Big data models and algorithms

Additional Info

  • ECTS credits: 3
  • Semester: 2
  • University: University of L'Aquila
  • Prerequisites:
    Basic courses on design and analysis of algorithms and data structures. Mathematical and programming maturity. Fundamentals of data analysis.
  • 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.
  • Topics:
    Large-Scale Data Mining Models, Algorithms, Storage Techniques for Massive Datasets
  • Books:
    J. Leskovec, A. Rajaraman, J. D. Ullman. Mining of Massive Datasets. 2nd Edition.
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