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UCAS Personal Statement

Programme Overview

The BSc Economics, Finance and Data Science is set against the backdrop of increasing demand for graduates with academic training in economics and finance whose analytical skills are complemented with knowledge of data science and coding capabilities. Whilst economics and finance form the basis of rigorous undergraduate programmes in leading institutions across the sector, these have not historically included the study of data science as part of the curriculum, leaving graduates to develop these skills independently. The BSc Economics, Finance and Data Science degree draws on the academic expertise of all Departments in Imperial College Business School (Economics and Public Policy; Finance; Analytics, Marketing and Operations; Management and Entrepreneurship) to offer students the rigorous study of economics and finance, enriched through the study of data science and its applications within these disciplines. A further dedicated sequence of modules develops the essential skills identified by employers. Through the study of economics students will analyse how households, firms and governments behave and interact to determine income, wealth and well-being, and hence inform both business decisions and public policy design. Years 1 and 2 include a theoretical exploration at the individual level through core Microeconomics modules, and at the aggregate level through core Macroeconomics modules.

The importance of evaluating frameworks against an evidence base is emphasised throughout, with students engaging in quantitative data analysis. Through the study of finance students will develop a core understanding of financial markets, financial institutions as well as the design of financial instruments. In Accounting students will construct and interpret financial statements, while in Corporate Finance students explore how firms can maximise value through financing and investment decisions, paving the way to finance options in Year 3. Both finance and economics interact heavily with mathematical and statistical methods. Through core Econometrics modules students develop an in-depth theoretical understanding of empirical methods relevant to economics and finance, alongside key applications enhanced by the study of data science. Students will learn to programme from the outset, which alongside modules in Machine Learning, Databases and Cloud Computing, will offer the tools with which to address a wide range of empirical questions, using both small and large datasets.

The core curriculum is further enhanced by a cross-cutting module designed to develop skills identified as essential by employers, such as strong communication and presentation skills (e.g. the ability to communicate ideas visually and verbally), effective teamwork in diverse organisations, critical thinking, design thinking and a creative mindset to problem solving. It integrates a new type of management skills training focused on Leadership, Ethics, Awareness, Diversity and Societal Impact (LEADS). In their final year students will be able to choose from specialist electives in Economics, Finance, Data Science, in addition to electives from all academic areas within the Business School. These reflect the broad scope of expertise within the Business School, in areas such as health, energy and climate change and innovation and entrepreneurship. The final year structure is sufficiently flexible to allow students to specialise in either of the three fields through appropriate selection of Year 3 modules, so as to enable access to leading Masters programmes in either of the three disciplines. Students who prefer to continue with a mix of electives across the three areas of study can also do so.

Learning Outcomes

Upon successful completion of FHEQ Level 4 of BSc Economics, Finance, and Data Science programme students will:
• Comprehend a range of microeconomic concepts and modelling frameworks and be able to competently apply them to analyse a range of decision problems of consumers and firms, using appropriate quantitative methods, and to evaluate the effects of economic policy on decisions and outcomes.

• Structure and solve economic and finance problems in mathematical format, as well as to interpret these mathematical solutions in terms of their “real world” economic context. Understand and utilise statistical inference to in the context of economics and finance, including probability distributions, confidence intervals, hypothesis testing and correlation analysis.

• Develop programming skills and the ability to “data wrangle” and visualise data. Understand and implement some commonly used data structures.

• Demonstrate knowledge and understanding of core macroeconomic, accounting and financial concepts and principles with reference to real life applications. Use basic statistical and computational techniques (e.g. using R or Python) to produce and analyse macroeconomic, financial and accounting data and apply basic problem-solving skills and mathematical techniques to analyse basic macroeconomic and financial models.

• Develop intellectual, cognitive and transferable skills such as communication and analysis of data, theory and evidence.

• Apply innovative and creative thinking and problem-solving skills to complex, ambiguous, uncertain, and systemic problems. Explore, define and reframe problems, and generate solutions or alternative approaches for existing ones.

Upon successful completion of FHEQ Level 5 of BSc Economics, Finance, and Data Science programme students will:
• Comprehend microeconomic modelling frameworks that relate to the interaction of decision-makers within a market and otherwise and be able to appropriately apply these to different contexts and to evaluate policy issues, interpret analytical findings, and critically evaluate these against relevant evidence.

• Demonstrate understanding of causal inference and the ability to apply econometric methods in the context of economics and finance, including randomised experiments, matching, regression analysis, instrumental variables and two-stage least squares, as well panel data and time series analysis.

• Formulate and solve different classes of optimisation problems via software. Demonstrate ability to run Monte-Carlo simulation. Understand core problems of machine learning (supervised and unsupervised learning) and to implement standard algorithms from ML. Demonstrate knowledge of data-base theory and different approaches to storing data.

• Recognise and explain the importance and practical implications of risk and uncertainty in macroeconomics and finance. Define and describe a selection of basic dynamic macroeconomic and
financial models, together with their applications to asset pricing, portfolio choice and macroeconomics. Demonstrate an understanding of corporate financial decisions, such as capital structure and discounted cash flow analysis.

• Apply relevant econometric and computational methods to develop computer code designed to analyse data/models and assess the effectiveness of macroeconomic policies and financial decisions. Further develop intellectual, cognitive and transferable skills, including written and verbal communication.

• Demonstrate the ability to articulate ideas and concepts visually and verbally, embracing uncertainty and seeking new opportunities by exploration and experimentation. Understand how to communicate and perform effectively within a team and within an organisation.

Upon successful completion of FHEQ Level 6 of BSc Economics, Finance, and Data Science programme students will:

• Have developed knowledge in a number of specialised areas in microeconomics and be able to synthesise and evaluate research literature in these areas, proficiently analyse microeconomic issues using advanced analytical methods and creatively formulate and address research questions pertaining to these areas.

• Demonstrate the ability to use state-of-the-art econometrics and data science methods to address applied problems in economics and finance. This arsenal of methods may encompass panel data (static and dynamic models), fixed and random effects models, the estimation of local average and marginal treatment effects using instrumental variables, GMM, textual analysis and big data.

• Define, describe and compare dynamic macroeconomic and financial models by using appropriate econometric, machine learning and computational methods, to provide economic policy and financial guidance. Apply financial economics to selected topics, such as: asset management, derivatives, risk management, banking and financial intermediation, corporate governance, and dynamic asset pricing.

• Justify, interpret and communicate insights from the evaluation of real-world problems in finance and economics. Transfer the analytical skills developed in the context of economics to other settings. Develop system thinking and apply empathy to define solutions via leadership, ethics, awareness, diversity and societal impact (LEADS).