MODULES #1

Title: Foundations of Python Programming

Instructor: Michele Tizzoni

Contents:
– Tools, basics
– Strings
– Lists
– Tuples, Sets, Dictionaries
– Conditionals, for loops
– While loops, sequences

Suggested Readings:

– SoftPython, Fondamenti: https://it.softpython.org/#A—Fondamenti

– 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 Italian. Tutoring sessions are not mandatory but are highly recommended. No remote learning solutions can be requested or arranged.

Final test: The final test will consist of a written exam administered on the last day of class. More detailed information on the time and place for the test will be provided by the instructors upon the course's beginning.

Module requirements:

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

– at least 4GB RAM

– Operating System: Windows 8 or more recent, 64-bit macOS 10.13 or more recent,Linux (any distribution)

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

– Anaconda for Python:  download page 

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: An introduction to economic, psychological and sociological sciences

Contents: 

– Introduction to economics (6 hours): 

– Introduction to quantitative methods in psychology (6 hours): Cognitive Data Science

Instructor: TBD

This course aims to highlight how cognitive and psychological data can be investigated within the framework of network science. Slides, reading activities, and online discussions will be the main techniques powering this course.

Suggested Readings: 

• Haim, E., & Stella, M. (2023). Cognitive networks for knowledge modelling: A gentle tutorial for data- and cognitive scientists. Available on Researchgate.

• Siew, C. S. (2019). spreadr: An R package to simulate spreading activation in a network. Behavior Research Methods, 51(2), 910-929.

• Stella, M. (2022). Cognitive network science for understanding online social cognitions: A brief review. Topics in Cognitive Science, 14(1), 143-162.

– Introduction to sociology (6 hours)

Instructor: TBA

We will cover the main epistemological and methodological aspects of research with social data using quantitative, qualitative, and mixed methods. We will focus more on quantitative methods, in particular on the nature of data available, the construction and use of valid research instruments, and the different methods of analysis applied to social data.

Suggested readings: 

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

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