Educational offer - University of Padova

Information on the course unit

Degree course	Second cycle degree in DATA SCIENCE SC2377, Degree course structure A.Y. 2017/18, A.Y. 2018/19	bring this page with you
Number of ECTS credits allocated	6.0
Type of assessment	Mark
Course unit English denomination	COGNITIVE, BEHAVIORAL AND SOCIAL DATA
Website of the academic structure	http://datascience.scienze.unipd.it/2018/laurea_magistrale
Department of reference	Department of Mathematics
Mandatory attendance	No
Language of instruction	English
Branch	PADOVA
Single Course unit	The Course unit can be attended under the option Single Course unit attendance
Optional Course unit	The Course unit can be chosen as Optional Course unit

Syllabus

Prerequisites:	Notions of machine learning
Target skills and knowledge:	At the end of the course, students will be able to understand complex issues in cognitive, social and behavioural sciences, choose the appropriate methodology and instruments to extract information from cognitive, behavioural and social data and integrate data science knowledge to social, brain, mind and behavioural aspects. They will acquire: - Basic concepts of cognitive psychology, social psychology and behavioural science. - Instruments and methodologies for cognitive, behavioural and social data analysis. - Practical skills of data analysis applied to cognitive, social and behavioural problems.
Examination methods:	Written and oral exam
Assessment criteria:	It will be evaluated the knowledge of the arguments proposed during classes, the acquisition of concepts and methodologies proposed and the ability to apply them.
Course unit contents:	The aim of the course is to provide an overview of concrete data science applications in behavioural science, cognitive science, neuroscience and social science. The course gives an underground of methods to analyse and learn behavioural, cognitive and brain functional/structural data. It provide a review of studies, with several examples of recent practical applications, also according with the students interests. Limits in the state of the art and future directions will be discussed. The course contents are the following: • Basic concepts of human brain cognitive functioning (attention, memory, learning, language, etc.) and how to measure it • Basic concepts of social psychology and social behaviour (preferences, judgments, group identity, etc.) and how to measure it • What are behavioural measures and how to measure them (e.g., RT); implicit and explicit behavioural measures (e.g., the IAT) • Extracting and predicting information from behaviour (e.g. lie detection, predicting malicious behaviour from social networks activity, fake online reviews, security applications, etc.) • What are psychophysiological measures and how to measure them (e.g., HR variability, SCR, facial expressions, EEG, fRMI, etc.) • Extracting and predicting information from psychophysiological measures • Extracting and predicting information from brain activity: mind reading applications (e.g., psychopathology detection, reconstructing visual experiences from brain activity, brain computer interface devices, etc.) • Social and behavioural data for marketing application (e.g. skill assessment and prediction, psychology of taxes, predicting preferences and personality from social networks activity, sentiment analysis, etc.) • Issue related to the application of machine learning in behavioural research (e.g. the problem of reproducibility)
Planned learning activities and teaching methods:	The lecturer will introduce each topic discussing the relevant issues and the most interesting and recent experimental evidences and applications
Textbooks (and optional supplementary readings)