Course Details for A.Y. 2017/2018
Name:
Big Data: Models And Algorithms / Big Data: Models And Algorithms
Basic information
Credits:
: Master Degree in Computer Science 3 CFU (d)
Degree(s):
Master Degree in Computer Science 2nd anno curriculum NEDAS Elective
Language:
English
Course Objectives
To know and be able to design, analyze and implement algorithms for massive data sets using
state-of-the-art algorithmic techniques in the area. To know and be able to apply experimental
algorithmic techniques. To know alternative processing models that are relevant to big data.
Course Content
- Data Mining
- Algorithmic techniques and models for massive data sets
- Experimental algorithmics
Learning Outcomes (Dublin Descriptors)
On successful completion of this course, the student should
- 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 systematically identify and analyze problems and make informed choices for
solving the problems based on the analysis
- Be able to design and implement algorithms for problems related to massive data sets
through state-of-the-art software frameworks
- Be able to lookup and apply relevant research literature for problems related to massive
data sets
- Be able to Express oneself in writing at a scientific level
- Know the foundations of the algorithmic experimental process design
Prerequisites and Learning Activities
Basic courses in algorithms and data structures. Mathematical and programming maturity.
Basics 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
- Sean Luke, Essentials of Metaheuristics, 2nd Edition
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: 01 marzo 2018, 11:01