The course aims at describing the big data processing framework, both in terms of methodologies and technologies.
- will be able to use technologies for the distributed storage of datasets;
- will know the MapReduce distributed processing framework and its leading extensions;
- will know the principal algorithms used in order to deal with classical big data problems, as well as to implement them using a distributed processing framework;
- will be able to choose appropriate methods for solving big data problems.
Movement of video lectures
In a few days, the recordings of the lectures will be moved to the University's OneDrive space. Students are therefore invited to check that their academic account linked to Office365 is activated.
Remote office hours organization
Starting today, office hours will take place remotely. On each Thursday, students can connect from 17:00 to the meeting «ricevimento-malchiodi» organized on meet.jit.si , writing their name and surname in the chat, and waiting to be called. The channel is open to all participants, so the need for private office hours must be reported, always in the chat, when connecting.
Organization of distance learning
Until further notice, the lectures of «Statistics and data analysis» and «Algorithms for massive datasets» will take place via distance learning. On the days when a course is scheduled, a video recording of the lesson will be made available on the corresponding Web page. Students can send questions to the teacher via email on any clarifications: the following day a documentcontaining the answer to the questions of general interest will be published.
Recording of the lecture «Technical preliminaries» for the Algoritms for massive datasets course
The recording of the lecture «Technical preliminaries» for the Algorithms for massive datasets course is available.
Restricted access to lecture recordings
Access to confidential content has changed. The «Course material» section in the pages of the involved courses describes the new method.
Recording of the lecture «Mathematical preliminaries» for the Algoritms for massive datasets course
The recording of the lecture «Mathematical preliminaries» for the Algorithms for massive datasets course is available.
Cancellation of teaching activities
All teaching activities are canceled until 29/2.
Office hours on February, 20th
The office hours of February, 20th are canceled.
Beginning of the Algorithms for massive datasets course
The lectures of Algorithms for massive datasets will start on Wednesday February, 26th at 14:30 in classroom alfa of the Computer science department. Starting from the following week, lectures will take place as shown in the course timetable.
Lectures are in English.
Lectures take place at the educational sector of Città Studi, according to the following tentative schedule:
|Monday||15:30 - 17:30 (*)||G9|
|Wednesday||14:30 - 18:30||G12|
(*) Monday lectures, aimed at students of the Master in Computer
Science, take place only in the weeks shown in the calendar below.
Any change to the schedule will be announced in class and published in paragraph News of this page.
Thursday, at 17:00.
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.
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.
The recording of some lectures, marked with (R) in the course schedule, is available. Authentication is done using the Office365 academic account.
It is also suggested to read the following material.
- To practice with Spark: H. Karau, A. Konwinski, P. Wendell, M. Zaharia, Learning Spark. Lightning-Fast Big Data Analysis, O'Reilly, 2015 (ISBN:978-1-449-35862-4).
- For a deeper study of Spark: S. Ryza, U. Laserson, S. Owen, J. Wills, Advanced Analytics with Spark. Patterns for Learning from Data at Scale, O'Reilly, 2015 (ISBN:978-1-491-91276-8).
- About distributed file systems and the MapReduce paradigm: Yahoo! Hadoop Tutorial (besides Chapter 2 in RU).
- For a deeper study of the practical parts: Data Science and Engineering with Spark program of edX.
The course requires knowledge of the main topics of bachelor-level computer programming, calculus, probability, and statistics.
The exam consists of a project and an oral test, both related to the topics covered in the course. The project, possibly joint with the Statistical methods for machine learning course, 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.