- Docente: Anatole Cheysson
- Credits: 6
- SSD: SECS-P/01
- Language: English
- Teaching Mode: Traditional lectures
- Campus: Bologna
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Corso:
Second cycle degree programme (LM) in
Economics and Public Policy (cod. 5945)
Also valid for Second cycle degree programme (LM) in Applied Economics and Markets (cod. 5969)
Second cycle degree programme (LM) in Economics and Economic Policy (cod. 8420)
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from Apr 17, 2024 to May 23, 2024
Learning outcomes
This course provides theory and tools for using Python as a programming language in economic research. It (i) introduces students to the basic logic and syntax of Python (e.g. object-oriented programming) and (ii) emphasizes how to use it to perform Data Science-related tasks (working with relational dataset, big data, data visualization, among others).
Course contents
The course is targeted to students interested in using Python for empirical economic research. It is divided as follows.
Module 1 (data processing): presentation and practice with the libraries (NumPy and Pandas) for importing and working with datasets. Tasks covered: merging and reshaping datasets, importing/exporting large dataset, handling unstructured data, among others.
Module 2 (data visualization): presentation of aesthetics for data visualization. Presentation and practice with the libraries (Matplotlib and Seabon) for visually presenting data patterns.
Module 3 (data mining): if time allows, presentation of data mining libraries (Scrapy and BeautifulSoup) for information retrieving (i.e. web scrapping).
Readings/Bibliography
Dale, K., 2016. Data visualization with python and javascript: scrape, clean, explore & transform your data. " O'Reilly Media, Inc.".
McKinney, W., 2012. Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. " O'Reilly Media, Inc.".
VanderPlas, J., 2016. Python data science handbook: Essential tools for working with data. " O'Reilly Media, Inc.".
Teaching methods
Lectures and home assignments. Students may be asked to present material to lead discussion on some topics.
Assessment methods
Problem sets, final written exam, and/or development of individual or group projects (to be determined based on the total number of enrolled students).
Office hours
See the website of Anatole Cheysson