Course Details for A.Y. 2019/2020

The information on this page refer to the academic year 2019/2020. Therefore, several links are not available and data may be incomplete. To read the current information on the course, if it is still available, go to the university course catalogue . To read information relating other academic years, use the list at the bottom of this page.

A. Year:

2019/2020

Name:

Big Data: Models And Algorithms / Big Data: Models And Algorithms

Type:

Course

Basic information

Code:

DT0317

Sector:

INF/01

Credits:

: Master Degree in Computer Science 3 CFU (d)

Term:

2^nd semester

Degree(s):

Master Degree in Computer Science 2^nd anno curriculum NEDAS Elective

Language:

English

Teacher(s):

Mattia D'Emidio

Course Objectives

Upon completion of this course the student will have reliably demonstrated the ability to design, analyze and implement algorithms for massive data sets using state-of-the-art algorithmic techniques in the area. Furthermore, the student will be able to understand: i) storage strategies that are suited for large-scale datasets (e.g. distributed, unstructured); ii) alternative processing models that are relevant to big data; iii) fundamentals of large-scale data mining. Finally, the student will acquire basic knowledge of experimental algorithmic techniques and data analysis.

Course Content

Data Mining
Algorithmic techniques, storage frameworks, processing models for massive data sets
Experimental algorithmics

Learning Outcomes (Dublin Descriptors)

On successful completion of this course, the student should

Understand the challenges of large scale data mining
Be able to describe in a comprehensible manner, analyze, evaluate, and compare the performance of algorithms, with a focus on models of computation relevant to massive data sets
Be able to design and implement algorithms for computational problems at large scale through state-of-the-art techniques
Be able to lookup and apply relevant research literature for problems related to storage and processing of massive data sets
Be able to express oneself in writing at scientific level
Know the foundations of the algorithmic experimental process design

Prerequisites and Learning Activities

Basic courses on design and analysis of algorithms and data structures. Mathematical and programming maturity. Fundamentals of data analysis.

Assessment Methods and Criteria

Written Exam + Oral discussion (and/or Homework/Project)

Textbooks

Catherine McGeoch, A Guide to Experimental Algorithmics
J. Leskovec, A. Rajaraman, J. D. Ullman, Mining of Massive Datasets. 2nd Edition

Internet Resources

Homepage:

http://www.mattiademidio.com/graduate.php

Teaching material:

Teaching material available on the external website http://www.mattiademidio.com/graduate.php.

Course page updates

This course page is available (with possible updates) also for the following academic years:

To read the current information on this course, if it is still available, go to the university course catalogue .

Course information last updated on: 28 novembre 2019, 10:10