BSc (Hons) Economics with Data Science

This module has three distinct but constituent sections. After a week of introduction, its first part, made up of five weeks of lectures, deals with different key thinkers in economics and the schools of economic thought that they gave rise to. The focus of this section is to present important Economists, their core ideas, and discuss why their viewpoints are so different. The student will be introduced to Adam Smith, David Ricardo, Karl Marx and Alfred Marshall. Through this sequence the student will become aware of different systems of analysis in the field of economics and start her/him thinking of the different frameworks of economic theorising.

The neoclassical school, following the work of Alfred Marshall, will be presented last indicating that the next two sections will be primarily concerned with tools and analytical arguments emanating from this tradition. The next seven weeks will be on microeconomic theory. In contradistinction with the plurality of the first part of the module, this part will focus on deductive reasoning used in mainstream rational choice and perfect competition theory. The module will cover preference theory, demand and supply, income and substitution effects, cost and revenue curves, perfect competition and partial equilibrium theory. This progression will end with a description of general equilibrium and the two welfare theorems. The focus here is the internal consistency of neoclassical microeconomics as exemplified in rational choice theory and competitive markets, and its combination of mathematical methods and deductive logic.

The last seven weeks will be on macroeconomics. In this section the focus will be on the following specific concepts: national accounting, inflation, unemployment and business cycles. The analysis of these concepts will be through the use of contemporary schools of economic thought (New Classical, New Keynesian, Post Keynesian, Austrian and Monetarist) and their analytical frameworks. By viewing these concepts through these different frameworks, the student is introduced into open ended discussions on these topics as different answers are equally valid as long as students can clearly identify the theoretical frameworks that he/she is using. This section’s purpose is also to introduce topics that will be investigated at greater length in the second year of the programme.

This module will introduce you to the fundamentals of programming and object orientation, including the following basic ideas of programming:

• variables
• memory and assignment statements
• control through conditional statements, loops
• functions and procedures
• objects and classes
• instance variables and methods
• arrays
• user interaction
• interaction between objects. 

This module introduces the mathematical methods used in the analysis of modern economics. This module is suitable for students both with and without an A Level in Mathematics (or equivalent). Students will revise and apply the basic concepts from algebra and differential calculus to relevant economic problems. Furthermore they will learn partial derivatives and second partial derivatives of functions of two or more independent variables, constrained and unconstrained optimization. The students will be taught how to use these tools in economic applications.

This module is aimed at novice programmers who have learnt some basic programming techniques. The course builds on this knowledge by developing your ability to combine short segments of code to create larger projects. 

You'll begin by developing their knowledge of object-oriented programming through learning about principles such as encapsulation and abstraction. You'll be presented with existing code and designs which you'll explore, complete and debug. You'll build upon an example application in the development of a final project.

You'll be expected to engage in both the technical and social implications of your work, considering the applications applicability to a human context. This may include the ethical and cultural impact of software. 

During this work, you'll apply taught techniques in organising, planning and evaluating your code.  

In this module, subtitled ‘Everything is a Text’, you will consider the value of different types of texts and ways of imparting knowledge and ideas. You will reflect upon your identities as learners and future professionals in the world, considering a range of contexts: the academic/educational context, personal settings and the eco-systems that you live and work in. These reflections will be used to inform your practices as academic learners.

You will explore academic literacies, different ways of knowing and consider what counts as ‘legitimate’ knowledge. You will engage with critical thinking, making arguments and establishing criteria to defend intellectual positions and these skills will be acknowledged as social practices that produce and reinforce meaning and frameworks of understanding and knowledge.

Furthermore, you will engage with a wide range of academic and non-academic material, individuals and environments in order to contribute to discussions regarding attitudes and assumptions about ideas and experience, including within labour markets, cultural hegemonies, distributions of power and the relationship between the individual and society. In this way, the social interactions, relationships and contexts that underpin academic literacies in higher education will be made explicit.

You will discuss these ideas with students and tutors from the different subjects at Goldsmiths, and learn to be part of the wider university community. You will also be able to submit an assignment which could be a written, graphically designed, audio, video, or negotiated project. You will get to choose the assessment that best shows what you can do.

This module, subtitled ‘Researching Our World & Lives’, builds on the conceptual and contextual foundations of Identity, Agency and Environment 1.

You will learn how to conduct academic research and will be offered the opportunity to broaden and deepen your understanding of the relationship between your own interests, skills, values, career and non-career aspirations, the concepts, theories and contexts of your discipline, and the world.

You will reflect upon your identities as researchers, and learn how the research skills you’ve acquired both within your studies and the world more generally can be related to problem-solving in a wide range of contexts. You will consider your agency as researchers, what you can and cannot research, the ethical issues involved, and think reflexively about your position as a researcher in a range of environments and eco-systems.

Formal conventions of academic research and writing will be integrated into your individualised contexts and goals, enabling the expression of ideas and perspectives that may challenge the status quo. The module will encourage creativity, activism, decision-making and the formation of judgements leading to action-planning in relation to research topics and types of evidence, and professional planning.

You will learn to critique your own subject disciplines. Interdisciplinary sharing of knowledge will ensure that assessment and learning practices provide you with the opportunity to develop new lines of thinking and knowing, within formative collaborative learning and research communities.

This module builds on the first year Introductory Economics module and the Mathematics and Statistics for Economics and Business module to give a more in depth perspective of microeconomic theory and its technical apparatus.

The first six weeks give an overview of the technical and theoretical analysis that forms the core of the neoclassical theory of consumption, production and market interaction. It introduces the following topics: choice under uncertainty, inter-temporal choice, incomplete and asymmetric information, principal-agent problem, basic game theory, dynamic and static oligopoly, price differentiation, markup pricing and market concentration.

In contrast, the rest of the module focuses on aspects of microeconomic behavior that do not conform to rational choice theory as developed in traditional neoclassical economics. Each week will explore a different alternative approach, icluding: Simon’s Bounded Rationality, Sen’s Capability Approach, Behavioural Economics.

The module aims to provide a good understanding of the key areas of macroeconomics, through the analytical tools of different schools of thought.

This module covers the evolution of macroeconomic analysis through a succession of key models, including Keynesian, Monetarist, New Classical, Real Business Cycle, New Keynesian, and Post Keynesian approaches. It studies the analytical details, the underlying economic assumptions, and the historical context in which they emerged. It also provides an introduction to structural theories of business cycles. You study economic growth and economic development, explaining the differences between the two and using historical examples. Classical, Keynesian, and neoclassical theories, as well as structural dynamics are discussed.

The final two weeks are devoted to the political economy of economic policy, from the viewpoint of different schools of thought: controversies on the effectiveness of fiscal and monetary policy, individual rationality applied to policy decisions, economic and political disagreements, models of voting on macro-policy, and recent developments.

This module introduces the quantitative methods used by economists in their empirical work. You will be technically trained in estimation theory, and introduced to broader questions regarding the use of these tools in applied analysis.

You study the basic properties of the normal distribution, and the difference between the normal and other types of probability distributions. You also learn hypothesis testing and the basic theory for linear regressions and the use of ordinary least squares in empirical analysis. Furthermore, you will learn about problems that may arise with this estimation method.

This is followed by two weeks of methodology on statistics for the social sciences, and a philosophical discussion on the appropriate use and limits of these techniques in analysing social phenomena.

This module will focus on the application of quantitative theory using data sets that you were introduced to in the Quantitative Economics module.

With the guidance of the lecturer and associate lecturers, you will find and collect appropriate data (from Eurostat, OECD, UK and other national governments databases, etc.), form a research question and come up with statistical tests in order to test your hypothesis. You will then write a report that explains your findings and the reason you used this specific estimation tool in relation to your data set. An integral part of this is to consider the limitations of your statistical toolbox in analyzing the specific data set, and more broadly, the limitations of statistical analysis in answering policy or theoretical questions.

An important part of this module is to train you in using computer packages to analyse real data. Students will learn to use computer programs to run regressions, do hypothesis testing, correlation analysis and, more generally, analyze the raw data you have collected.

This module’s central purpose is to investigate the methods of historical enquiry useful for an economist and give a survey of the economic and social conditions from the industrial revolution until the fall of the Berlin Wall.

The first week covers historiography with special emphasis on the techniques economists and economic historians use. This week will focus on the various methods economic historians use, from statistical and data analysis, to economic theory applications and narrative histories, analysing the strength and weakness of each approach for different types of enquiry.

The rest of the module provides an overview of key economic and social changes in Europe for the period from the start of the industrial revolution until the fall of the Berlin Wall. You will learn about the economic, social and technological changes that occurred during these 250 years. You will learn about changes in wealth, aggregate income and income distribution, consumer habits, wages, trade and monetary aggregates. You will also learn about institutional changes (Labor and Poor Laws, Welfare State) and social changes (family structure, workers unions, literacy) and the interplay between institutional/social and economic structures. Finally, you will discuss the changing technological conditions of society during this period and its ramifications for the social and economic organisation of society.

This module not only gives you general knowledge of the economic and social evolution of Europe, but also provides context and gives you an understanding of why economic theory developed the way it did.

Increasingly, computer systems in research and industry are designed to leverage large amounts of data. The data explored by such systems are rich and varied. They could include anything from the results of clinical trials to information gleaned from analysing millions of tweets to understanding how people talk positively and negatively about politics. In this module, you'll learn how to develop systems that operate in and make use of such data-rich environments. This module builds on other material in the programme such as mathematics and databases.   

This module will show you how to work with data. Getting data from various sources, visualising data in compelling, informative ways, processing data to make it useful and shareable, and reasoning with data to test hypotheses and make parameterised predictions. The module will also introduce you to a new language and programming environment that is well-adapted to languages for these applications.

This module will help students to develop a career plan and prepare for their employment search. It will cover practical aspects such as writing a CV, a cover letter and application forms and developing an internet presence through websites such as Linkedin. It will give students the tools to prepare for interviews, including case interviews, assessment centres, and psychometric tests. It will cover the development of soft skills such as leadership and networking skills as well as the development of commercial awareness through business case studies. The students will also be introduced to current theories of career development and employability.

Lots of students join Goldsmiths because they want to make a difference in society, to bring about positive change and develop skills and experiences which will allow them to access exciting careers. Goldsmiths’ Social Change module will allow you to do work on group projects with students from other departments to bring about change. You’ll be introduced to the UN’s Sustainable Development goals and core project management theories and practices allow you to work across a number of weeks towards a final Festival of Ideas where you’ll report work back to the academic and local community.

This module intends to broaden your knowledge beyond the Classical Linear Regression Model (CLRM) estimated by Ordinary Least Squares (OLS). You'll focus on multiple regression, violation of the assumptions of the CLRM, and issues relating to time series econometrics.

he module draws on datasets used by macroeconomists in their analysis of aggregate economic performance. You'll learn how to employ econometrics to analyse and test relationships using annual or quarterly data on investment, consumption, output (GDP), interest rates, wages and consumer and producer prices, and employment. Topics studied this term include:

• Dynamic modelling
• Conditional expectations
• Multiple regression analysis
• Heteroscedasticity
• Models using cross-section, time series, and panel data
• Autoregressive distributed lags
• Autocorrelation
• Cointegration.

This module builds on the Econometrics mdoule by extending your knowledge in the field of multivariate analysis. You will gain more detailed knowledge of econometric techniques in the following fields:

• financial econometrics and other high-frequency data sets
• panel data that includes stationary and non-stationary panels
• Vector Autoregressive Models

You will also extend your knowledge of matrix algebra and its applpication and use in different topics.

Throughout this module you will not only extend your knowledge in statistical theory, but also gain practical experience in using appropriate computer packages to run statistical testing. You will enhance you knowledge of Eviews, Stata and other computer software and will also be informed of the practical issues relating to the use and limitations of these computer packages.

This module gives you an introduction to machine learning and statistical learning. Topics include supervised learning (regression and classification as parametric and non-parametric methods), and unsupervised learning, but discusses also semi-supervised learning, reinforcement learning, and elements of the learning theory.

You'll explore recent applications of machine learning and deep learning by illustrating practically, using modern software libraries, how machine learning algorithms and methods are applied effectively, and how models’ performances can be optimised using real-world datasets. Ethical aspects related to machine learning are also discussed.

Visualisation is essential for understanding and communicating information, and for making informed decisions based on data. This module takes the view that data visualisation is a core interdisciplinary component of data science. Effective visualisation requires a combination of computational skills, statistical knowledge, an understanding of human visual perception, and a rigorous and creative approach to working with data.

In this module, you'll explore both the practical skills necessary for manipulating and visualising data, as well as the theoretical knowledge essential for making judgements about how to most effectively discover and communicate insights from data.

You'll become familiar with core visualisation tools and techniques within the Python data science ecosystem. You'll establish a critical approach to data visualisation, starting with interrogating how data is collected, continuing throughout each stage of the visualisation process culminating in the communication of information and possible real-world implications. You'll gain practice answering questions with data and build confidence in presenting your findings, ensuring they are trustworthy and valid. You will gain knowledge of the human visual system, which is essential for designing effective visual communication.

The module introduces you to data mining techniques and methods utilised in the process of discovering patterns in data generated in various fields such as business, financial, social, medical etc., that are abundantly available nowadays.

You'll learn practical skills through data mining algorithm implementation, and through conducting knowledge discovery in data with specialised software and libraries. You'll explore the applicability of data mining techniques in areas such as text mining and sentiment analysis, financial applications and credit scoring, prediction modelling in health.

Neural Networks are widely used techniques for modelling and classifying data. They are used in industry for data analysis applications such as image classification, speech analysis and regression tasks.

This module will provide you with specialised theoretical and practical knowledge of a range of Neural Network architectures that are appropriate for data-oriented tasks. This module is complementary to the Machine Learning and Artificial Intelligence modules in the programme, focusing on the area of neural computation.  

You'll be introduced to the theory and practice of neural computation. You'll learn the principles of neuro-computing with Neural Networks widely used for addressing real-world problems such as regression, pattern recognition and time-series prediction. 

Artificial Intelligence is a broad discipline and includes many ideas and techniques. This module will focus on the latest developments in this rapidly expanding field. The emphasis is on solving real-world problems and building creative applications with state-of-the-art technology.  

Topics will vary, and may include deep learning, generative deep learning, fully and partially observable environments, environments containing other artificial intelligences, deterministic, stochastic and dynamic environments, problem solving as search, inductive, deductive and probabilistic reasoning, knowledge representation and learning, philosophical and moral dimensions, challenges for society, possibilities and AI history.

This module gives you an understanding of the international economy that integrates the real and monetary sides, and the economic and the political spheres. Different traditions of economic analysis will be used to shed lights on three main issues: the European Union and the Eurozone, China’s trade policy, and the international monetary system since Bretton Woods.

The first five weeks concentrate on trade. They cover the standard tools (comparative advantage, neoclassical trade theory, and new economic geography) as well as analytical and historical perspectives on international competition. It focuses on different insights concerning economic analysis and policy, as well as the integration with the political dimension.

The last five weeks focus on the international monetary system. By combining the standard tools of international macroeconomics with historical and analytical perspectives, the aim is to understand the evolution of the international monetary system since Bretton Woods, and its effects on national economies. Special attention is given to currency areas, especially the Eurozone, and to the connection between monetary arrangements and real and political aspects.

This module extends your knowledge on key microeconomic topics and gives you an interdisciplinary perspective. It focuses on social questions and introduces, in a critical manner, the types of answers economic theory provides. The four core questions investigated are:

• What goods should the government provide, and how should this provision be funded?
• How do we account and care for future generations?
• How do we deal with environmental issues?
• How does the government decide what action to take in a democratic society?

This module explores the emerging field of behavioural economics – a body of work that lies in the interface between economics and other disciplines, such as psychology, sociology and philosophy.

The module builds on introductory economics knowledge. It engages in a detailed, critical analysis of the assumptions that characterize homo economicus – the rational economic actor. It outlines important empirical challenges and criticisms levelled against viewing agents as expected utility maximisers. In doing so, it covers different strands of behavioural economics – both classical (developed primarily by Herbert Simon) and modern (developed, among others, by Daniel Kahneman, Amos Tversky and Richard Thaler). It examines their historical origins, pioneers, philosophical underpinnings, methodological differences and important insights. 

The module explores psychological and philosophical foundations of human decision makers and examines the role of cognitive limitations, constrained computational abilities, emotions, social preferences and the decision environment in shaping the behaviour of human agents. It develops fundamental notions such as bounded rationality, satisficing and heuristic decision-making and presents a selection of models that employ them. It also sketches the different fields of economics and social sciences where these insights are fruitfully applied. The ethical and philosophical conundrums associated with translating some of these insights in to actionable public policy and designing institutions are also analysed.

This module extends your knowledge in econometric analysis by introducing you to the historical development of the tools that econometricians use, and by engaging you in the methodological and practical limitations that real world statistical analysis of social data faces. The focus of the module is the changing landscape in data collecting and inference by national governments and big organizations from the 1930a until today. You will consider core questions of doing quantitative analysis, such as how much does econometrics explain? What is the right methodology for social statistics? And what questions can and cannot be explained by data inference?

You will investigate three different aspects of the time series econometrics toolbox in depth to give context to the technical training from previous modules. These aspects are:

• A historical survey of the development of econometric techniques from the 1930s until today
• The development, limits and strengths of data collected by national governments for inference and policy analysis
• Open questions in the methodology of data analysis in the social sciences

Furthermore, you will consider how data collecting by national governments and other institutions has changed since the advent of the internet and the big data revolution. This follows naturally the progression of more data that national governments collect and collate since the beginning of the twentieth century, with the main difference being that the scale is now of a new level, and the type of data substantially different to the traditional census, national accounts or industrial production data that national governments habitually collect. New ways in which government organizations (e.g. Central Banks) use this new type of data will be presented and the analytical and methodological challenges of these new types of data will be analysed.

This module helps you develop key communication and presentation skills, in particular articulating economic ideas to economists and non-economists alike. The module focuses on case studies, which may include anti-trust legislation, macroeconomic reports on the UK, or any other applications of economic analysis.

You will learn to use PowerPoint effectively, present graphs, statistical findings and do background research to prepare for a presentation. Throughout the module, you are encouraged to write letters about economic issues to newspapers and to comment in well-known economics blogs.

This module combines resources from three fields of economic theory: macroeconomic analysis, history of economic thought and economic history.

The first seven weeks focus on six episodes in economic history, ending with the current (post 2007) crisis. The writings of different schools of economic thought are used and contrasted in order to see how different theorists understood and analyzed the crises and the types of solutions they offered to solve them. Discourses from outside economics (from psychology or/and sociology or/and anthropology) will also be utilized as competing interpretations that explain different aspects of these crises not captured by economic analysis.

Finally, the last three weeks will focus on the general concept of economic crises, and related concepts of risk and uncertainty, and discuss how these events alter our understanding of the workings of the market economy.

This module compares and contrasts the behaviour of individuals and institutions. It provides a more detailed understanding of the various levels of analysis that you have encountered throughout the degree and provides an applied discussion of the issues surrounding methodological individualism, including the social construction of the individual from different geographical and disciplinary traditions.

The first eight weeks of the module cover four levels: i) the individual; ii) firms and organisations; iii) the state; and iv) the supranational and international level.

The last two weeks are devoted to how individuals and institutions cope with uncertainty. The view of uncertainty as risk is integrated and contrasted with theories of strong and fundamental uncertainty, as well as perspectives from other social sciences.

This module explores the role of gender and race in areas of relevance to economics and management. You'll analyse gender and racial relations through both theoretical and methodological analyses, and look at how this relates to households, businesses and the wider economy.

You'll be introduced to multi-disciplinary theoretical viewpoints and use academic literature and everyday communication tools such as practitioner reports, blogs, podcasts and tweets. You'll gain the skills to critically evaluate current economic relations from a gendered and racial lens, develop suggestions to tackle current inequalities and communicate key insights to a wider audience with the help of a podcast.

Modules will be divided into two parts. The first part of each lecture will consist of introducing you to different theoretical viewpoints (e.g. stratification economics) and methodological approaches (e.g. social network analysis) while the second gives you the chance to take part in discussions of real-life case studies, and share your ideas through presentations with the class an element of their everyday life and its connection to conceptualizations of gender and race. 

The module will start with providing definitions of the concepts of gender and race and how these apply to management and economics, followed by weekly topics. Internalising the concept of de-disciplining (Ndlovu-Gatsheni, 2021), the weekly topics will be arranged according to different levels of institutional analysis (micro-, meso- and macro) and provide insights from diverse disciplines when explaining economic relations at this level of analysis. The particular focus lies in exploring how structural and normative constraints impact everyday economic relations and how these could be overcome. While the first eight weekly topics will be determined by the lecturer, you'll get the opportunity to input into the curriculum yourself, and help to choose the last two topics.