The program of the course is split in three main parts.
First Part of the course: Data Management.
We will cover the basic techniques and methodologies of data management, analysis and cleaning of datasets. Students will do practical examples by means of Python and Stata.
Second Part of the Course: Intermediate Statistics.
Studens will be introduced by means of a solid recap of statistical topics to the theoretical basis behind the implementation of Data analysis studies. In this part we will cover the proofs of some fundamentals statistic theorems (Weak Law of Large Numbers, Central Limit theorems) as well as the properties of some key statistical distributions. We will also the Montecarlo Sumulation method in Python and Stata.
lo studente verrà introdotto, attraverso un solidriepilogo dei concetti base di statistica alle basi teoriche necessarie all’implementazione dei processi di data analysis. Durante questi moduli lo studente verranno rivisti alcuni teoremi statistici (Weak Law of Large Numbers, Central Limit thorem) e proprietà di distribuzioni statistiche(4 ore). Inoltre applicazioni pratiche introdurranno lo studente al Metodo Montecarlo (Python e Stata).
Third Part of the course: Applied Econometrics and Machine Learning.
In this part of the class we will cover methods of Casual Inference as well as an introduction of Machine Learning.
The students we will be introduced to the concept of CEF and to discuss empirical results in light of this interpretation.
Subsequently, an early introduction to randomization trials and IV methods will be discussed. Finally, students will be introduced to the Machine Learning Methodology