This course introduces the principal techniques related to the analysis of large amounts of data.
|07/02/2019||The Big scale analytics oral tests of February will take place in the meeting room at the fifth floor of the Computer science department, on 12/2 at 14:30 (last name starting from A to M), and on 13/2 at 9:30 (last name starting from N to Z).|
|24/01/2019||Starting Monday 28/01, office hour will be handled via e-mail.|
|10/01/2019||The Big scale analytics oral tests of January will take place in the meeting room at the fifth floor of the Computer science department, on 16/1 at 15:00 (last name starting from A to H), and on 17/1 at 15:00 (last name starting from L to Z).|
|13/12/2018||The office hours of December, 18th are canceled.|
|29/11/2018||The Big scale analytics lectures of 4/12, 11/12, and 8/1 will take place in aula Tau; the lecture of 18/12 will take place in aula 207.|
|13/11/2018||Starting from 20/11, the Tuesday classes will take place in aula 207.|
|05/11/2018||The classes of November, 6th and 7th are canceled, as well as the office hours of November 6th.|
|30/10/2018||Reminder: the class of Big scale analytics of 28/11 will take place in aula V5.|
|08/10/2018||The class of Big scale analytics of 17/10 will take place in aula 305.|
|05/10/2018||Office hours for the fall semester will be on Tuesday from 16:30 in the teacher's office, starting from 09/10.|
|05/10/2018||Starting from 09/10, classes will take place on Tuesday from 14:30 till 16:30 in aula Alfa and on Wednesday from 13:30 till 15:30 in aula Magna.|
Lectures will take place at the educational sector of Città Studi, according to the following tentative schedule:
|Tuesday||16:30 - 18:30 14:30 - 16:30||aula V8 aula Alfa Aula 207|
|Wednesday||13:30 - 15:30||aula 402 aula Magna|
Any change to the schedule will be announced in class and published in paragraph News of this page.
Tuesday, at 16:30.
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 theoric part of the course is based on the following textbook: Anand Rajaraman and Jeff Ullman, Mining of Massive Datasets (marked by RU in the lectures calendar)., 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 part on distributed file systems and MapReduce is based on the adopted textbook and on the Hadoop tutorial published by Yahoo!
Some labs refer to the Data Science and Engineering with Spark edX program.
The exam consists in an oral test.