Course Details for A.Y. 2019/2020
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
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
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