Dipartimento di InformaticaUniversità degli Studi di Milano
Dario Malchiodi

Big scale analytics2017/18

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 wednesday 03/14, regular office hour are canceled; students can arrange an appointment via e-mail.

  2. Big scale analytics test of Jaunary 2018

    The Big scale analytics test of January will take place on 15/1 starting from 9:30 in the professor's office.

  3. Schedule change for the Big scale analytics course

    The December, 19th class of the Big scale analytics course is canceled. It will be catched up on Jan 8th, 2018.

Earlier news (5)

  1. Schedule change for the Big scale analytics course

    The December, 11th class of the Big scale analytics course will take place on Dec 12th.

  2. Schedule change for the Big scale analytics course

    The forthcoming classes of the Big scale analytics course will take place on Dec 4th, Dec 11th, Dec 18th and Dec 19th.

  3. Schedule change for the Big scale analytics course

    The 27/11 class of the Big scale analytics course will take place in aula 6 at 14:30, and the class of 28/11 will take place at 13:30 in aula alfa.

  4. Docker container for the Big scale analytics course

    Students of the Big scale analytics course can download a ZIP archive containing all files needed to build and run the docker container used during lectures.

  5. Office hours of Jan 9th canceled

    The office hour of Jan 9th are canceled; students can arrange an appointment via e-mail.

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 Delta
Tuesday 14:30 - 16:30 aula Omega

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 (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.

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 25/09/2017 HDFS
L02 26/09/2017 Map-reduce in Hadoop
  • RU 2.2
L03 02/10/2017 Examples of map-reduce algorithms
L04 03/10/2017 Analisi di algoritmi map-reduce
  • RU 2.5
L05 09/10/2017 Link analysis
  • RU 5.1
L06 10/10/2017 Apache Spark
L07 16/10/2017 Finding similar items
  • RU 3.1-3.3
L08 17/10/2017 Finding similar items
  • RU 3.4-3.5
L09 23/10/2017 Finding similar items
L10 24/10/2017 Mining data streams
L11 30/10/2017 Frequent itemsets
  • RU 6.1-6.2
L12 31/10/2017 Frequent itemsets
  • RU 6.3-6.4
L13 06/11/2017 Clustering
  • RU 7.1-7.2
L14 07/11/2017 Clustering
L15 13/11/2017 noSQL data base: mongodb
L16 14/11/2017 Recommendation systems
  • RU 9.1-9.2
L17 20/11/2017 Recommendation systems
  • RU 9.3-9.4
L18 21/11/2017 Web advertising
  • RU 8.1-8.4
L19 27/11/2017 Regression
L20 28/11/2017 Logistic regression
    L21 04/12/2017 Perceptrons
      L22 12/12/2017 Deep learning and TensorFlow
      L23 18/12/2017 External seminar
        L24 08/01/2018 External seminar

          Exam modalities

          The exam consists in an oral test, by appointment.