Modules  #2

Title: Foundations of Algorithms in Python

Instructor: TBD

Course website: https://tlouf.github.io/Py4DataSci-course

Contents:

– Functions, error handling and testing

– Composite data structures

– Efficient array computing with NumPy

– Dealing with data files: text, CSV, JSON formats

– Data visualization

– Analyzing data with dataframes

Prerequisites (IMPORTANT):  If you enroll only in module 2 without having attended module 1, prior to attending, make sure you have the required Python skills: to assess yourself, you should be able to perform in a couple of hours part A of these old exams: https://sps.davidleoni.it/#Esami-passati

Suggested Readings:

– SoftPython, Analisi dati (formati, visualizzazione, pandas): https://it.softpython.org/#B—Analisi-dati

– Allen Downey, Pensare in Python Italiano– 2nda edizione:  https://github.com/AllenDowney/ThinkPythonItalian/raw/master/thinkpython_italian.pdf

Teaching mode and Language: Classes will be exclusively in person and in English. No remote learning solutions can be requested or arranged.

Final test: More detailed information on the modes/platform to perform the final exam will be provided by the instructor upon the course beginning.

Module Requirements:

Computer: Students are required to bring their laptop computer (not a tablet!) with:

– at least 4GB RAM

– at least 5GB free on the hard drive

Software: Before participating, you are strongly advised to install the following software:

– Python:  follow the instructions from https://tlouf.github.io/Py4DataSci-course/0-getting-started/1-python-install.html, and ideally also from https://tlouf.github.io/Py4DataSci-course/0-getting-started/2-course-setup.html (assistance will be provided at the start of the course if necessary).

– Any browser

Title: Introduction to Statistics

Instructor: TBD

Contents:  

- Basic concepts: population, sample, parameter, statistic

- Descriptive vs. inferential statistics

- Describing data:

• through plots and tables

• through numerical properties (measures of central tendency and variation)

- Probability: definition and properties

- Random variables; probability distributions: discrete and continuous

- Sampling

- Confidence intervals

- Hypothesis testing

- Simple linear regression

Suggested Readings:  

• Illowsky B, Dean S, et al, Introductory statistics, OpenStax

• Newbold P, Carlson WL & Thorne BM, Statistics for Business and Economics, Eighth or Ninth (Global) Edition, Pearson Education Limited

• Lamberto Soliani, Statistica di base, ed. Piccin, 2015; also avilable online at http://www.dsa.unipr.it/soliani/soliani.html

Teaching mode and Language: Classes will be in English and online/blended. Links to attend will be shared on Moodle before the class starts 

Final Test: More information will be provided by the Instructor upon the course beginning.

Title: Methods in the economic, psychological, and sociological sciences

Contents:

– Introduction to macroeconomics (6 hours) 

Instructor: Diego Giuliani

Study materials: 

The CORE Econ Team (2023). The Economy 2.0: Macroeconomics. Open access e-text available at https://books.core-econ.org/the-economy/macroeconomics 

Students are expected to study by Saturday, 25 July, the following units:    

• Unit 1: "The supply side of the macroeconomy: Unemployment and real wages"; 

• Unit 2: "Unemployment, wages, and inequality: Supply-side policies and institutions"; 

• Unit 3: "Aggregate demand and the multiplier model".

– Introduction to cognitive data science (6 hours)

Instructor: Massimo Stella

Study materials: 

• Watch the video - https://drive.google.com/file/d/1YishdM1od1PBoLTYjR4xXAAEwILY6SPG/view?usp=sharing

• Haim, E., & Stella, M. (2026). Cognitive networks for knowledge modeling: A gentle introduction for data- and cognitive scientists. Wiley Interdisciplinary Reviews: Cognitive Science, 17, e70026. https://doi.org/10.1002/wcs.70026

• Kenett, Y. N., & Hills, T. T. (2022). Editors’ introduction to networks of the mind: How can network science elucidate our understanding of cognition? Topics in Cognitive Science, 14(1), 45–53. https://doi.org/10.1111/tops.12598

– Introduction to sociological methodology (6 hours)

Instructor: Elena Pavan

Study materials: 

• Watch the video - Joy Buolamwini: How I'm fighting bias in algorithms. https://www.ted.com/talks/joy_buolamwini_how_i_m_fighting_bias_in_algorithms 

• Jason Radford and Kenneth Joseph (2020), Theory In, Theory Out: The Uses of Social Theory in Machine Learning for Social Science, Frontiers in Big Data, https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2020.00018/full 

Teaching Mode and Language: Classes will take place in English and online/blended mode. Participants in this module are supposed to prepare the assigned materials autonomously.

Final test: More detailed information on the time and place of the test will be provided by the instructors upon the course beginning.