Analytics and Data Science in Economics and Management I

A) Python Course for Data Science (A. Chessa):
1) Introduction to the language: basic statements (if, else, type casting), cycles and functions, examples and exercices;
2) Diving into the language: advanced types: sets and dictionaries, classes and modules, using PIP and ipython, examples and exercises;
3) Scraping the web: introduction to BeautifulSoup, the regular expressions module re, the request module, examples and exercises;
4) Introduction to Plotting: basic numpy, plotting overview, examples and exercises;
5) Data science utilities: introduction to SQL (sqlite/mysql).

B) Causal Inference and Evaluation Methods (F. Mealli): This part of the course deals with statistical methods for inferring causal effects from data from randomized experiments or observational studies. Students will develop expertise to assess the credibility of causal claims and the ability to apply the relevant statistical methods for causal analyses. Examples from many disciplines: economics, education, other social sciences, epidemiology, and biomedical science. Evaluations of job training programs, educational voucher schemes, changes in laws such as minimum wage laws, medical treatments, smoking, military service.

This course involves using the software R, which is available for free. No previous experience with R is necessary. However, those without previous R experience should expect to spend extra time on assignments as they familiarize themselves with the program.

Prerequisites: Matrix Algebra + Optimal Control + Foundations of Probability & Statistical Inference