This course introduces the principal techniques related to the analysis of large amounts of data.
Slides for the seminar on «The role of the data scientist»
The slides of the seminar on «The role of the data scientist» by A. Condorelli are available.
Office hours canceled
Starting Friday 04/20, regular office hour are canceled; students can arrange an appointment via e-mail.
Office hours for the spring semester
Office hours for the spring semester will be during Thursday from 14:30 until 17:30 in the teacher's office, starting from 9/3.
Office hours canceled
Starting Thursday 02/17, regular office hour are canceled until next semester; students can arrange an appointment via e-mail.
Classroom change for the Big scale analytics lecture of 10/1/2017
The Big scale analytics class of 10/1/2017 will take place in Aula Beta.
Office hours rescheduled on 22/12
Office hours of 22/12 will end at 16:00.
Office hours of November 24th canceled
The office hours of November 24nd are canceled; students can arrange an appointment via e-mail.
Schedule change for the Big scale analytics lectures
Starting from September 27th, the Big scale analytics classes will take place in Aula Delta.
Office hours for the fall semester
Office hours for the fall semester will be during Thursday from 14:00 until 17:00 in the teacher's office, starting from 29/09.
Starting today, Prof. Malchiodi has moved his office in room P102 at the first floor in the via Comelico seat of the department.
Lectures are in italian.
Lectures will take place at the Computer science department, according to the following tentative schedule:
|Monday||15:30 - 17:30||aula Delta|
|Tuesday||15:30 - 17:30||aula Delta|
Any change to the schedule will be announced in class and published in paragraph News of this page.
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 theoric part of 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 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, by appointment.