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Big and Smart Data Analysis (STG-MA-FCM-BSD)

STG-MA-FCM-BSD

Description

This course aims to providing students with a robust (albeit introductory) foundation in data analysis for policy making. By the end of the course, students will be able to both spot bad data analysis and understand the pillars of sound data analysis. The aim of the course is to enable students to apply data-driven approaches in their future roles within government, research, or policy analysis.

In the initial part of this course, we will embark on a short discussion of fundamental concepts in data analysis (and data manipulation). We will begin by honing students’ ability to discern and critique misleading statistics, ensuring they can identify potential pitfalls and misrepresentations in data-driven arguments. Moving forward, our focus will shift to understanding and addressing spurious correlations, delving into the intricacies of endogeneity and omitted variables to uncover the real patterns hidden within data. Finally, we will dive into the intricate task of disclosing causal relationships, emphasizing the importance of exogeneity and desing-based policy evaluation in drawing meaningful conclusions from data. Throughout this course, the discussion will be rigorous but not formal, leveraging intuition more than equations. We will ground our discussions in practical applications drawn from empirical studies within the social sciences. These case studies will provide real-world context and relevance to the topics covered, including the analysis of politician compensation, the examination of visa status and its correlation with crime, the study of public resource allocation and its impact on corruption, the evaluation of political campaigning strategies and their influence on voting behavior, and so on.

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