Course Unit

Catalogue

Big data models and algorithms

Unit Coordinator: Mattia D'Emidio
Programme: Erasmus Mundus, Double Degrees
ECTS Credits: 3
Semester: 2
Year: 1
Campus: University of L'Aquila
Language: English
Aims:

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.

Content:

Large-Scale Data Mining Models, Algorithms, Storage Techniques for Massive Datasets

Pre-requisites:

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

Reading list:

J. Leskovec, A. Rajaraman, J. D. Ullman. Mining of Massive Datasets. 2nd Edition.


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InterMaths Network
A network of 12 European Universities, coordinated by Department of Information Engineering, Computer Science and Mathematics (DISIM) at University of L'Aquila in Italy (UAQ)