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

– 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 management (6 hours) 

Instructor: TBD

– 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 sociological methodology (6 hours)

Instructor: TBD

In the second part of this course, we will discuss the fundamental aspects of studying human behaviour and decision making, both in a realistic view of how people behave and how social science methodology needs to accommodate the current understanding of people’s decision-making processes.

Suggested readings:  Goldthorpe, J. H. (2016). Sociology as a population science. Cambridge: Cambridge University Press.

Teaching mode and Language: Classes will take place in English and exclusively in 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 of the test will be provided by the instructors upon the course beginning.