Dipartimento di InformaticaUniversità degli Studi di Milano
Dario Malchiodi

Big scale analytics2015/16

MSc in Computer science · University of Milan · Lectures are in Italian · 6 credits · 48 hours

Archived edition: this page is kept for reference, and some materials or links may no longer be available.

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

News

  1. Office hours canceled

    Starting Monday 06/13, regular office hour are canceled until next semester; students can arrange an appointment via e-mail.

  2. Office hours canceled for two weeks

    Office hours in the next two weeks are canceled; students can contact the teacher via e-mail.

  3. Office hours of February 22nd canceled

    The office hours of February 22nd are canceled; students can arrange an appointment via e-mail.

  4. AWS credit

    Students of the big scale analytics course can get promotional codes for AWS registering at the AWS educate program and specifying «Università degli Studi di Milano» as their institution. Additional credits can be obtained through the GitHub education program.

  5. Suspension of the Big scale analytics lesson

    The Big scale analytics class of October, 7th is canceled.

  6. Office hours for the fall semester

    Office hours for the fall semester will be during Monday on 14:30 in the teacher's office, starting from 5/10.

Course schedule

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

Day Hour Place
Monday 14:30 - 16:30 aula 5
Wednesday 14:30 - 16:30 aula 6

Any change to the schedule will be announced in class and published in paragraph News of this page.

Office hours

By appointment, room 5015 of the Computer Science Department. 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)

The part on distributed file systems and MapReduce is based on the adopted textbook and on the Hadoop tutorial published by Yahoo!

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.

Lectures calendar

Lecture Date Topic Material
L01 28/09/2015 Introduction to data mining
    L02 30/09/2015 Large-scale file systems and Hadoop
    L03 05/10/2015 Map-reduce algorithms
      L04 12/10/2015 Map-reduce and Hadoop
      L05 14/10/2015 noSQL
      L06 19/10/2015 Apache Spark
      L07 21/10/2015 Finding similar items
        L08 26/10/2015 Finding similar items
          L09 28/10/2015 Mining data streams
            L10 02/11/2015 Mining data streams
              L11 04/11/2015 Link analysis
                L12 09/11/2015 Link analysis
                  L13 11/11/2015 Frequent itemsets
                    L14 16/11/2015 Frequent itemsets
                      L15 18/11/2015 Web advertising
                        L16 23/11/2015 Amazon Web Services: S3, EC2, and EMR
                          L17 25/11/2015 Clustering
                            L18 30/11/2015 Clustering
                              L19 02/12/2015 Recommendation systems
                                L20 09/12/2015 Recommendation systems
                                  L21 14/12/2015 Perceptrons
                                    L22 16/12/2015 Regression
                                      L23 21/12/2015 Dimensionality reduction
                                        L24 11/01/2016 Social networks analysis

                                          Exam modalities

                                          The exam consists in an oral test, by appointment.