MSc Economics & Data Analytics

Course Learning Outcomes

Applied Microeconomics

Upon successful completion of this module, students should be able to:

• Understand Core Econometric Concepts (LO1) Explain the purpose and scope of econometrics, including the basic principles of statistical and regression analysis as they apply to economics.
• Construct testable economic hypothesis (LO2) Conduct basic descriptive and inferential statistical analyses and interpret the results in economic and business contexts.
• Collect and analyse data to test hypothesis (LO3) Learn to collect appropriate dataset to test hypothesis. Familiarize themselves with econometric software methods (e.g., Stata, R) to organize, clean, and analyse datasets. Use these tools to perform regression analysis and diagnostic testing.
• Interpret Econometric Models (LO4) Interpret and critique the results of regression models, focusing on understanding assumptions, limitations, and implications of the results.
• Develop Analytical Skills for Decision-Making (LO5) Apply econometric results to inform business and policy decision-making, demonstrating how data can support or challenge economic hypotheses.

Data Analysis

Upon completion of the module, students should be able to demonstrate their ability to:

• Describe the key concepts related to the collection and analysis of quantitative data using appropriate statistical techniques (LO1).
• Apply and effectively interpret the results of statistical analysis methods in economic research (LO2).
• Design and conduct an empirical research project using quantitative data (LO3).

Data Mining and Machine Learning

On successful completion of this module the student should be able to:

• LO1 Demonstrate a comprehensive understanding of data mining and machine learning fundamental concepts, algorithms and process
• LO2 Demonstrate an understanding of the purpose and breadth of areas of application of data mining and machine learning
• LO3 Identify machine learning algorithms appropriate for particular classes of problems
• LO4 Undertake a comparative evaluation of the strengths and limitations of various data mining techniques
• LO5 Comprehensive understanding of the state of the art techniques in data mining and machine learning
• LO6 Demonstrate capacity to perform a self-directed piece of practical work that applies data mining techniques in a real-world problem and considers potential commercial risk.

Econometrics

Upon successful completion of this module, students should be able to:

• Understand Core Econometric Concepts (LO1) Explain the purpose and scope of econometrics, including the basic principles of statistical and regression analysis as they apply to economics.
• Construct testable economic hypothesis (LO2) Conduct basic descriptive and inferential statistical analyses and interpret the results in economic and business contexts.
• Collect and analyse data to test hypothesis (LO3) Learn to collect appropriate dataset to test hypothesis. Familiarize themselves with econometric software methods (e.g., Stata, R) to organize, clean, and analyse datasets. Use these tools to perform regression analysis and diagnostic testing.
• Interpret Econometric Models (LO4) Interpret and critique the results of regression models, focusing on understanding assumptions, limitations, and implications of the results.
• Develop Analytical Skills for Decision-Making (LO5) Apply econometric results to inform business and policy decision-making, demonstrating how data can support or challenge economic hypotheses.

Macroeconomics in a Global context

• The student will be able to evaluate the dramatic effects of existing economic activity on the global climate and its living species.
• To understand the integrating force of international trade and capital movements, and the effect of shorter-term financial commitments
• To critically evaluate the dynamics of international economic institutions and their significance, and their effects on regional and local economies
• Be able to offer alternative empirically based explanations for international changes in investment, production, prices, and trading patterns globally.

Modelling of Data and Predictive Analytics

On the completion of this module students will be able to:

• Use python programming language to access and manipulate data.
• Understand of the differences between time series, cross sectional and panel data, and how these differences influence economic and predictive modelling.
• Relate data, models and predictions to real world, commercial and academic problems.
• Construct one predictive model, either in Python or an econometric software package (e.g., R or Eviews).
• Conclude your analysis by proposing data driven solutions or recommendations.

Business Consulting project – data analysis

On successful completion of this module, students will be able to:

• Demonstrate business acumen and team working skills by running a virtual business in collaboration with other students and engaging in group presentation
• Understand and apply relevant theoretical concepts and analytical tools to investigate a chosen business/ industry, identify issues and suggest appropriate evidence-based solutions
• Understand the environment that the chosen business/ industry operates in, and the trends, challenges and opportunities that this might present for its current and future operation
• Evaluate economic, financial and non-financial data, and provide thorough and clearly presented analyses in text and graphically
• Demonstrate originality and self-direction to write up an independent business consultancy report, which is rigorously researched, thoroughly analysed, relevant, clearly presented, structured and communicated, and produced to expected postgraduate standards.

Economics Dissertation

• Understand and apply relevant social/scientific research methods to a significant research question.
• Demonstrate a strong capability in applying technical software packages to analyse economic data.
• Examine and critically evaluate the literature pertaining to the field of research.
• Evaluate complex ideas with analysis and critical evaluation in the research area.
• Demonstrate originality and self-direction to write up a dissertation, argued and structured rigorously to expected postgraduate standards.