating to labour supply and demand, wage formation and earnings inequalities, e.g.: Labour Demand; Labour Supply; Human Capital and Compensating Wage Differentials; Inequality in Earnings; Labour Mobility; Discrimination; Unemployment.

• Philosophy of Economics

  In this module you will develop an understanding of the different approaches to national economic policy. You will consider the economic advantages and disadvantages of globalisation and look at the effects of tax-cutting, deregulation, privatisation, mixed economy, efficiency and income distribution.

• Topics in Economic History

  In this module you will develop your understanding of important topics from economic history, covering periods of economic growth and wellbeing, agricultural and urban development, globalisation and migration, banking and monetary systems, and the Great Depression and recovery.

• Topics in Development Economics

  In this module you will develop an understanding of both theoretical and empirical issues in Development Economics, such as the behaviour of credit and insurance markets in developing economies, the existence of poverty traps and the role of income, ethnicity, gender and caste in the development process.

• Corporate Finance

  The module aims to introduce the student to what factors affect corporate financial decisions. Particular emphasis is given to the concepts of Net Present Value and Risk. The learning outcomes include: Understand what the goals of a firm are; Understand how investments are valued (Internal rate of returns) in order to help with good financial planning); Understand the concepts of risk, agency costs and how they feed into financial decision making; Understand the process of price formation in financial markets; Understand venture capital and different types of debt finance and debt valuation, including leverage.

• Topics in Public Economics

  In this module you develop an understanding of the effects of government policy upon the economy and the design of policy. You will look at empirical methods for policy evaluation and discuss research carried out in public economics, on topics such as income taxation, welfare support, behavioural responses, and social security.

• Advanced Topics in Game Theory
  In this module you will develop an understanding of the mathematical models used to study and analyse strategic interactions between agents. You will look at the fundamental concepts in game theory as applied to economics in general and microeconomics in particular. You will become familiar with basic equilibrium concepts such as Nash equilibrium and subgame perfect equilibrium, and be able to find equilibrium outcomes of simple games including the use of backward induction.
• Topics in Finance