EC244: Introduction to Applied Data Science for Economists
Juliana Cunha Carneiro Pinto
Module Leader
Introduction
This module introduces students to the practical application of data science techniques in economics and policy analysis. It provides hands-on experience with real-world datasets, using Python for data collection, cleaning, manipulation, visualisation, and reproducible reporting. The module bridges econometric foundations with modern data science workflows, preparing students for advanced study (e.g. EC349) and for analytical careers in policy, government, or business.
Principal Aims
This module aims to equip students with the practical data skills needed to work with real-world economic and policy data in professional and research settings. Building on core statistical and econometric foundations, the module introduces modern data science workflows used by economists for data acquisition, cleaning, transformation, visualisation, and reproducible analysis.
The module develops students’ ability to move from structured theoretical understanding of data to applied analytical practice using real datasets. Through hands-on work in Python, students will learn how to manage messy and complex data, communicate evidence clearly to technical and non-technical audiences, and produce reproducible analytical outputs relevant to policy, business, and social science applications.
A further aim is to strengthen quantitative employability and curriculum coherence within the Economics degree by providing a bridge between econometric foundations (e.g. EC226, EC203) and more advanced analytical modules such as machine learning, spatial analysis, and applied empirical research.
Principal Learning Outcomes
Formulate economic and policy-relevant data science questions.
Use Python (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas) and Artificial Inteligence (AI) for data manipuá02lation and visualisation.
Clean and transform datasets from multiple formats (CSV, JSON, SQL, API/Web scraping).
Produce reproducible workflows using Jupyter Notebooks.
Create ethical and effective data visualisations and dashboards.
Communicate data-driven insights for technical and non-technical audiences.
Understand core concepts in spatial data and spatial economics
Use spatial data to examine policy-relevant economic issues.
Syllabus
The module will typically cover the following topics: the data science pipeline and reproducible analysis; Python programming foundations using NumPy and Jupyter Notebooks; data manipulation and transformation with Pandas; handling non-standard data formats including SQL, APIs, JSON, and relational databases; data visualisation principles and practice using Matplotlib and Seaborn; spatial data analysis including geographic joins, shapefiles, and heat maps using GeoPandas; advanced visualisation and interactive dashboards using Plotly and Streamlit; reproducible workflow automation and project structure; ethical use of data and communication of data-driven insights.
Context
- Optional Module
- L100 - Year 2, LM1D (LLD2) - Year 2, V7ML - Year 2, L1CA - Year 2
- Pre or Co-requisites
- Modules: EC226-30 or EC203-30
Assessment
- Assessment Method
- Coursework (30%) + Exam (70%)
- Coursework Details
- Assessment (10%) , Exam (70%) , Final Project (20%)
- Exam Timing
- Summer
Subject Specific Skills
- Applied Economics
- Economic information
- Economic principles
- Research and debate
- Abstraction
- Analysis of incentives
- Analytical reasoning
- Analytical thinking and communication
- Creative thinking
- Critical thinking
- Policy evaluation
- Problem solving
Transferable Skills
- Applied data literacy and spatial visualisation
- Competence in Python programming.
- Communication of complex empirical results to policy and broad audiences.
- Teamwork and collaborative analysis through group assignments
- Data-based skills
- IT skills
- AI skills
- Numeracy and quantitative skills
- Research skills
- Information technology
- Math, Statistical, data-based research skills