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Glossary
Semester (Sem)
1First Semester
2Second Semester
AAnnual course
Language
Course completely offered in italian
Course completely offered in english
--Not available
Innovative teaching
The credits shown next to this symbol indicate the part of the course CFUs provided with Innovative teaching.
These CFUs include:
  • Subject taught jointly with companies or organizations
  • Blended Learning & Flipped Classroom
  • Massive Open Online Courses (MOOC)
  • Soft Skills
Course Details
Context
Academic Year 2016/2017
School School of Industrial and Information Engineering
Name (Master of Science degree)(ord. 270) - MI (481) Computer Science and Engineering
Track T2A - COMPUTER SCIENCE AND ENGINEERING
Programme Year 2

Course Details
ID Code 050702
Course Title BIG DATA TECHNOLOGIES
Course Type Mono-Disciplinary Course
Credits (CFU / ECTS) 5.0
Semester --
Course Description Nowadays, big data is one of the most valuable assets for companies and institutions, which rely on data analysis techniques for studying solutions and for taking informed decisions. The availability of very large data sets (up to Peta Bytes) is bringing a paradigm shift in decision making processes about everyday life, from smart cities to government and medicine. Specifically, big data requires a data-centered background, as it entails a specific technology; big data experts with a technological background have the skills that make them the most precious professional figures in research as well as in industry. This course will introduce advanced topics and outline current research challenges related to big data technologies, including data analysis, data mining and machine learning. 1) Introduction to big data (1h) - Concepts and Definitions - Data science - Course overview 2) Big data Engines (4h) - Map Reduce, Hadoop and Hadoop ecosystem - Spark 3) NoSQL data bases (2h) - Overview of NoSQL databases - Big data and NoSQL 4) Data streaming systems (4h) - Stream reasoning and Complex Event Processing systems - Apache Storm - Apache Flink - Spark streaming 5) Machine learning and Data mining (8h) - Machine learning introduction - Mining big data - Deep learning - Spark Mlib hands-on 6) Big data applications and case studies (6h) - Genomic computing - Recommender systems - Social analytics

Schedule, add and removeAlphabetical groupProfessorLanguageCourse details
From (included)To (excluded)
--AZZZZCeri Stefano
manifesti v. 3.4.3 / 3.4.3
Area Servizi ICT
22/10/2020