Studies on Human Behaviour – SHB 2020

COURSE DESCRIPTION

This course is taught in English. The aim of this course is to study the behaviour of people. The course is data intensive and hands on. It covers all the phases from experiment design, data collection, data preparation and data analysis. After a brief theoretical introduction, the course will consist of running real world experiments, on large amounts of data. The exam will consist of presenting the results of the experiment in a public presentation. This inter-disciplinary course bridges competences in sociology, ethics and computer science.

INSTRUCTIONS

The Fall 2020 Edition of the course Studies on Human Behaviour is delivered remotely, due to the current circumstances. The format is mixed, with some lessons delivered synchronously and some asynchronously. You will find Sync and Async tags in each lesson in the calendar.

A synchronous lesson is taught in real-time, with the professor and the class, via Zoom. All synchronous lectures will be recorded and made available on Moodle and on this website.
An asynchronous lesson is instead recordered by the professor before end and made available to the students on Moodle and on this website. You are not required to watch these lessons during the lessons hours, however we suggest you to do so because it helps keeping up with the workload.

The idea behind this course is that the students do most of the work during the course duration (up to December) because we believe this should yield considerably better results.

Studying human behaviour involves the analysis of real data about people. Data collection is then a crucial part of the whole process. For this reason, the students of the course will be taught and asked to collect data from their own smartphones via an application developed by the Knowdive group, ideally over a period of 2 weeks. Such data is the one that the students will analyse to generate insights for the final examination. In the calendar you can find the dates of the data collection and when the data will be made avilable (dates can still change). More details will be provided by the professors during the first lessons.

SUGGESTED READINGS

People interested in knowing more details about what we do in this course can refer to these books:

Mainly about Data Science:

• Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006.
• Grus, Joel. Data science from scratch: first principles with python. O’Reilly Media, 2019.
• VanderPlas, Jake. Python data science handbook: Essential tools for working with data. O’Reilly Media, 2016.

Mainly about systems and frameworks for Big Data:

• Carpenter, Jeff, and Eben Hewitt. Cassandra: the definitive guide: distributed data at web scale. O’Reilly Media, 2021.
• Chambers, Bill, and Matei Zaharia. Spark: The definitive guide: Big data processing made simple. O’Reilly Media, 2018.