Economics and Data Science with a Year in Business - BSc (Econ) (Hons)

Royal Holloway, University of London

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.

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.

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.

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.

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.

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.

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.

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.

This year will be spent on a work placement. You will be supported by the Placements Office and the Royal Holloway Careers and Employability Service to find a suitable placement. However, Royal Holloway cannot guarantee that all students who are accepted onto this degree programme will secure a placement, and the ultimate responsibility lies with yourself. This year forms an integral part of the degree programme and you will be asked to complete assessed work. The mark for this work will count towards your final degree classification.

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.

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.

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.

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.

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.

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.

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.

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.

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.