MSc Economics and Data Analytics

• You will acquire deep understanding of microeconomic theory and its practical applications.
• You will solve economic problems and develop analytical and problem-solving skills in applied contexts.
• You will be equipped with the ability to interpret and critically assess empirical evidence in microeconomic policy debates.
• You will be able to apply microeconomic principles to address contemporary economic issues also evaluating the role of governments

The Data Analysis module provides an introduction to research methods used in the Social Sciences, particularly in Economics research. The focus of this module is on the use of statistical methods.

Students will learn how to:

1. pose simple, clear research questions and structure a research project.
2. collect, clean and organize data;
3. describe data using simple statistics and graphs;
4. use regression analysis in cross-sections;
5. apply critical interpretation and discussion of results from such analysis.

   The main tool that you will learn to use for these purposes is Stata. This module can be adapted to use another software such as R and python depending on IT lab facilities.

In short, you will learn about different types of datasets, how to use the data, and different ways to visualise the data. You will also gain skills in research design and how to apply data to answer questions that arise in Economic, Policy, or other Social Science research. You will learn and gain understanding on how to interpret regression analysis and will be able to discuss and defend your analysis.

The aim of this module is to enable students with no prior exposure to rigorous empirical analysis to learn about data analysis and quantitative methods and their uses and limitations. Students will learn to clean, organise, visualise, and interpret data for the purpose of answering questions that are asked in Economic research.

This module serves as an entry-level course in econometrics, designed for students with backgrounds outside Economics and Data Analytics, such as Business, Management, and Finance.
Students will gain foundational knowledge of Econometric principles and methods, with a focus on applying Data Analysis techniques to solve real-world economic problems. The module will balance theoretical understanding with practical data handling skills, using accessible software (such as Stata or R) and focusing on real-world applications. By the end of this module, students will be prepared to apply Econometric analysis within economic research, business, and policy contexts, providing a foundation for more advanced quantitative studies (e.g., doctoral level studies).

The module will cover statistical methods based on the Econometric literature that can be used for causal inference in Economics and the Social Sciences more broadly, empirical analyses, with focus on the effects of counterfactual policies, such as the effects of implementing a government policy change, changing a price, or introducing new products.

In this module, you will learn how these empirical tools can improve an understanding of economic policy and decision-making using data and theory. You will learn how to pose a testable question, how to retrieve data, how to handle the data with a software, and most importantly, how to interpret the quantitative results and apply it in the real world.

This module is aimed at providing students with an understanding of the global nature of the activity of the modern economy and its effect on the macro-economic policy of states. The long-standing tendency for states’ economic operations to be treated in isolation from one another has led to a continuous neglect of the often overwhelming contextual factors in the formulation of theory, such as the importance of the sustainability agenda worldwide. The module presents an integrated perspective of the key dimensions of global economic activity rather than the ‘hybrid’ treatment of the past conceptions of separable economic categories. This module recognises the interpenetration of global economic activities between the key actors.
Students will be able to investigate and assess macroeconomic phenomena through reference to the day-to-day activities of economic agents. These are central to the entire course in which this module sits.

The aims of this module are:

• You will acquire a solid understanding of macroeconomic theory and its practical applications at the international and global level.
• You will develop analytical and problem-solving skills in a global economic context.
• You will be able to apply macroeconomic principles critically to address contemporary global economic issues.
• You will be equipped with the critical capacity to assess the role of empirical evidence in macroeconomic policy arguments.

This module begins with a foundation-level of Mathematics and Python appropriate for non-cognate entry to a master’s in Economics and Data Analytics.
The Mathematics will be primarily of an applied nature, including linear algebra (matrices). The Python will begin with use of Jupyter Notebook, handling of data and simple functions. Subsequent to this, we will use Python to acquire data via to public API, manipulation of data, and the running of a predictive model, for example Long Short-Term Memory (LSTM).
At the same time as establishing Python skills, the module will provide students with a solid grounding in the application of data for Business and the use of analytics to create commercial value.

The aims of the module are:

• You will have introductory skills in Python programming language.
• You will be fluent in the differences between types of data and how to handle them.
• You will be able to translate real world challenges into data and models, and vice versa.
• You will be able to construct at least one type of predictive model.
•  You will be able to put forward data-driven results, solutions, and proposals.

This module provides an appreciation of Data Mining and Machine Learning fundamental concepts, algorithms, and process. It covers Machine Learning algorithms and Data Mining techniques for data analysis, pattern mining, clustering, classification and regression. It equips students with practical skills in applying Data Mining and Machine Learning techniques in real-world analytics problems.

The aims of this module are to:

• provide students with an understanding of Data Mining and Machine Learning fundamental concepts, algorithms, and process
• understand the purpose and breadth of areas of application of Data Mining and Machine Learning
• understand and compare the techniques and tools available for various types of data analytics problems
• develop students with practical skills in applying data mining techniques to solve real-world analytics problems

The Business Consulting Project is a 60-credit year-long module which is an alternative core module on the MSc Economics and Analytics course. It builds on the previously-studied modules, which will give students an opportunity to apply and integrate their learning from the programme of study to the module activities and assessment.
The module will also introduce students to the analytical tools and key processes of Business Consultancy. The analytical frameworks will provide students with a useful structure for analysis and thinking and guide their decision-making process.
Some of the key processes used in Business Consultancy are the identification of main problems or challenges facing an organisation or an industry, data analyses, presentation of solutions, and recommendations. Students will engage with these processes by participating in the Business Simulation Exercise and by completing the Business Consultancy Report.

The Business Simulation Platform replicates realistic market environment and allows students to apply their knowledge in practical Business situations. In the Business Simulation Task, students will be involved in running their own virtual business in collaboration with other students. They will have to consider multiple factors and real-world Business situations, analyse financial and non-financial data, and make well-reasoned decisions by the set deadlines. Students will then reflect on the decision-making process and their teamwork in a formal Business presentation.

In the Business Consultancy Report, students will be required to demonstrate thorough understanding of their chosen real-world Business or Industry, identify the strengths but also challenges/risks that it is facing, evaluate outcomes, problem-solve, and provide suitable and feasible solutions. Students will be expected to engage in rigorous research on their chosen Organisation/Industry and the environment it operates in, collect financial and non-financial data and statistical evidence, apply analytical tools and techniques, and present their findings and analyses clearly, both in text and graphically, to produce a well-structured, clearly-communicated and thoroughly-referenced Business Consultancy Report of postgraduate standard.

The Business Consultation Project will represent a significant piece of work from which students can reflect on the higher-order academic skills developed through the course. Students will also develop a range of transferable skills and thorough knowledge of the Business/Industry they are interested in, which may support their employment. The skills acquired through the module include research skills, analytical skills, critical thinking, problem identification and problem-solving abilities, time management, teamwork, communication skills, including presentation of large sets of data, and report writing skills.

This module aims to:

• engage students in a major piece of independent research on a Business or Industry that the student has interest in or in which the student works or wants to find future employment.
• engage students in experiential learning through the Business Simulation Exercise.
• facilitate learning through individual and collaborative practical activities.
• assimilate knowledge and understating that students have gained from the taught elements of the course and apply the key concepts and analytical tools in the practical context.
• apply and develop research analysis and report writing skills to produce a clearly written and well-presented text.
• enhance students employability skills via teamwork and group presentation, as well as research on the Business and Industry of their choice.

The Dissertation will give you the opportunity to produce an individual and sustained piece of original work that addresses a specific area (of your choosing) in the fields of Economics and Analytics. The dissertation will allow you to demonstrate your intellectual and conceptual skills through your background research and application of theoretical knowledge.

The Dissertation is the final module in the study of your MSc degree and it will enable you to utilise and integrate your learning from your programme of study by applying aspects of your learning to a particular topic of investigation. In undertaking this work, the Dissertation will develop your analysis, critical evaluation, and self-reflection skills.

You will be supported by research workshops providing a thorough understanding of quantitative research methods, how to design and carry out independent research, as well as how to support and justify conclusions drawn by data modelled with software packages. As independent learners you will be supported by an assigned Supervisor, who will support you through this learning process.

This module aims to:

• allow students to engage in a major piece of independent research in the fields of Economics and Analytics
• assimilate knowledge and understating from the taught elements of the course and apply to a topic of your choice
• provide students with a thorough understanding of scientific and social (quantitative and qualitative) research methods
• apply the use of technical software packages to model quantitative data
• develop students analytical, critical evaluation, and time-management skills
• apply and develop research analysis and writing skills to produce a clearly written and well-presented text which includes all the elements required for the Dissertation, using appropriate research methods to analyse and synthesise research findings.