Programming and Data Analysis 5 credits

The ability to manage and analyse large and complex datasets is an essential skill for economists and business professionals. This course gives you the foundational programming and data management skills needed to work effectively in modern analytical environments. It will help you build a practical toolkit that supports both your academic work and future professional development. During the course, Python is used as the main analytical environment. You will learn fundamental programming concepts and how to import, clean, transform, merge, and structure data, as well as perform database queries exploratory analysis. Practical applications and hands-on exercises are central part of the course. The course also introduces the efficient use of Generative AI and digital tools to support programming and analytical workflows. After completing the course, you will be able to translate economic and business questions into structured analytical tasks, write basic programs in Python, prepare datasets, and apply descriptive and graphical methods to analyse real-world data. You will have a versatile skill set that prepares you for further studies in econometrics, empirical research, and applied data analysis in economics and business. **Connection to Research ** The course is research-linked in several ways. First, it introduces you to the computational and data-management practices that underpin contemporary empirical research in economics, finance, and related social sciences. You will work with research-style datasets and learn how transparent workflows, reproducible notebooks, careful documentation, and critical interpretation support rigorous academic work. Second, the course prepares you for more advanced courses in econometrics, applied economics, and thesis work by strengthening your ability to organise and evaluate empirical material before formal modelling. **Connection to Practice ** The course is strongly practice-oriented. You work hands-on with realistic datasets and complete tasks that resemble analytical work in business, policy, and consulting settings: cleaning messy data, combining sources, producing visual summaries, documenting workflows, and communicating results for decision-making. The emphasis on reproducible notebooks, visual communication, and structured problem-solving mirrors professional practice in data-driven organisations. The course therefore supports employability in roles that require analytical reasoning and practical data capability. **Connection to Ethics, Responsibility, Sustainability (ERS) ** Ethics, responsibility, and sustainability are embedded in the course through content. You are taught to reflect on data quality, transparency, reproducibility, and responsible interpretation of analytical results. After completing the course, you are able to discuss ethical risks linked to data work, including bias, selective reporting, misuse of visualisations, opacity in analytical choices, and the responsible use of generative AI tools. Sustainability and broader societal relevance are addressed through the choice of examples and datasets, such as inequality, labour markets, prices, and other policy-relevant economic topics.