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.
Feedback form for the Big data analytics and technologies PhD course
Students enrolled in the Big data analytics and technologies PhD course are invited to fill in a feedback form.
Lectures are in English.
Lectures will take place at the Computer science department in the meeting room at 1st floor.
By appointment (via e-mail).
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
studenti.unimi.it) signing with name and student ID number and recalling that the response time may vary depending on the teacher commitments.
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 exam consists in the discussion of a project to be agreed with teachers.