Dipartimento di InformaticaUniversità degli Studi di Milano
Dario Malchiodi

Algorithms for massive data (DSE)2020/21

MSc in Data Science and Economics · University of Milan · Lectures are in English · 3 credits · 20 hours

Archived edition: this page is kept for reference, and some materials or links may no longer be available. Current edition (2026-27) →
This page refers to the module Algorithms for massive data within the course «Algorithms for massive data, cloud and distributed computing» of the Master in Data science for economics. The course of Algorihms for massive datasets for the master in Computer science is described in a specific page.

The objective of the course is to introduce the fundamental concepts at the basis of massive data management and analysis, including the main processing techniques dealing with data at massive scale and their implementation on distributed computational frameworks.

Expected results

At the end of the module, students shall know the main approaches enabling them to analyze massive amounts of data, as well as the ability to design and execute computations on big data, deployed on modern distributed computing systems.

News

  1. Office hours change

    Starting Monday 31/05, office hour will be handled via e-mail.

  2. Availablity of recorded lectures of «Algorithms for massive datasets» for the Master in Data Science for Economics

    The videorecorded lectures of the course «Algorithms for massive datasets» for the Master in Data Science for Economics will be available until the end of March.

  3. Projects for the «Algorithms for massive datasets» course (Master DSE)

    The description of the projects for the course «Algorithms for massive datasets» for the Master in «Data Science for Economics» are available.

Earlier news (2)

  1. Tutoring «Algorithms for massive datasets» (DSE)

    Starting from March 1st, students can attend five tutoring sessions held on the same days previously used for lectures (Monday and Tuesday, 15:30-17:00). Students will receive an e-email message containing the corresponding zoom link.

  2. Semester for the Algorithms for massive datasets (DSE) course

    The class of Algorithms for massive datasets (DSE) will be delivered in the spring semester.

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

Lectures are based:

  • on the textbook Mining of Massive Datasets, written by A. Rajaraman and J. Ullman (marked by RU in the calendar of lectures), available as a free download in the authors' Web site and published in hardcopy by Cambridge University Press (ISBN:9781107015357);
  • on the notes and sample code published in the calendar of lectures.

Syllabus

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

Prereqs

The course requires knowledge of the main topics of bachelor-level computer programming, calculus, probability, and statistics.

Lectures calendar

Lecture Date Topic Material
L01 25/01/2021 Mathematical preliminaries.
L02 26/01/2021 Technical preliminaries.
L03 01/02/2021 Link analysis.
  • RU 5.1-5.2.
L04 02/02/2021 Link analysis.
    L05 08/02/2021 HDFS and MapReduce. MapReduce algorithms.
    L06 09/02/2021 Spark.
    L07 15/02/2021 Finding similar items.
    L08 16/02/2021 Frequent itemsets.
    • RU 6.1-6.2.
    L09 22/02/2021 Frequent itemsets.
    • RU 6.3-6.4.
    L10 23/02/2021 Recommendation systems.
    L11 01/03/2021 Tutoring: Spark I.
    L12 02/03/2021 Tutoring: Spark II.
      L13 08/03/2021 Tutoring: Spark III.
        L14 09/03/2021 Tutoring: Recommendation systems.
        L15 15/03/2021 Tutoring: Market-basket analysis.

        Exam modalities

        The exam consists of a project and an oral test, both related to the topics covered in the course. The project requires to process one or more datasets through the critical application of the techniques described during the classes, and is described in a written report. The evaluation of the project, expressed with a pass/fail mark, considers the level of mastery of the topics and the clarity of the report. The oral test, which is accessed after a positive evaluation of the project, is based on the discussion of some topics covered in the course and on in-depth questions about the presented project. The evaluation of the oral test, expressed on a scale between 0 and 30, takes into account the level of mastery of the topics, clarity, and language skills.

        How to sign up

        Written exam (prof. Ardagna, prof. Foresti)

        Subscribe through UniMia to the exam call if you want to sustain one or both parts of the exam. You will receive an email a few days before the exam, asking you which part of the written part of the exam you will attend. If you already passed one of the two parts of the exam, please register through Ariel to be admitted to the written exam for the part you still need to sustain. Students who do not subscribe to the exam will not be admitted to sustain it. We need the exact number of people who will sustain the written exam, to organize (virtual) rooms, especially in this emergency situation.

        Project and oral exam (prof. Malchiodi)

        Send en email to prof. Malchiodi, containing the link to your project. You will be contacted in order to organize the oral exam after the project has been checked. You do not need to sign up through UniMia, but you are asked to send your work in the proximity of formal exam calls.

        Final mark

        After successful completion of all parts, the final mark will be recorded in association with your UniMia subscription for the written part of the exam.

        Mark expiration

        The mark of each part of the exam expires after 1 year. If, after one year from when you passed one of the parts of the exam, you did not register the final mark, you will need to repeat all the parts of the exam (also the ones you already passed).

        Exam sessions

        Session Date
        June N/A
        July N/A
        September N/A
        September N/A
        January N/A
        February N/A