Big Data and Machine Learning have never been more omnipresent for discovery and research in economics. They increasingly play a role in enabling policymakers to address important social and economic problems. Therefore, it is essential that data users and consumers can identify high quality data and understand the implications of poor data analysis.

In this course, you will explore how big data and machine learning are being used in economics and social science research. This course provides a practical, skills-based approach. You will be guided through the process and pitfalls of using and interpreting results based on big data from project inception to final data analysis. Practical skills will be developed through the completion of weekly data analysis tasks and coding exercises.

Topics covered include: poverty and disadvantage, education, gender disparities, Covid-19 impacts, and social-policy evaluations. In the context of these topics, the course will also provide an introduction to basic methods in data science, including machine learning for prediction, causal inference (econometric approaches and machine learning embellishments), and heterogeneous treatment effects. This course will discuss the benefits and drawbacks of each of Machine Learning methods in a non-technical manner and through real-world case-studies and applications.