2018-19 2017-18 2016-17 2015-16 2014-15 2013-14 2012-13

This course introduces the principal techniques related to the analysis of large amounts of data.

## News

Date Info
23/06/2017 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.
20/04/2017 Office hours canceled
Starting Friday 04/20, regular office hour are canceled; students can arrange an appointment via e-mail.
06/03/2017 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.
01/02/2017 Office hours canceled
Starting Thursday 02/17, regular office hour are canceled until next semester; students can arrange an appointment via e-mail.
09/01/2017 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.
20/12/2016 Office hours rescheduled on 22/12
Office hours of 22/12 will end at 16:00.
21/11/2016 Office hours of November 24th canceled
The office hours of November 24nd are canceled; students can arrange an appointment via e-mail.
26/09/2016 Schedule change for the Big scale analytics lectures
Starting from September 27th, the Big scale analytics classes will take place in Aula Delta.
21/09/2016 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.
02/09/2016 Office relocation
Starting today, Prof. Malchiodi has moved his office in room P102 at the first floor in the via Comelico seat of the department.

## Language

Lectures are in italian.

## Course schedule

Lectures will take place at the Computer science department, according to the following tentative schedule:

Day Hour Place
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.

## Office hours

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.

### Course material

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)

Some labs refer to the Data Science and Engineering with Spark edX program.

## Syllabus

The course explains the topics listed in the lecture calendar, covering the textbook contents as well as the contents of the remaining documents listed in Course material.