| CODE | ECN3302 | |||||||||
| TITLE | Economic Data Analysis using Python | |||||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | |||||||||
| MQF LEVEL | Not Applicable | |||||||||
| ECTS CREDITS | 4 | |||||||||
| DEPARTMENT | Economics | |||||||||
| DESCRIPTION | This course combines economic theory and practice with modern data science tools by equipping students with essentials of scientific programming using Python, the industry standard for economic quantitative analysis. Designed specifically for economics students with no coding background, the module uses practical economic datasets to bridge the gap between economic theory and data-driven application. Students will master data management, visualization, and scientific computing to prepare for professional analytical roles. Real world economic datasets will be used to enrich the learning experience of this study-unit. Study-unit Aims: The study-unit aims to provide students (with no or few coding background) with the fundamentals of scientific programming. This will serve to augment economic theory skills with computational power to enhance economic analysis. The study-unit aims to provide students with skills in data handling and visualization of economic data. Learning Outcomes: 1. Knowledge & Understanding By the end of the study-unit the student will be able to: - Build algorithms to solve economic problems; - Translate economic logic into functional Python code; - Critically assess which computational tool is best for a specific economic dataset or economic related research question; - Construct interactive visualizations that clearly communicate economic trends and behaviour derived from available data to stakeholders. 2. Skills By the end of the study-unit the student will be able to: - Develop appropriate skills to build algorithms (with the appropriate coding language) to solve economic problems; - Develop skills to build interactive charts which can be used for simulation of economic variables; - Apply knowledge to the formulation of heatmaps and geographical plots to visualize spatial economic data; - Apply knowledge to deconstruct complex theories into logical sequential steps; - Develop skills in the general use of Python; - Construct (using code) and estimate regression models to identify relationships between economic variables. Interpret statistical output and diagnose model violations. Main Text/s and any supplementary readings: View reading list |
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| STUDY-UNIT TYPE | Lecture and Tutorial | |||||||||
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2025/6. It may be subject to change in subsequent years. |
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