Big data and digital methods

A.Y. 2021/2022
Overall hours
INF/01 SPS/08
Learning objectives
The course aims to provide students with the theoretical and methodological tools needed to autonomously conduct qualitative and quantitative empirical research based on digital data. Big Data and digital methods will, on the one hand, be problematized through theoretical reflections on the datafication of contemporary societies; on the other hand, they will be introduced as central methodological approaches in today's social and marketing research. During the course each aspect of the digital inquiry will be covered step by step: research design, data collection and cleaning, analysis, interpretation and visualization of the results. Students will be guided through a hands-on approach to the use of different techniques (digital ethnography, network analysis, qualitative and quantitative text analysis) and analysis tools, also thanks to intensive exercise sessions. This training course will end with the realization and presentation by students of digital surveys on research topics introduced in class.
Expected learning outcomes
Students are expected to become competent in crafting original research on digital platforms. Three main skill sets sould be mastered by students: 1) theoretical competence, 2) methodological competence, 3) technical competence. For the first skillset students should be familiar with current theoretical expertise on digital platforms, including digital methods based approaches as well as politcal economy ones. For the second skill set students are expected to become proficient with an array of different research methods, such as: quantitative analysis (eg sentiment analysis, influencer detection), ethnographic analysis, network analysis. It is expected that students will be more familiar with one specific aspect but working knowledge of all will be required. For the third skill set students are expected to became proficient in the technical tools needed to finalize research for digital platforms: including data mining and data analysis for python, as well as third party data analysis tools.
Course syllabus and organization

Single session

Lesson period
First trimester
Class time will follow the standard calendar and will be held on the Microsoft Teams platform. The exercises will always be either on Web sites, e.g., or on student's own computer.

"More specific information on the delivery modes of training activities for academic year 2021/22 will be provided over the coming months, based on the evolution of the public health situation."
Course syllabus
Part A: introduzione a Python per l'analisi dei dati. Il ciclo REPL (read-eval-print); Jupyther; formati dei dati con CSV e JSON; i moduli Pandas, Numpy, Matplotlib e Networkx.
Part B: quantitative analysis in social sciences; mini-projects and their interpretation; the quali-quanti method.
Prerequisites for admission
General ability with data manipulation, as, e.g. in intermediate-advanced use of Spreadsheets.
Previous coding experience is not necessary.
Teaching methods
Recited lectures, in classroom or online, will introduce you to Python and to the basic data analys techniques.
Coding and data analys labs will focus on playground datasets, such as Twitter corpora.
Teaching Resources
All materials are web-accessible and will be linked/shared in class.
For an introduction to Python please see J. VanderPlas, "A Whirlwind Tour of Python:"
Assessment methods and Criteria
A Python exam project, to be preliminary agreed with the instructors, e.g. a Jupyther notebook for the analysis of data sources such as input files or web resources.
INF/01 - INFORMATICS - University credits: 6
Lessons: 80 hours
by email appointment
MS Teams platform