Big data analytics and technologies 2015-16

PhD in Computer Science

Academic year

The course aims at illustrating some data analysis techniques characterized by scalability properties, as well as at implementing these techniques through exploitation of technologies expressly designed to be used in distributed environments. The course is co-held with prof. Santini.


Date Info
18/03/2016 Students enrolled in the Big data analytics and technologies PhD course are invited to fill in a feedback form.

Course schedule

Lectures will take place at the Computer science department in the meeting room at 1st floor.

Office hours

By appointment. It is possible contact the teacher by e-mail, taking care to read in advance the guide prepared by Prof. Sebastiano Vigna and clearly specifying in the message the course name and the academic year. In particular, students are encouraged to always use their academic address (i.e. based on the domain signing with name and student ID number and recalling that the response time may vary depending on the teacher commitments.

Course material

The course is based on the following textbook: Anand Rajaraman and Jeff Ullman, Mining of Massive Datasets, available both as a freely downloadable PDF and published in hardcopy by Cambridge University Press (ISBN:9781107015357). The suggested readings for the practical part are Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia, Learning Spark. Lightning-Fast Big Data Analysis, O'Reilly, 2015 (ISBN:978-1-449-35862-4) and Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills, Advanced Analytics with Spark. Patterns for Learning from Data at Scale, O'Reilly, 2015 (ISBN:978-1-491-91276-8)


The course explains the topics listed in the lecture calendar. The described software is published in two git repositories, respectively for theory and for lab parts.

Lectures calendar


The lectures calendar is based on Google calendar and is accessible also through feed, iCal and Web.

Exam modalities

The exam consists in the discussion of a project to be agreed with teachers.